Relevant bibliographies by topics / Planning under uncertainity

Academic literature on the topic 'Planning under uncertainity'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Planning under uncertainity"

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Mehta, Sahil, and Prasenjit Basak. "A Novel Design, Economic Assessment, and Fuzzy-Based Technical Validation of an Islanded Microgrid: A Case Study on Load Model of Kibber Village in Himachal Pradesh." International Transactions on Electrical Energy Systems 2022 (November 10, 2022): 1–17. http://dx.doi.org/10.1155/2022/9639253.

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Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification.
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HAFEZ, WASSIM ALY. "AUTONOMOUS PLANNING UNDER UNCERTAINTY: PLANNING MODELS." International Journal of General Systems 15, no. 4 (December 1989): 321–45. http://dx.doi.org/10.1080/03081078908935056.

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Li, Wenkai, Chi-Wai Hui, Pu Li, and An-Xue Li. "Refinery Planning under Uncertainty." Industrial & Engineering Chemistry Research 43, no. 21 (October 2004): 6742–55. http://dx.doi.org/10.1021/ie049737d.

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Carpaneto, Enrico, Gianfranco Chicco, Pierluigi Mancarella, and Angela Russo. "Cogeneration planning under uncertainty." Applied Energy 88, no. 4 (April 2011): 1059–67. http://dx.doi.org/10.1016/j.apenergy.2010.10.014.

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Wu, Lilian Shiao-Yen, J. R. M. Hosking, and Jeanne M. Doll. "Business planning under uncertainty." International Journal of Forecasting 8, no. 4 (December 1992): 545–57. http://dx.doi.org/10.1016/0169-2070(92)90065-h.

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Khodaei, Amin, Shay Bahramirad, and Mohammad Shahidehpour. "Microgrid Planning Under Uncertainty." IEEE Transactions on Power Systems 30, no. 5 (September 2015): 2417–25. http://dx.doi.org/10.1109/tpwrs.2014.2361094.

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Ierapetritou, M. G., E. N. Pistikopoulos, and C. A. Floudas. "Operational planning under uncertainty." Computers & Chemical Engineering 18 (January 1994): S553—S557. http://dx.doi.org/10.1016/0098-1354(94)80090-1.

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Ierapetritou, M. G., E. N. Pistikopoulos, and C. A. Floudas. "Operational planning under uncertainty." Computers & Chemical Engineering 20, no. 12 (January 1996): 1499–516. http://dx.doi.org/10.1016/0098-1354(95)00235-9.

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Goretzki, Lukas, and Martin Messner. "Coordination under uncertainty." Qualitative Research in Accounting & Management 13, no. 1 (April 18, 2016): 92–126. http://dx.doi.org/10.1108/qram-09-2015-0070.

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Purpose This paper aims to examine how managers use planning meetings to coordinate their actions in light of an uncertain future. Existing literature suggests that coordination under uncertainty requires a “dynamic” approach to planning, which is often realized in the form of rolling forecasts and frequent cross-functional exchange. Not so much is known, however, about the micro-level process through which coordination is achieved. This paper suggests that a sensemaking perspective and a focus on “planning talk” are particularly helpful to understand how actors come to a shared understanding of an uncertain future, based upon which they can coordinate their actions. Design/methodology/approach This paper builds upon a qualitative case study in the Austrian production site of an international manufacturing company. Drawing on a sensemaking perspective, the paper analyses monthly held “planning meetings” in which sales and production managers discuss sales forecasts for the coming months and talk about how to align demand and supply. Findings The authors show how collective sensemaking unfolds in planning meetings and highlight the role that “plausibilization” of expectations, “calculative reasoning” and “filtering” of information play in this process. This case analysis also sheds light on the challenges that such a sensemaking process may be subject to. In particular, this paper finds that competing hierarchical accountabilities may influence the collective sensemaking process and render coordination more challenging. Originality/value The paper contributes to the hitherto limited management accounting and control literature on operational planning, especially its coordination function. It also extends the management accounting and control literature that draws on the concept of sensemaking. The study shows how actors involved in planning meetings create a common understanding of the current and future situation and what sensemaking mechanisms facilitate this process. In this respect, this paper is particularly interested in the role that accounting and other types of numbers can play in this context. Furthermore, it theorizes on the conditions that allow managers to overcome concerns with hierarchical accountabilities and enact socializing forms of accountability, which is often necessary to come to agreements on actions to be taken.
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Gelfi, Mustarakh, and Hendra Achiari. "Port Planning Under Deep Uncertainty." CSID Journal of Infrastructure Development 3, no. 1 (May 21, 2020): 18. http://dx.doi.org/10.32783/csid-jid.v3i1.79.

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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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Books on the topic "Planning under uncertainity"

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Sunil, Sharma. Strategic judgment under pervasive uncertainity. Ahmedabad: Indian Institute of Management, 2015.

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Formulation of tradeoffs in planning under uncertainty. London: Pitman, 1990.

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Ortgiese, Michael. Räumliche Planung unter Unsicherheit. Karlsruhe: Institut für Städtebau und Landesplanung Universität Karlsruhe, 1997.

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Infanger, Gerd. Planning under uncertainty: Solving large-scale stochastic linear programs. Danvers, Mass: Boyd & Fraser, 1994.

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IMACS, International Symposium (2nd 1984 Upton N. Y. ). Energy markets in the longer-term: Planning under uncertainty. Amsterdam: North-Holland, 1985.

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Infanger, Gerd. Planning under uncertainty: Solving large-scale stochastic linear programs. Danvers, Mass: Boyd & Fraser, 1993.

