Dissertations / Theses on the topic 'Planning under uncertainity'
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Wilson, Michael Thomas Ph D. Massachusetts Institute of Technology. "Mapping under uncertainity : spatial politics, urban development, and the future of coastal flood risk." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120237.
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Thesis: Ph. D. in Urban and Regional Planning, Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 283-311).
Flooding is the most common and single largest source of disaster-caused property damage in the United States. The past year, 2017, was the costliest for weather and climate disasters in US history. To mitigate these losses, the Federal Emergency Management Agency and National Flood Insurance Program produce Flood Insurance Rate Maps (FIRMs) that often provide the most comprehensive and authoritative flood hazard information for a community. Despite reform efforts for greater map accuracy, spatial politics may render the computationally efficient 100- year floodplain delineation of questionable effectiveness, equity, and legitimacy for long-term land use planning. Given changing coastal flooding and sea level rise, how can risk mapping inform and improve future urban development? The dissertation: (1) positions flood mapping in the larger context of urban risk computation; (2) chronicles and statistically analyzes the nationwide map adoption process; (3) uses spatial analysis, document review, semi-structured interviews, and grounded theory to identify how these updates are proxies for nonstationary flood risk in Plymouth County, MA and New York City, NY; (4) compiles a novel survey of recent large-scale development decisionmaking in Boston, and (5) pilots a probabilistic indicator that models project-level flood risk information. I observe that the differences in location, wealth, and race between counties are associated with varying FIRM adoption process durations as well as whether a county may appeal and receive revised maps. I argue that coastal communities with sociopolitical clout can bend the process of computational risk assessment, through either contestation or collaboration over risk classification. I find the planning information shock of updated maps, however, is a largely insufficient signal to change developer behavior. Therefore, I pioneer the Future Flood Resilience Indicator (FFRI) as a decision support tool for developers to understand the long-term flood risk of their proposed development projects and planners to ascertain the impact of their policies. In conclusion, the dissertation provides policy makers with: (1) new data on how map adoption is not a purely scientific and technical process, (2) further evidence that the current 100- year flood standard is inadequate, and (3) resilience-building tools for land use planning.
by Michael Thomas Wilson.
Ph. D. in Urban and Regional Planning
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Willquist, André. "Uncertainty Discretization for Motion Planning Under Uncertainty." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170496.
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In this thesis, the problem of motion planning under uncertainty is explored. Motion planning under uncertainty is important since even with noise during the execution of the plan, it is desirable to keep the collision risk low. However, for the motion planning to be useful it needs to be possible to perform it in a reasonable time. The introduction of state uncertainty leads to a substantial increase in search time due to the additional dimensions it adds to the search space. In order to alleviate this problem, different approaches to pruning of the search space are explored. The initial approach is to prune states based on having strictly worse uncertainty and path cost than other found states. Having performed this initial pruning, an alternate approach to comparing uncertainties is examined in order to explore if it is possible to achieve a lower search time. The approach taken in order to lower the search time further is to discretize the covariance of a state by using a number of buckets. However, this discretization results in giving up the completeness and optimality of the algorithm. Having implemented these different ways of pruning, their performance is tested on a number of different scenarios. This is done by evaluating the planner using the pruning in several different scenarios including uncertainty and one without uncertainty. It is found that all of the pruning approaches reduce the overall search time compared to when no additional pruning based on the uncertainty is done. Additionally, it is indicated that the bucket-based approach reduce the search time to a greater extent than the strict pruning approach. Furthermore, the extensions made results in no increase in cost or a very small increase in cost for the explored scenarios. Based on these results, it is likely that the bucket pruning approach has some potential. However more studies, particularly with additional scenarios, needs to be made before any definitive conclusions can be made.
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Kuter, Ugur. "Planning under uncertainty moving forward /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3802.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Murphy, Elizabeth M. "Planning and exploring under uncertainty." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:bb3d85f6-117b-4f5e-92ab-b6acc87aef79.
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Scalable autonomy requires a robot to be able to recognize and contend with the uncertainty in its knowledge of the world stemming from its noisy sensors and actu- ators. The regions it chooses to explore, and the paths it takes to get there, must take this uncertainty into account. In this thesis we outline probabilistic approaches to represent that world; to construct plans over it; and to determine which part of it to explore next. We present a new technique to create probabilistic cost maps from overhead im- agery, taking into account the uncertainty in terrain classification and allowing for spatial variation in terrain cost. A probabilistic cost function combines the output of a multi-class classifier and a spatial probabilistic regressor to produce a probability density function over terrain for each grid cell in the map. The resultant cost map facilitates the discovery of not only the shortest path between points on the map, but also a distribution of likely paths between the points. These cost maps are used in a path planning technique which allows the user to trade-off the risk of returning a suboptimal path for substantial increases in search speed. We precompute a probability distribution which precisely approximates the true distance between any grid cell in the map and goal cell. This distribution under- pins a number of A* search heuristics we present, which can characterize and bound the risk we are prepared to take in gaining search efficiency while sacrificing optimal path length. Empirically, we report efficiency increases in excess of 70% over standard heuristic search methods. Finally, we present a global approach to the problem of robotic exploration, uti- lizing a hybrid of a topological data structure and an underlying metric mapping process. A ‘Gap Navigation Tree’ is used to motivate global target selection and occluded regions of the environment (‘gaps’) are tracked probabilistically using the metric map. In pursuing these gaps we are provided with goals to feed to the path planning process en route to a complete exploration of the environment. The combination of these three techniques represents a framework to facilitate robust exploration in a-priori unknown environments.
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Sakamoto, Philemon. "UAV mission planning under uncertainty." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36230.
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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.Includes bibliographical references (p. 205-209).
With the continued development of high endurance Unmanned Aerial Vehicles (UAV) and Unmanned Combat Aerial Vehicles (UCAV) that are capable of performing autonomous fiunctions across the spectrum of military operations, one can envision a future military in which Air Component Commanders control forces comprised exclusively of unmanned vehicles. In order to properly manage and fully realize the capabilities of this UAV force, a control system must be in place that directs UAVs to targets and coordinates missions in a manner that provides an efficient allocation of resources. Additionally, a mission planner should account for the uncertainty inherent in the operations. Uncertainty, or stochasticity, manifests itself in most operations known to man. In the battlefield, such unknowns are especially real; the phenomenon is known as the fog of war. A good planner should develop plans that provide an efficient allocation of resources and take advantage of the system's true potential, while still providing ample "robustness" ill plans so that they are more likely executable and for a longer period of time.
(cont.) In this research, we develop a UAV Mission Planner that couples the scheduling of tasks with the assignment of these tasks to UAVs, while maintaining the characteristics of longevity and efficiency in its plans. The planner is formulated as a Mixed Integer Program (MIP) that incorporates the Robust Optimization technique proposed by Bertsimas and Sim [12].
by Philemon Sakamoto.
S.M.
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Krüger, Niclas. "Infrastructure investment planning under uncertainty /." Örebro : Örebro University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-6618.
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Gatica, Diaz Escobar Gabriel. "Capacity planning under clinical trials uncertainty." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/8400.
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Kubali, Volkan C. (Volkan Cevat). "Task and contingency planning under uncertainty." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35032.
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Culver, David M. (David Martin). "Robust reconnaissance asset planning under uncertainty." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84714.
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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 105-107).
This thesis considers the tactical reconnaissance asset allocation problem in military operations. Specifically this thesis presents methods to optimize, under uncertain conditions, tactical reconnaissance asset allocation in order to maximize, within acceptable levels of asset risk exposure, the expected total information collection value. We propose a deterministic integer optimization formulation and two robust mixed-integer optimization extensions to address this problem. Robustness is applied to our model using both polyhedral and ellipsoidal uncertainty sets resulting in tractable mixed integer linear and second order cone problems. We show through experimentation that robust optimization leads to overall improvements in solution quality compared to non-robust and typical human generated plans. Additionally we show that by using our robust models, military planners can ensure better solution feasibility compared to non-robust planning methods even if they seriously misjudge their knowledge of the enemy and the battlefield. We also compare the trade-offs of using polyhedral and ellipsoidal uncertainty sets. In our tests our model using ellipsoidal uncertainty sets provided better quality solutions at a cost of longer average solution times to that of the polyhedral uncertainty set model. Lastly we outline a special case of our models that allows us to improve solution time at the cost of some solution quality.
by David M. Culver.
S.M.
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Richter, Felix Milo [Verfasser]. "Hierarchical planning under uncertainty / Felix Richter." Ulm : Universität Ulm, 2018. http://d-nb.info/1150301880/34.
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Matrosov, E. S. "Planning water resource systems under uncertainty." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1468958/.
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Stationarity assumptions of linked human-water systems are frequently invalid given the difficult-to-predict changes affecting such systems. Population growth and development is fuelling rising water demand whilst in some parts of the world water supply is likely to decrease as a result of a changing climate. A combination of infrastructure expansion and demand management will be necessary to maintain the water supply/demand balance. The inherent uncertainty of future conditions is problematic when choosing a strategy to upgrade system capacity. Additionally, changing stakeholder priorities mean multi-criteria planning methods are increasingly relevant. Various modelling-assisted approaches are available to help the water supply planning process. This thesis investigates three state-of-the-art multi-criteria water source systems planning approaches. The first two approaches seek robust rather than optimal solutions; they both use scenario simulation to test the system plans under different plausible versions of the future. Under Robust Decision Making (RDM) alternative strategies are simulated under a wide range of plausible future scenarios and regret analysis is used to select an initial preferred strategy. Statistical cluster analysis identifies causes of system failure enabling further plan improvement. Info-Gap Decision Theory tests the proposed strategies under plausible conditions that progressively deviate from the expected future scenario. Decision makers then use robustness plots to determine how much uncertain parameters can deviate from their expected value before the strategies fail. The third approach links a water resource management simulator and a many-objective evolutionary search algorithm to reveal key trade-offs between performance objectives. The analysis shows that many-objective evolutionary optimisation coupled with state-of-the art visual analytics helps planners assess the best (approximately Pareto-optimal) plans and their inherent trade-offs. The alternative plans are evaluated using performance measures that minimise costs and energy use whilst maximising engineering and environmental performance criteria subject to basic supply reliability constraints set by regulators. The analyses show that RDM and Info-Gap are computationally burdensome but are able to consider a small number of candidate solutions in detail uncovering the solutions’ vulnerabilities in the face of uncertainty in future conditions while the multi-objective optimisation approach is able to consider many more possible portfolios and allow decision makers to visualize the trade-offs between performance metrics.
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Richter, Felix [Verfasser]. "Hierarchical planning under uncertainty / Felix Richter." Ulm : Universität Ulm, 2018. http://d-nb.info/1150301880/34.
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Hoang, Lan Ngoc. "Adaptation planning under climate change uncertainty." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5567/.
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This project explores the uncertainty factors in drought planning for a water resource zone in Sussex. Nine planning options from the 2009 Sussex Water Resource Management Plan were assessed using four climate products: the 2009 UK Climate Projections Change Factors, the Spatial Coherent Projections, the 11 runs of the HadRM3 regional climate model and their subsequent downscaling by the Future Flows Project. The varying drought statistics from these four climate products reflect post-processing uncertainty - the uncertainty stemming from the process of converting original climate model outputs into products of different formats, variables and temporal/spatial scales. Overall, the study has integrated a cascade analysis of climate uncertainty, climate post-processing uncertainty, hydrological uncertainty, water resource model uncertainty and demand uncertainty on water resource planning. The study combines Robust Optimisation, Decision-Scaling and Robust Decision Making into Robust Decision Analysis, a decision making framework for dynamic adaptation pathways in response to different levels of uncertainty and risk averseness. Post-processing uncertainty is the dominate uncertainty until 2030s; 2050s is then dominated by demand and socio-economic uncertainty. The most severe droughts within the Spatial Coherent Projections and the 2009 UK Climate Projection products are variations of the 1975-1976 and the 1988-1989 droughts, two of the worst historic droughts currently used as the design events for drought planning in Sussex. The system appears to be robust to variations of these past droughts. Yet, under different sequences of droughts from the HadRM3 and Future Flows products, the system demonstrated frequent supply failures in the 2050s, unless water demand is maintained at the 2007 level or lower. While operational costs in the 2030s are generally within the region of 4 to 5 million GBP per year, those in the 2050s Market Forces jumped to the region of 5 to 15 million GBP per year and with supply deficit from 0 to 1100 Ml/year. When demand grows by 35% from the 2007 baseline level, universal metering becomes a key option. Despite climate post-processing uncertainty, the main hotspots of water deficits remains similar across the climate products and are driven by network bottlenecks and the continually high dependence of the system on water sources a round the Hardham area. The study also indicates that inter-regional transfers might not be as reliable as assumed.
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Wu, Yue. "Robust global supply chain planning under uncertainty." Thesis, London School of Economics and Political Science (University of London), 2009. http://etheses.lse.ac.uk/2062/.
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The New World Economy presents business organizations with some special challenges that they have never met before, when they manage their activities in the global supply chain network. Business managers find that traditional managerial approaches, techniques and principles are no longer effective in dealing with these challenges. This dissertation is a study of how to solve new problems emerging in the global supply chain network. Three main issues identified in the global supply chain network are: production loading problems for global manufacturing, logistics problems for global road transport and container loading problems for global air transport. These problems involve a higher level of uncertainty and risk. Three types of dual-response strategies have been developed to hedge the uncertainty and short lead time in the above three problems. These strategies are: a dual-response production loading strategy for global manufacturing, a dual-response logistics strategy for global road transport and a dual-response container loading strategy for global air transport. In order to implement these strategies, the two-stage stochastic recourse programming models have been formulated. The computational results show that the two-stage stochastic recourse models have an advantage in comparison to the corresponding deterministic models for the three issues. However, the two-stage stochastic recourse models lack the ability of handling risk, which is particularly important in today's highly-competitive environment. We thus develop a robust optimization framework for dealing with uncertainty and risk. The robust optimization framework consists of a robust optimization model with solution robustness, a robust optimisation model with model robustness and a robust optimization model with trade-off between solution robustness and model robustness. Each type of the robust optimization models represents a different measure of performance in terms of risk and cost. A series of experiments demonstrate that the robust optimization models can create a global supply chain planning system with more flexibility, reliability, agility, responsiveness and lower risk.