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Shao, Juping, Yanan Sun, and Bernd Noche. Optimization of Integrated Supply Chain Planning under Multiple Uncertainty. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47250-7.

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Lessons from RAND's work on planning under uncertainty for national security. Santa Monica, CA: RAND, 2012.

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Production and decision theory under uncertainty. Oxford: Basil Blackwell, 1987.

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Strategic choice under uncertainty: Multinational corporations and the pressure to disinvest from South Africa. Lanham, Md: University Press of America, 2010.

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Book chapters on the topic "Planning under uncertainity"

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O’Sullivan, Patrick, Gary D. Holtzclaw, and Gerald Barber. "Road Planning under Uncertainty." In Transport Network Planning, 128–38. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003182993-7.

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Agha-Mohammadi, Ali-Akbar, Sandip Kumar, and Suman Chakravorty. "Motion Planning Under Uncertainty." In Advances in Intelligent and Autonomous Aerospace Systems, 309–85. Reston, VA: American Institute of Aeronautics and Astronautics, Inc., 2012. http://dx.doi.org/10.2514/5.9781600868962.0309.0386.

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Walker, Warren E., Vincent A. W. J. Marchau, and Jan H. Kwakkel. "Dynamic Adaptive Planning (DAP)." In Decision Making under Deep Uncertainty, 53–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_3.

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Lucas, C., G. Mitra, and S. A. Mirhassani. "Supply Chain Planning Under Uncertainty." In Quick Response in the Supply Chain, 77–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59997-2_10.

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Paniagua-Arís, Enrique, José T. Palma-Méndez, and Fernando Martín-Rubio. "A Planning under Uncertainty Model." In Computer Aided Systems Theory — EUROCAST 2001, 196–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45654-6_16.

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Wollmer, Richard D. "Critical path planning under uncertainty." In Mathematical Programming Essays in Honor of George B. Dantzig Part II, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0121082.

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Dubois, Didier, and Hélène Fargier. "Qualitative Decision Rules Under Uncertainty." In Planning Based on Decision Theory, 3–26. Vienna: Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-2530-4_1.

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Friske, Letícia Maria, and Carlos Henrique Costa Ribeiro. "Planning Under Uncertainty with Abstraction Hierarchies." In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 1057–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_126.

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Faísca, Nuno P., and Michael C. Georgiadis. "Planning and Material Design Under Uncertainty." In Process Systems Engineering, 229–53. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2014. http://dx.doi.org/10.1002/9783527631209.ch9.

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Faísca, Nuno P., and Michael C. Georgiadis. "Planning and Material Design Under Uncertainty." In Multi-Parametric Programming, 229–53. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527631216.ch9.

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Conference papers on the topic "Planning under uncertainity"

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Basu, A., and A. Elnagar. "Planning under uncertainty." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240360.

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Vaziri, Kabeh, Paul Carr, and Linda Nozick. "Program planning under uncertainty." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419845.

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Abramson, Mark, Corban Bryant, Francis Carr, Ramsay Key, Stephan Kolitz, and Philemon Sakamoto. "Robust Planning Under Uncertainty." In Infotech@Aerospace. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-7027.

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Babu, Neela P., Jan Kuakkel, Daniel P. Loucks, and Warren E. Walker. "Capacity Planning under Nonstationary Uncertainties." In First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Anaylsis (ISUMA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41170(400)101.

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Chakravorty, S., and R. Saha. "Hierarchical motion planning under uncertainty." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434836.

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How, Jonathan P. "Planning and Learning under Uncertainty." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3105812.

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Ahmadi, Mohamadreza, Masahiro Ono, Michel D. Ingham, Richard M. Murray, and Aaron D. Ames. "Risk-Averse Planning Under Uncertainty." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147792.

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Zhao, Bing. "Analysis and Control for Hydrosystems under Uncertainty." In 29th Annual Water Resources Planning and Management Conference. Reston, VA: American Society of Civil Engineers, 1999. http://dx.doi.org/10.1061/40430(1999)136.

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Haqqani, Mohammad, Xiaodong Li, and Xinghuo Yu. "Multi-objective journey planning under uncertainty." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205556.

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Saborío, Juan Carlos, and Joachim Hertzberg. "Practical Assumptions for Planning Under Uncertainty." In 9th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006189004970502.

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Reports on the topic "Planning under uncertainity"

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Robinson, Stephen M. Planning Under Uncertainity: Methods and Applications. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada343351.

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Ferris, Michael C., and Stephen M. Robinson. Planning Under Uncertainty: Methods and Applications. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada398087.

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Dantzig, George B., and Peter W. Glynn. Parallel Processors for Planning Under Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada199067.

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Robinson, Stephen M. Planning Under Uncertainty: Methods and Applications. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada522122.

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Ferris, Michael C., and Stephen M. Robinson. Planning Under Uncertainty: Methods and Applications. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada422242.

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Kuter, Ugur, Dana Nau, and John F. Lemmer. Interactive Planning under Uncertainty with Casual Modeling and Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada447944.

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Infanger, G. Planning under uncertainty solving large-scale stochastic linear programs. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/10158866.

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Infanger, G. Planning under uncertainty solving large-scale stochastic linear programs. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/6169922.

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Manski, Charles. Vaccination Planning under Uncertainty, with Application to Covid-19. Cambridge, MA: National Bureau of Economic Research, February 2021. http://dx.doi.org/10.3386/w28446.

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10

Bleach, R. D., L. Burger, J. E. Burke, and J. D. Carlson. Planning Under Uncertainty: SDIO and the Emerging U.S.-Soviet Interaction. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada344799.

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