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Wellman, Michael P. "Formulation of tradeoffs in planning under uncertainty." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/45692.
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Klein, Robert H. (Robert Henry). "Planning under uncertainty with Bayesian nonparametric models." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90672.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-119).
Autonomous agents are increasingly being called upon to perform challenging tasks in complex settings with little information about underlying environment dynamics. To successfully complete such tasks the agent must learn from its interactions with the environment. Many existing techniques make assumptions about problem structure to remain tractable, such as limiting the class of possible models or specifying a fixed model expressive power. Complicating matters, there are many scenarios where the environment exhibits multiple underlying sets of dynamics; in these cases, most existing approaches assume the number of underlying models is known a priori, or ignore the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. The first is Simultaneous Clustering on Representation Expansion (SCORE) for learning Markov Decision Processes (MDPs) that exhibit an underlying multiple-model structure. SCORE addresses the co-dependence between observation clustering and model expansion. The second contribution provides a realtime, non-myopic, risk-aware planning solution for use in camera surveillance scenarios where the number of underlying target behaviors and their parameterization are unknown. A BNP model is used to capture target behaviors, and a solution that reduces uncertainty only as needed to perform a mission is presented for allocating cameras. The final contribution is a reinforcement learning (RL) framework RLPy, a software package to promote collaboration and speed innovation in the RL community. RLPy provides a library of learning agents, function approximators, and problem domains for performing RL experiments. RLPy also provides a suite of tools that help automate tasks throughout the experiment pipeline, from initial prototyping through hyperparameter optimization, parallelization of large-scale experiments, and final publication-ready plotting.
by Robert H. Klein.
S.M.
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Huang, Yanfeng Anna. "Supply chain planning decisions under demand uncertainty." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45229.
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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008."June 2008."
Includes bibliographical references (leaves 68-71).
Sales and operational planning that incorporates unconstrained demand forecasts has been expected to improve long term corporate profitability. Companies are considering such unconstrained demand forecasts in their decisions on investment in supply chain resources. However, demand forecasts are often associated with uncertainty. This research applies Monte Carlo simulation, value at risk and gain curve analysis, and real option analysis to investigate how the uncertainty of demands affects supply chain planning in order to make better supply chain investment decisions. This analytical framework was used to analyze the ocean shipping plans and inland trucking arrangements for Chiquita. Demands for Product A and front haul over a six-year period were simulated based upon forecasted distributions. The net income, revenue and costs as affected by ocean shipping plans were obtained by inputting the simulated demands to ocean shipping models. The major decision for Chiquita is whether to charter one large ship or two ships which provide approximately equivalent capacity. A large ship would save fuel costs. The plans for two smaller ships have the flexibility of using one ship only if future demand or price reactions warrant it. Using the analytical framework, a plan for two smaller ships is superior to that for one large ship because of significant real option value, particularly in the event of increases in fuel costs in the future. Chiquita's current inland trucking model, a mixed arrangement with a dedicated fleet and common carriers, seems to offer a good solution for the future needs. A model provided in this research offers a simple method to optimize the size of the dedicated fleet.
by Yanfeng Anna Huang.
M.Eng.in Logistics
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Temizer, Selim 1977. "Planning under uncertainty for dynamic collision avoidance." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/64487.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 157-169).
We approach dynamic collision avoidance problem from the perspective of designing collision avoidance systems for unmanned aerial vehicles. Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configurations, we investigate automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder behavior. We first formulate the problem within the Partially Observable Markov Decision Process (POMDP) framework, and use generic MDP/POMDP solvers offline to compute vertical-only avoidance strategies that optimize a cost function to balance flight-plan deviation with risk of collision. We then describe a second framework that performs online planning and allows for 3-D escape maneuvers by starting with possibly dangerous initial flight plans and improving them iteratively. Experimental results with four different sensor modalities and a parametric aircraft performance model demonstrate the suitability of both approaches.
by Selim Temizer.
Ph.D.
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Jin, Shan. "Long term power generation planning under uncertainty." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1468096.
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Maldonado, Jaime 1958. "Strategic planning--an approach to improving airport planning under uncertainty." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/13635.
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Trevizan, Felipe W. "Short-Sighted Probabilistic Planning." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/274.
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Planning is an essential part of intelligent behavior and a ubiquitous task for both humans and rational agents. One framework for planning in the presence of uncertainty is probabilistic planning, in which actions are described by a probability distribution over their possible outcomes. Probabilistic planning has been applied to different real-world scenarios such as public health, sustainability and robotics; however, the usage of probabilistic planning in practice is limited due to the poor performance of existing planners.
In this thesis, we introduce a novel approach to effectively solve probabilistic planning problems by relaxing them into short-sighted problems. A short-sighted problem is a relaxed problem in which the state space of the original problem is pruned and artificial goals are added to heuristically estimate the cost of reaching an original goal from the pruned states. Differently from previously proposed relaxations, short-sighted problems maintain the original structure of actions and no restrictions are imposed in the maximum number of actions that can be executed. Therefore, the solutions for short-sighted problems take into consideration all the probabilistic outcomes of actions and their probabilities. In this thesis, we also study different criteria to generate short-sighted problems, i.e., how to prune the state space, and the relation between the obtained short-sighted models and previously proposed relaxation approaches.
We present different planning algorithms that use short-sighted problems in order to solve probabilistic planning problems. These algorithms iteratively generate and execute optimal policies for short-sighted problems until the goal of the original problem is reached. We also formally analyze the introduced algorithms, focusing on their optimality guarantees with respect to the original probabilistic problem. Finally, this thesis contributes a rich empirical comparison between our algorithms and state-of-the-art probabilistic planners.
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Mittal, Geetanjali 1979. "Real options approach to capacity planning under uncertainty." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28295.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 128-133).
This thesis highlights the effectiveness of Real Options Analysis (ROA) in capacity planning decisions for engineering projects subject to uncertainty. This is in contrast to the irreversible decision-making proposed by the deterministic strategies based on expected estimates of parameters drawn years in advance. Effectiveness is measured by three metrics: cost efficiency, capacity sufficiency and Value at Risk. The study documents the effects of uncertainty on planning facilities with high fixed-costs. It addresses engineers and planners by presenting fundamental insights of ROA without expecting Options-pricing knowledge a priori. The main idea is demonstrated via a case study of hydropower capacity planning. An analytical probabilistic capacity planning tool is developed to compare results given by traditional valuation and ROA. The tool may be useful for determining resource utilization policies and decision-making in the construction of such plants. Two specific options have been examined: (1) Vary size and timing of capacity increment (2) Defer hydropower plant construction to observe demand by relying on low fixed-cost and high operational-cost facilities in the initial years. The conclusion is that dynamic capacity planning approach is more effective if the forecasts are pessimistic or optimistic but not necessarily if realized parameters are similar to forecasts. Decisions based on distribution of driving factors and outcomes may be better aligned with the management's overall risk preferences than those based solely on expected mean of these parameters.
by Geetanjali Mittal.
S.M.
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Almgren, Torgny. "Time planning under uncertainty in a mining environment." Licentiate thesis, Luleå tekniska universitet, 1989. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-25872.
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Martin, Kiel M. (Kiel Michael). "Dynamic planning under uncertainty for theater airlift operations." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40387.
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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.Includes bibliographical references (p. 92-93).
In this thesis, we analyze intratheater airlift operations, and propose methods to improve the planning process. The United States Air Mobility Command is responsible for the air component of the world wide U.S. military logistics network. Due to the current conflict in Iraq, a small cell within Air Mobility Command, known as Theater Direct Delivery, is responsible for supporting ongoing operations by assisting with intratheater airlift. We develop a mathematical programming approach to schedule airlift missions that pick up and deliver prioritized cargo within time windows. In our approach, we employ composite variables to represent entire missions and associated decisions, with each decision variable including information pertaining to the mission routing and scheduling, and assigned aircraft and cargo. We compare our optimization-based approach to one using a greedy heuristic that is representative of the current planning process. Using measures of efficiency and effectiveness, we evaluate and compare the performance of these different approaches. Finally, we adjust selected parameters of our model and measure the resulting changes in operating performance of our solutions, and the required computational effort to generate the solutions.
by Kiel M. Martin.
S.M.
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Davis, Joshua Daniel. "Motion planning under uncertainty: application to an unmanned helicopter." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4280.
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A methodology is presented in this work for intelligent motion planning in an
uncertain environment using a non-local sensor, like a radar sensor, that allows the
sensing of the environment non-locally. This methodology is applied to an unmanned
helicopter navigating a cluttered urban environment. It is shown that the problem of
motion planning in a uncertain environment, under certain assumptions, can be posed as
the adaptive optimal control of an uncertain Markov Decision Process, characterized by a
known, control dependent system, and an unknown, control independent environment.
The strategy for motion planning then reduces to computing the control policy based on
the current estimate of the environment, also known as the "certainty equivalence
principle" in the adaptive control literature. The methodology allows the inclusion of a
non-local sensor into the problem formulation, which significantly accelerates the
convergence of the estimation and planning algorithms. Further, the motion planning and
estimation problems possess special structure which can be exploited to reduce the
computational burden of the associated algorithms significately. As a result of the
methodology developed for motion planning in this thesis, an unmanned helicopter is
able to navigate through a partially known model of the Texas A&M campus.
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Luo, Sifo. "Capacity planning under demand and manufacturing uncertainty for biologics." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112865.
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Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (page 58).
Due to the long lead times and complexity in drug development and approval processes, pharmaceutical companies use long range planning to plan their production for the next 10 years. Capacity planning is largely driven by the long-term demand and its forecast uncertainty. The impact of uncertainties at manufacturing level, such as factory productivity and production success rate, are not entirely taken into account since only the average values of each manufacturing parameter are used. Can we better allocate production among manufacturing facilities when both demand and manufacturing uncertainties are considered? In this thesis a stochastic optimization approach is followed to minimize the deviation from target capacity limit under different manufacturing and demand scenarios. The mixed integer linear model incorporates the impact of demand and manufacturing variation on production allocation among manufacturing facilities through Monte Carlo generated scenarios. The thesis model is designed in a way that can be used as a decision tool to perform robust capacity planning at the strategic level.
by Sifo Luo.
M. Eng. in Supply Chain Management
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Klingebiel, Ronald. "Planning and managing under uncertainty : flexibility in strategic initiatives." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612085.
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Hellander, Anja. "Multi-Hypothesis Motion Planning under Uncertainty Using Local Optimization." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166582.
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Motion planning is defined as the problem of computing a feasible trajectory for an agent to follow. It is a well-studied problem with applications in fields such as robotics, control theory and artificial intelligence. In the last decade there has been an increased interest in algorithms for motion planning under uncertainty where the agent does not know the state of the environment due to, e.g. motion and sensing uncertainties. One approach is to generate an initial feasible trajectory using for example an algorithm such as RRT* and then improve that initial trajectory using local optimization. This thesis proposes a new modification of the RRT* algorithm that can be used to generate initial paths from which initial trajectories for the local optimization step can be generated. Unlike standard RRT*, the modified RRT* generates multiple paths at the same time, all belonging to different families of solutions (homotopy classes). Algorithms for motion planning under uncertainty that rely on local optimization of trajectories can use trajectories generated from these paths as initial solutions. The modified RRT* is implemented and its performance with respect to computation time and number of paths found is evaluated on simple scenarios. The evaluations show that the modified RRT* successfully computes solutions in multiple homotopy classes. Two methods for motion planning under uncertainty, Trajectory-optimized LQG (T-LQG), and a belief space variant of iterative LQG (iLQG) are implemented and combined with the modified RRT*. The performance with respect to cost function improvement, computation time and success rate when following the optimized trajectories for the two methods are evaluated in a simulation study. The results from the simulation studies show that it is advantageous to generate multiple initial trajectories. Some initial trajectories, due to for example passing through narrow passages or through areas with high uncertainties, can only be slightly improved by trajectory optimization or results in trajectories that are hard to follow or with a high collision risk. If multiple initial trajectories are generated the probability is higher that at least one of them will result in an optimized trajectory that is easy to follow, with lower uncertainty and lower collision risk than the initial trajectory. The results also show that iLQG is much more computationally expensive than T-LQG, but that it is better at computing control policies to follow the optimized trajectories.
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Maricar, Noor M. "Efficient Resource Development in Electric Utilities Planning Under Uncertainty." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11258.
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The thesis aims to introduce an efficient resource development strategy in electric utility long term planning under uncertainty considerations. In recent years, electric utilities have recognized the concepts of robustness, flexibility, and risk exposure, to be considered in their resource development strategy. The concept of robustness means to develop resource plans that can perform well for most, if not all futures, while flexibility is to allow inexpensive changes to be made if the future conditions deviate from the base assumptions. A risk exposure concept is used to quantify the risk hazards in planning alternatives for different kinds of future conditions.
This study focuses on two technical issues identified to be important to the process of efficient resource development: decision-making analysis considering robustness and flexibility, and decision-making analysis considering risk exposure. The technique combines probabilistic methods and tradeoff analysis, thereby producing a decision set analysis concept to determine robustness that includes flexibility measures. In addition, risk impact analysis is incorporated to identify the risk exposure in planning alternatives. Contributions of the work are summarized as follows. First, an efficient resource development framework for planning under uncertainty is developed that combines features of utility function, tradeoff analysis, and the analytical hierarchy process, incorporating a performance evaluation approach. Second, the multi-attribute risk-impact analysis method is investigated to handle the risk hazards exposed in power system resource planning. Third, the penetration levels of wind and photovoltaic generation technologies into the total generation system mix, with their constraints, are determined using the decision-making model. The results from two case studies show the benefits of the proposed framework by offering the decision makers various options for lower cost, lower emission, better reliability, and higher efficiency plans.Ph. D.
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Zhao, Bining Zhao. "Electricity-Gas Systems: Operations and Expansion Planning Under Uncertainty." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152398462190157.
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Wirnshofer, Florian [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "State estimation and planning under uncertainty for robot manipulation." Freiburg : Universität, 2021. http://d-nb.info/1238016251/34.
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Guan, Charlie Zeyu. "Efficient planning for near-optimal contact-rich control under uncertainty." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120435.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 91-95).
Path planning classically focuses on avoiding environmental contact. However, some assembly tasks permit contact through compliance, and such contact may allow for more efficient and reliable solutions under action uncertainty. But optimal manipulation plans that leverage environmental contact are difficult to compute. Environmental contact produces complex kinematics that create difficulties for planning. This complexity is usually addressed by discretization over state and action space, but discretization quickly leads to computationally intractability if the optimal solution is desired. To overcome the challenge, we use the insight that only actions on configurations near the contact manifold are likely to involve complex kinematics, while segments of the plan through free space do not. Leveraging this structure can greatly reduce the number of states considered and scales much better with problem complexity. We develop the composite MDP algorithm based on this idea and show that it performs comparably to full MDP solutions at a fraction of the computational cost. However, the composite MDP still requires minutes to hours of computation, which is unsuitable for robots operating in novel environments. To overcome this limitation, we use the insight that environments are generally composed of a limited set of geometries. We can precompute the kinematic models of the dynamic object relative to these constituent geometries (constituent MDPs), and use them to assemble a kinematic model of the dynamic object relative to an environment with all constituent geometries present, by merging state spaces and transition functions. However, the straightforward assembly algorithm does not produce a sufficient computational speedup. Therefore, we introduce four assumptions to significantly reduce computation time. We demonstrate our algorithm to compute policies for novel environments on the order of seconds, without sacrificing solution quality.
by Charlie Zeyu Guan.
S.M.
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Undurti, Aditya. "Planning under uncertainty and constraints for teams of autonomous agents." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68405.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 155-164).
One of the main advantages of unmanned, autonomous vehicles is their potential use in dangerous situations, such as victim search and rescue in the aftermath of an urban disaster. Unmanned vehicles can complement human first responders by performing tasks that do not require human expertise (e.g., communication) and supplement them by providing capabilities a human first responder would not have immediately available (e.g., aerial surveillance). However, for unmanned vehicles to work seamlessly and unintrusively with human responders, a high degree of autonomy and planning is necessary. In particular, the unmanned vehicles should be able to account for the dynamic nature of their operating environment, the uncertain nature of their tasks and outcomes, and the risks that are inherent in working in such a situation. This thesis therefore addresses the problem of planning under uncertainty in the presence of risk. This work formulates the planning problem as a Markov Decision Process with constraints, and offers a formal definition for the notion of "risk". Then, a fast and computationally efficient solution is proposed. Next, the complications that arise when planning for large teams of unmanned vehicles are considered, and a decentralized approach is investigated and shown to be efficient under some assumptions. However some of these assumptions place restrictions - specifically on the amount of risk each agent can take. These restrictions hamper individual agents' ability to adapt to a changing environment. Hence a consensus-based approach that allows agents to take more risk is introduced and shown to be effective in achieving high reward. Finally, some experimental results are presented that validate the performance of the solution techniques proposed.
by Aditya Undurti.
Ph.D.
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Lee, Yun Shin. "Essays on stochastic forecasting and inventory planning under model uncertainty." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609889.
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Krukanont, Pongsak. "System modeling for energy planning and policy making under uncertainty." Kyoto University, 2005. http://hdl.handle.net/2433/144450.
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Kyoto University (京都大学)0048
新制・課程博士
博士(エネルギー科学)
甲第11893号
エネ博第119号
新制||エネ||30(附属図書館)
23673
UT51-2005-N727
京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻
(主査)教授 手塚 哲央, 教授 吉川 榮和, 教授 中込 良廣
学位規則第4条第1項該当
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Forsell, Nicklas. "Planning under risk and uncertainty : optimizing spatial forest management strategies /." Umeå : Dept. of Forest Resource Management, Swedish University of Agricultural Sciences, 2009. http://epsilon.slu.se/200939.pdf.
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Capitanul, Elena Mihaela. "Airport strategic planning under uncertainty : fuzzy dual dynamic programming approach." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30109/document.
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Les aéroports sont des connecteurs critiques dans le système opérationnel de transport aérien. Afin de répondre à leurs obligations opérationnelles, économiques et sociales dans un environnement très volatil, ont besoin d'aéroports à embrasser le changement plutôt que d'y résister. Comme toute autre industrie, font face à des aéroports un large éventail de risques, dont certains spécifiques au transport aérien, les autres ayant seulement une influence indirecte mais assez puissant pour perturber les activités aéroportuaires. La planification longue terme de l'aéroport est devenue une question complexe en raison de la croissance constante de la demande de trafic aérien. Une nouvelle dimension de complexité est apparue lorsque l'incertitude a commencé à avoir un impact plus en plus perturbatrice, et significativement coûteuse sur le développement des infrastructures aéroportuaires. Historiquement, la capacité des outils traditionnels pour atténuer le risque et l'incertitude ont avérée inefficace. D'innombrables événements imprévus comme les attaques terroristes, la récession économique, les catastrophes naturelles, ont eu un impact dramatique sur les niveaux de trafic, certains avec une portée mondiale. Pour ce type hautement improbable d'événements peut être ajouté les progrès technologiques, de nouvelles modèles d'affaires des compagnies aériennes et aéroports, les changements de politique et de réglementation, préoccupation croissante pour l'impact environnemental. Dans ce contexte, la thèse met en avant une approche novatrice pour aborder l'évaluation des risques et de l'atténuation dans l'incertitude dans les projets de développement des infrastructures aéroportuaires à long terme. La thèse se développe sur le formalisme récemment développée de nombres flous comme un outil clé pour aborder l'incertitude. Après un examen approfondi de l'industrie aéroportuaire dans le contexte des environnements incertains, nombres double flous et double floue arithmétiques sont introduits. Comme le projet de développement des infrastructures aéroportuaires est un autre cas de problème de prise de décision en plusieurs étapes, la programmation dynamique est prise en compte afin d'optimiser le processus séquentiel de prise de décision. L'originalité de l'approche réside dans le fait que l'ensemble du processus sera floue et la composante double floue de la programmation dynamique sera introduite. Pour valider notre méthode, une étude de cas sera développéeAirports are critical connectors in the air transportation operational system. In order to meet their operational, economic and social obligations in a very volatile environment, airports need to embrace change rather than resist it. Like any other industry, airports face a wide array of risks, some specific to air transportation, other having only an indirect influence but powerful enough to disrupt airport activities. Long term airport planning has become a complex issue due to the constant growth in air traffic demand. A new dimension of complexity emerged when uncertainty began having a more, and more disruptive, and significantly costly impact on developing airport infrastructure. Historically, the ability of traditional risk and uncertainty mitigation tools proved inefficient. Countless unforeseen events like terrorist attacks, economic recession, natural disasters, had a dramatic impact on traffic levels, some with a global reach. To these highly improbable type of events can be added technological advancements, new airlines and airports business models, policy and regulation changes, increasing concern for environmental impact. In this context, the thesis puts forward an innovative approach for addressing risk assessment and mitigation under uncertainty in long-term airport infrastructure development projects. The thesis expands on the newly developed formalism of fuzzy dual numbers as a key tool to address uncertainty. After a comprehensive review of the airport industry in the context of uncertain environments, fuzzy dual numbers and fuzzy dual calculus are introduced. Since airport infrastructure development project is another case of multi-stage decision-making problem, dynamic programming is considered in order to optimize the sequential decision making process. The originality of the approach resides in the fact that the entire process will be fuzzified and fuzzy dual dynamic programming components will be introduced. To validate our method, a study case will be developed
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Jamalnia, Aboozar. "Evaluating the performance of aggregate production planning strategies under uncertainty." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/evaluating-the-performance-of-aggregate-production-planning-strategies-under-uncertainty(073f9c1d-2b78-474a-8fa1-cbb0b4878354).html.
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The thesis is presented in three papers format. Paper 1 presents the first bibliometric literature survey of its kind on aggregate production planning (APP) in presence of uncertainty. It surveys a wide range of the literatures which employ operations research/management science methodologies to deal with APP in presence of uncertainty by classifying them into six main categories such as stochastic mathematical programming, fuzzy mathematical programming and simulation. After a preliminary literature analysis, e.g. with regard to number of publications by journal and publication frequency by country, the literature about each of these categories is shortly reviewed. Then, a more detailed statistical analysis of the surveyed research, with respect to the source of uncertainty, number of publications trend over time, adopted APP strategies, applied management science methodologies and their sub-categories, and so on, is presented. Finally, possible future research paths are discussed on the basis of identified research trends and research gaps. The second paper proposes a novel decision model to APP decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of present study, are discussed. The third paper is to appraise the performance of different APP strategies in presence of uncertainty. The relevant models for various APP strategies including the pure chase, the pure level, the modified chase and the modified level strategies are derived from the fundamental model developed for the mixed chase and level strategy in paper 2. The same procedure, which is used in paper 2, follows to solve the models constructed for these strategies with respect to the aforementioned objectives/criteria in order to provide business and managerial insights to operations managers about the effectiveness and practicality of these APP policies under uncertainty. Multiple criteria decision making (MCDM) methods such as additive value function (AVF), the technique for order of preference by similarity to ideal solution (TOPSIS) and VIKOR are also used besides multi-objective optimisation to assess the overall performance of each APP strategy.
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Atenas, Maldonado Felipe Eduardo. "A two-stage model for planning energy investment under uncertainty." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170925.
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Tesis para optar al grado de Magíster en Ciencias de la Ingeniería, Mención Matemáticas AplicadasMemoria para optar al título de Ingeniero Civil Matemático
We consider risk-averse stochastic programming models for the Generation Expansion Planning problem for energy systems with here-and-now investment decisions and generation variables of recourse. The resulting problem is coupled both along scenarios and along power plants. We develop a new decomposition technique to solve the energy optimization problem, resulting from the combination of two existing procedures, one to deal with stochastic programming problems through decomposition for different realizations of the stochastic process representing the uncertain data, and the second one is a method aim to find solutions to nonsmooth optimization problems. More precisely, we combine the Progressive Hedging algorithm to deal with scenario separability, obtaining a separate subproblem for each scenario, and an inexact proximal bundle method to handle separability for different power plants in each subproblem. By suitably combining these approaches, if the evaluation errors of the proximal bundle method vanish asymptotically, then bundle method converges to an approximate solution to each scenario subproblem. Thus, under mild convexity assumptions, the Progressive Hedging algorithm generates a sequence that converges to a solution to the original problem. The methodology is satisfactorily assessed on a test instance of the Generation Expansion Planning problem, whose reduced size allows us to compare the results with those obtained when solving the problem directly, and without decomposition.
CONICYT-PFCHA/Magister Nacional/2018-22181067 y CMM Conicyt PIA AFB170001
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Tabaeh, Izadi Masoumeh. "On knowledge representation and decision making under uncertainty." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103012.
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Designing systems with the ability to make optimal decisions under uncertainty is one of the goals of artificial intelligence. However, in many applications the design of optimal planners is complicated due to imprecise inputs and uncertain outputs resulting from stochastic dynamics. Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical framework to model these kinds of problems. However, the high computational demand of solution methods for POMDPs is a drawback for applying them in practice.In this thesis, we present a two-fold approach for improving the tractability of POMDP planning. First, we focus on designing good heuristics for POMDP approximation algorithms. We aim to scale up the efficiency of a class of POMDP approximations called point-based planning methods by designing a good planning space. We study the effect of three properties of reachable belief state points that may influence the performance of point-based approximation methods. Second, we investigate approaches to designing good controllers using an alternative representation of systems with partial observability called Predictive State Representation (PSR). This part of the thesis advocates the usefulness and practicality of PSRs in planning under uncertainty. We also attempt to move some useful characteristics of the PSR model, which has a predictive view of the world, to the POMDP model, which has a probabilistic view of the hidden states of the world. We propose a planning algorithm motivated by the connections between the two models.
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Fowler, David W. "Branching constraint satisfaction problems : sequential constrained decision making under uncertainty." Thesis, University of Aberdeen, 2002. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU153443.
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One of the main characteristics of our world is uncertainty. Making plans for the future is difficult, as we do not know exactly what the future holds. Companies must be flexible, ready to cope with the unpredictable demands that are placed on them. As a result, plans are often either short term, or tend to change soon after they are made. Another feature of the modern world is its pace. Decisions must be made quickly, or events may make them out of date before they can be implemented. In this thesis, we look at decision making problems in the presence of uncertainty about how the problem may develop over time, and in particular where the decisions must be made efficiently. Constraint based reasoning has proven to be a very successful technique for supporting decision making, but to date it has assumed static problems. In this thesis, we will show that constraint based methods can be used to reason about uncertain futures, and we will present a method which incorporates some ideas from decision theory to represent and solve such problems. In particular, we will formulate a class of problems, develop systematic optimisation search techniques, incomplete heuristic methods and compare with existing techniques.
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Cirillo, Marcello. "Planning in Inhabited Environments : Human-Aware Task Planning and Activity Recognition." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12466.
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Promised some decades ago by researchers in artificial intelligence and robotics as an imminent breakthrough in our everyday lives, a robotic assistant that could work with us in our home and our workplace is a dream still far from being fulfilled. The work presented in this thesis aims at bringing this future vision a little closer to realization. Here, we start from the assumption that an efficient robotic helper should not impose constraints on users' activities, but rather perform its tasks unobtrusively to fulfill its goals and to facilitate people in achieving their objectives. Also, the helper should be able to consider the outcome of possible future actions by the human users, to assess how those would affect the environment with respect to the agent's objectives, and to predict when its support will be needed. In this thesis we address two highly interconnected problems that are essential for the cohabitation of people and service robots: robot task planning and human activity recognition. First, we present human-aware planning, that is, our approach to robot high-level symbolic reasoning for plan generation. Human-aware planning can be applied in situations where there is a controllable agent, the robot, whose actions we can plan, and one or more uncontrollable agents, the human users, whose future actions we can only try to predict. In our approach, therefore, the knowledge of the users' current and future activities is an important prerequisite. We define human-aware as a new type of planning problem, we formalize the extensions needed by a classical planner to solve such a problem, and we present the implementation of a planner that satisfies all identified requirements. In this thesis we explore also a second issue, which is a prerequisite to the first one: human activity monitoring in intelligent environments. We adopt a knowledge driven approach to activity recognition, whereby a constraint-based domain description is used to correlate sensor readings to human activities. We validate our solutions to both human-aware planning and activity recognition both theoretically and experimentally, describing a number of explanatory examples and test runs in a real environment.
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Andersson, Elise, and Veronica Bertilsson. "Planera och budgetera osäkerhet i skolförvaltningen : en kvalitativ undersökning i tre kommuner." Thesis, Högskolan Kristianstad, Sektionen för hälsa och samhälle, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-9658.
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Problematiken för vår undersökning grundar sig i den nya skollagen som trädde i kraft i juli 2011. Där står det reglerat att kommunen är ansvarig för alla elever i en kommun. Då det nu råder fritt skolval enligt den nya skollagen, kan elever och dess vårdnadshavare själva välja vilken grundskola de vill gå på. Eleverna kan antingen välja en kommunal grundskola eller en friskola och därmed blir planeringen och budgeteringen av elevantalet osäker för kommunerna då de inte vet hur många elever som kommer att gå i de olika skolorna. Vårt syfte med denna undersökning är att skapa förståelse för och en fördjupad kunskap om hur skolförvaltningen inom kommunen går tillväga för att planera och budgetera osäkerhet och om budgetarbetet har påverkats av det fria skolvalet och friskolorna. Undersökningen baseras på en kvalitativ metod med tre intervjuer av ekonomer på skolförvaltningen i tre kommuner; Helsingborgs stad, Lunds kommun och Kristianstads kommun. Utifrån det empiriska materialet har det framkommit att ledorden i budgetarbetet är; planering, kommunikation och kontroll. För att kunna budgetera för verksamheten som är oviss på grund av det osäkra elevantalet är dessa ledord viktiga i hela budgetarbetet, från förberedelser av budget till uppföljningen av budgetutfallet. Vad som har förändrats med införandet av det fria skolvalet och friskolorna är att det har blivit svårare att planera verksamheten och veta vilken skola som eleverna kommer att välja. Detta har medfört att behovet av långsiktig planering har blivit ännu viktigare och rektorerna har infört ett par tillvägagångssätt för att försöka ta reda på vilken skola eleverna kommer att välja. Det fria skolvalet och friskolorna har även bidragit till att kontrollen och själva uppföljningen har blivit än viktigare än det var tidigare.The problem of our study is based on the new law of school which came into force in July 2011. In the law it is regulated that the municipality is responsible for all pupils in the municipality. Because of the new law of school the students and their guardians are free to choose which primary school they want to go to. Students can either choose a local school or an independent school and because of that planning and budgeting of the student population becomes more difficult for the municipalities when they do not know how many students will attend different schools. Our purpose in this study is to create an understanding and a deeper knowledge of how the school board in the municipality proceed in order to plan and budget uncertainty and if the budget has been affected by the free school choice and independent schools. This survey is based on a qualitative approach with interviews with economist on the school board in three municipalities; Helsingborgs’s city, Lund’s municipality and Kristianstad’s municipality. Based on the empirical material, it has emerged that the keywords in the budget are: planning, communication and control. To be able to budget for the schools which are uncertain because of the uncertain pupil numbers, these keywords are important throughout the whole budget process, from preparation of the budget to the monitoring of the budget. What has changed with the introduction of the free school choice and independent schools is that it becomes more difficult to plan for the schools and to know which school the students will choice. This has meant that long-term planning has become more important and principals have introduced a couple of approaches to attempt to find out at which school students will choice. The free school choice and independent schools have also contributed to the control and monitoring has become more important now than it was before.
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Shi, Yuan, and 石园. "A portfolio approach to procurement planning and risk hedging under uncertainty." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44905051.
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FILIPPO, THAIS HERNANDEZ. "STRATEGIC INVESTMENTS PLANNING AND EXECUTION UNDER UNCERTAINTY: REAL OPTION THEORY CONTRIBUTIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19254@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORPROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Este trabalho se propõe a orientar como utilizar de forma conjunta e complementar os conceitos de Estratégia Empresarial e Finanças, mais especificamente da Teoria de Opções Reais, uma moderna teoria de análise de investimentos sob incerteza. Nas empresas vem coexistindo dois sistemas para a alocação de recursos: o planejamento estratégico e a orçamentação de capital, em geral, o primeiro define as iniciativas estratégicas e o segundo faz a verificação de viabilidade econômico-financeira destas iniciativas. Entretanto, muitas vezes a intuição vai contra as análises financeiras tradicionais. Além disso, a complexidade da tomada de decisão estratégica em um ambiente de incerteza vem crescendo em função do acelerado dinamismo do mercado e da infinidade de oportunidades que aparecem em um mundo altamente globalizado e conectado. Portanto, a união dos conceitos atualmente dispersos nestes dois sistemas é de fundamental importância para a deliberação e execução de estratégias consistentes e lucrativas. A Teoria de Opções Reais, cujas características se aproximam mais da realidade estratégica por considerar as flexibilidades gerenciais e não ter a abordagem passiva das ferramentas tradicionais, aparece, então, como uma resposta a esta necessidade de aproximação. Neste contexto, esta dissertação busca analisar a contribuição desta teoria à Estratégia Empresarial e construir um modelo que aproxime estes dois campos de estudo e direcione a prática de planejamento e execução de investimentos estratégicos.
This work intends to give guidance on how to use jointly and complementarily the concepts of Corporate Strategy and Finance, specifically the Theory of Real Options, a modern theory of investment analysis under uncertainty. In corporate practice are co-existing two systems for resource allocation, strategic planning and capital budgeting. Usually the first defines the strategic initiatives and the second checks the economic viability of these initiatives. However, intuition often goes against the traditional financial analysis. Moreover, the complexity of strategic decision making in an uncertain environment is growing rapidly as a function of market dynamics and the myriad of opportunities that appear in a highly globalized and connected world. Therefore, the union of these two concepts currently dispersed in these systems is of fundamental importance for the deliberation and execution of consistent and profitable strategies. Real Options Theory, whose characteristics are closer to reality by considering the strategic and managerial flexibility and not having the passive approach of traditional tools, then appears as a response to this need for approximation. In this context, this dissertation seeks to analyze the contribution of this theory to business strategy and build a model that combines these two fields of study and directs the practice of planning and execution of strategic investments.
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Gandhi, Rikin Bharat 1981. "Examination of planning under uncertainty algorithms for cooperative unmanned aerial vehicles." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/28902.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 121-124).
(cont.) of UAVs and targets. Additionally, sensitivity trials are used to capture each algorithm's robustness to real world planning environments where planners must negotiate incomplete or inaccurate system models. The mission performances of both methods degrade as the quality of their system models worsen
Cooperation is essential for numerous tasks. Cooperative planning seeks actions to achieve a team's common set of objectives by balancing both the benefits and the costs of execution. Uncertainty in action outcomes and external threats complicates this task. Planning algorithms can be generally classified into two categories: exact and heuristic. In this thesis, an exact planner, based on Markov decision processes, and a heuristic, receding horizon controller are evaluated in typical planning problems. The exact planner searches for an optimal policy with global contingencies, while the heuristic controller sequentially approximates the global plans over local horizons. Generally, the two planners trade mission and computational performance. Although the results are limited to specific problem instances, they provide characterizations of the algorithms' capabilities and limitations. The exact planner's policy provides an optimal course of action for all possible conditions over the mission duration; however, the algorithm consumes substantial computational resources. On the other hand, the heuristic approach does not guarantee optimality, but may form worthy plans without evaluating every contingency. On a fully-observable battlefield, the planners coordinate a team of unmanned aerial vehicles (UAVs) to obtain a maximum reward by destroying targets. Stochastic components, including UAV capability and attrition, represent uncertainty in the simulated missions. For a majority of the examined scenarios, the exact planner exhibits statistically better mission performance at considerably greater computational cost in comparison to the heuristic controller. Scalability studies show that these trends intensify in larger missions that include increasing numbers
by Rikin Bharat Gandhi.
S.M.
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He, Ruijie. "Semi-conditional planners for efficient planning under uncertainty with macro-actions." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59660.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 163-168).
Planning in large, partially observable domains is challenging, especially when good performance requires considering situations far in the future. Existing planners typically construct a policy by performing fully conditional planning, where each future action is conditioned on a set of possible observations that could be obtained at every timestep. Unfortunately, fully-conditional planning can be computationally expensive, and state-of-the-art solvers are either limited in the size of problems that can be solved, or can only plan out to a limited horizon. We propose that for a large class of real-world, planning under uncertainty problems, it is necessary to perform far-lookahead decision-making, but unnecessary to construct policies that condition all actions on observations obtained at the previous timestep. Instead, these problems can be solved by performing semi conditional planning, where the constructed policy only conditions actions on observations at certain key points. Between these key points, the policy assumes that a macro-action - a temporally-extended, fixed length, open-loop action sequence, comprising a series of primitive actions, is executed. These macro-actions are evaluated within a forward-search framework, which only considers beliefs that are reachable from the agent's current belief under different actions and observations; a belief summarizes an agent's past history of actions and observations. Together, semi-conditional planning in a forward search manner restricts the policy space in exchange for conditional planning out to a longer-horizon. Two technical challenges have to be overcome in order to perform semi-conditional planning efficiently - how the macro-actions can be automatically generated, as well as how to efficiently incorporate the macro action into the forward search framework. We propose an algorithm which automatically constructs the macro-actions that are evaluated within a forward search planning framework, iteratively refining the macro actions as more computation time is made available for planning. In addition, we show that for a subset of problem domains, it is possible to analytically compute the distribution over posterior beliefs that result from a single macro-action. This ability to directly compute a distribution over posterior beliefs enables us to enjoy computational savings when performing macro-action forward search. Performance and computational analysis for the algorithms proposed in this thesis are presented, as well as simulation experiments that demonstrate superior performance relative to existing state-of-the-art solvers on large planning under uncertainty domains. We also demonstrate our planning under uncertainty algorithms on target-tracking applications for an actual autonomous helicopter, highlighting the practical potential for planning in real-world, long-horizon, partially observable domains.
by Ruijie He.
Ph.D.
48
Counsell, Christian John Adam. "End-to-end ensemble modelling for water resources planning under uncertainty." Thesis, Open University, 2018. http://oro.open.ac.uk/56567/.
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A hydrological model ensemble, considering two model structures (CatchMOD and PDM), parameter uncertainty and two contrasting methods for estimating potential evapotranspiration (PET), is developed to investigate the relative significance of different sources of uncertainty for water resources planning in the Thames basin. This model ensemble is driven by an ensemble of UKCP09 probabilistic and Future Flows climate change projections, for the 2030s, 2050s and 2080s, to quantify the projected impacts on a range of metrics of relevance to water resources planners using a water resources system model of London. These sources of supply-side uncertainty are shown to be significant, with the uncertainty associated with the climate change scenarios the largest but hydrological modelling uncertainty, and the method used to estimate PET also shown to be considerable. In terms of overall impacts, the central estimates for the 2030s, 2050s and 2080s are reductions in available resource of around 7%, 11% and 14% respectively. These impacts are shown to equate to economic costs of the order of £360m, £610m and £735m respectively to mitigate such reductions in supply. The range of uncertainty within each time-horizon is large, greater than the differences between the time-horizons, presenting a significant challenge in deciding the level and timing of investments to mitigate emerging risks. As an example, impacts considered reasonably likely by the 2080s (e.g. a central estimate of 14% impact on deployable output using both PET methods) may be as likely by the 2030s (e. g. using only the modified Penman-Monteith PET method). The estimates of future supply reliability are contrasted with demand forecasts and whilst the pressure associated with the latter is shown to be greater, both are significant and subject to large degrees of uncertainty. This thesis also highlights the need for detailed examination of hydrological model structures to provide evidence as to their strengths and weaknesses in their representation of key processes, particularly during droughts. The limitations of the climate change products currently used in the industry, particularly with regards to droughts and estimating changes in PET, are also explored. Significant ongoing research is developing decision-making approaches to support the planning of robust and resilient systems under an uncertain future. This thesis demonstrates that alongside this development, more research is needed to understand, identify and quantify the significant sources of uncertainty that need to be considered as part of the decision-making process.
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Lin, Wei-liang. "Planning under demand and capacity uncertainty in printed circuit board assembly /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Abusdal, Håvard. "A Decision Support Methodology for Strategic Planning Under Uncertainty in Maritime Transportation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18555.
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Measured in volume approximately 80 % of world trade is carried at sea and with just as many different actors the shipping industry acts close to a perfect market. The highly volatile nature of the industry with unexpected market fluctuations is the basis for the major decisions shipping companies are making. Especially the fleet size and mix problem in a strategic setting involving fleet changes during several planning periods as a company growth policy. This decision is therefore highly dependent on correct timing for those who want to succeed and an introduction to the shipping industry is given to state these properties. In this thesis various optimization models solving the fleet size and mix problem are presented where the best suited model structure related to the topic is chosen. This model is of deterministic nature, meaning that all input values are known, and based upon predefined routes. The decision regarding the fleet composition during several planning periods is aiming at determining an optimal fleet for a given market. The validity of the results solely relies on input data, which is highly uncertain into an unknown future. The predictions need to coincide with the real life development in order for the results to maintain its validity.Two different trades are used as cases, solved with the models presented. Some input parameters are changed and the differences are investigated. The main findings imply that only relative small changes of the input parameters resulted in very different decisions. The related loss of making the wrong decision is observed in the region of 100 – 200 million USD during three years. This large loss potential and the uncertainty related to the input parameters leads to a need for a method minimizing these effects. An approach is developed to treat uncertainties minimizing the losses by finding a robust fleet capable of handling a large set of generated future scenarios, called the “Scenario Algorithm”. The approach is divided into three main steps; the scenario generating step where development are based on historical fluctuations, a deterministic solution with the given scenario as basis and finally storing of all the solutions with a statistical analysis of the output. The algorithm is used on the two cases with two different scenario generating approaches, based on an exponential- and a continuous uniform distribution. The fleet size and mix decisions which appeared with the highest frequency were chosen, and gave a consistent estimate based on risk aversion decreasing the potential of making losses.The approaches presented in this thesis is not meant to give a correct answer on how the future will be, but help the decisions makers reduce the uncertainty connected to the strategic decision. The deterministic model give valuable information with a given scenario as input, but the model is only capable of evaluate the scenarios individually. The result found by the scenario algorithm evaluating scenarios collectively is therefore of higher value since it provide a more robust solution.