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Journal articles on the topic 'Planning under uncertainity'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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11

Jaillet, Patrick, Gar Goei Loke, and Melvyn Sim. "Strategic Workforce Planning Under Uncertainty." Operations Research 70, no. 2 (March 2022): 1042–65. http://dx.doi.org/10.1287/opre.2021.2183.

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A new study in the INFORMS journal Operations Research proposes a data-driven model for conducting strategic workforce planning in organizations. The model optimizes for recruitment and promotions by balancing the risks of not meeting headcount, budget, and productivity constraints, while keeping within a prescribed organizational structure. Analysis using the model indicates that there are increased workforce risks faced by organizations that are not in a state of growth or organizations that face limitations to organizational renewal (such as bureaucracies).

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12

Karakaya, Şakir, and Gülser Köksal. "Product-line planning under uncertainty." Computers & Operations Research 138 (February 2022): 105565. http://dx.doi.org/10.1016/j.cor.2021.105565.

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13

Wiener, Jan M., Matthieu Lafon, and Alain Berthoz. "Path planning under spatial uncertainty." Memory & Cognition 36, no. 3 (April 2008): 495–504. http://dx.doi.org/10.3758/mc.36.3.495.

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14

GAO, Yang, Hao XU, Mengqi HU, Jiang LIU, and Jiahao LI. "Path planning under localization uncertainty." Journal Européen des Systèmes Automatisés 50, no. 4-6 (December 28, 2017): 435–48. http://dx.doi.org/10.3166/jesa.50.435-448.

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15

Ahmed, Shabbir, and Nikolaos V. Sahinidis. "Robust Process Planning under Uncertainty." Industrial & Engineering Chemistry Research 37, no. 5 (May 1998): 1883–92. http://dx.doi.org/10.1021/ie970694t.

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16

Berg, G. J., and T. A. M. Sharaf. "Dynamic transmission planning under uncertainty." Electric Power Systems Research 8, no. 2 (March 1985): 131–36. http://dx.doi.org/10.1016/0378-7796(85)90042-2.

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17

Vlassis, Nikos, Geoff Gordon, and Joelle Pineau. "Planning under uncertainty in robotics." Robotics and Autonomous Systems 54, no. 11 (November 2006): 885–86. http://dx.doi.org/10.1016/j.robot.2006.06.001.

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18

Paraskevopoulos, Dimitris, Elias Karakitsos, and Berc Rustem. "Robust Capacity Planning Under Uncertainty." Management Science 37, no. 7 (July 1991): 787–800. http://dx.doi.org/10.1287/mnsc.37.7.787.

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19

Baier, Jorge A., and Javier A. Pinto. "Planning under uncertainty as GOLOGprograms." Journal of Experimental & Theoretical Artificial Intelligence 15, no. 4 (October 2003): 383–405. http://dx.doi.org/10.1080/0952813031000064567.

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20

Morrill, Nicola. "O.R. Support for planning under uncertainty." Impact 2021, no. 1 (January 2, 2021): 13–15. http://dx.doi.org/10.1080/2058802x.2021.1894793.

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21

Göx, Robert F. "Capacity Planning and Pricing under Uncertainty." Journal of Management Accounting Research 14, no. 1 (January 1, 2002): 59–78. http://dx.doi.org/10.2308/jmar.2002.14.1.59.

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This paper analyzes a capacity-planning and pricing problem of a monopolist facing uncertain demand. The model incorporates “soft” and “hard” capacity constraints (soft constraints can be relaxed at a cost while hard constraints cannot be relaxed) and demand uncertainty. The firm receives additional demand information within the plan-ning horizon. The solution to the planning problem depends crucially on what is known about demand at the time of the capacity decision as well as the pricing decision. Historical acquisition costs of capacity are relevant for pricing whenever the same information is available for capacity planning and pricing. However, when the firm re-ceives additional demand information before making the pricing decision, only marginal cost is relevant for pricing. Different types of capacity constraints, i.e., soft vs. hard, affect how much capacity the firm obtains, but not how the firm sets prices.

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22

Huang, G. H., B. W. Baetz, and G. G. Patry. "Trash-Flow Allocation: Planning Under Uncertainty." Interfaces 28, no. 6 (December 1998): 36–55. http://dx.doi.org/10.1287/inte.28.6.36.

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23

Lind, Hans, and Henry Muyingo. "Building maintenance strategies: planning under uncertainty." Property Management 30, no. 1 (February 3, 2012): 14–28. http://dx.doi.org/10.1108/02637471211198152.

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24

Liu, Ming Long, and Nikolaos V. Sahinidis. "Optimization in Process Planning under Uncertainty." Industrial & Engineering Chemistry Research 35, no. 11 (January 1996): 4154–65. http://dx.doi.org/10.1021/ie9504516.

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25

Dantzig, George B. "Planning under uncertainty using parallel computing." Annals of Operations Research 14, no. 1 (December 1988): 1–16. http://dx.doi.org/10.1007/bf02186471.

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26

Dantzig, George B., and Peter W. Glynn. "Parallel processors for planning under uncertainty." Annals of Operations Research 22, no. 1 (December 1990): 1–21. http://dx.doi.org/10.1007/bf02023045.

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27

Gorenstin, B. G., N. M. Campodonico, J. P. Costa, and M. V. F. Pereira. "Power system expansion planning under uncertainty." IEEE Transactions on Power Systems 8, no. 1 (1993): 129–36. http://dx.doi.org/10.1109/59.221258.

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28

Lasserre, J. B., and C. Mercé. "Robust hierarchical production planning under uncertainty." Annals of Operations Research 26, no. 1-4 (December 1990): 73–87. http://dx.doi.org/10.1007/bf02248585.

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29

Wu, Lilian Shiao-Yen. "Business Planning Under Uncertainty: Quantifying Variability." Statistician 37, no. 2 (1988): 141. http://dx.doi.org/10.2307/2348688.

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30

Gilles, Marc Aurèle, and Alexander Vladimirsky. "Evasive Path Planning Under Surveillance Uncertainty." Dynamic Games and Applications 10, no. 2 (October 21, 2019): 391–416. http://dx.doi.org/10.1007/s13235-019-00327-x.

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31

He, R., E. Brunskill, and N. Roy. "Efficient Planning under Uncertainty with Macro-actions." Journal of Artificial Intelligence Research 40 (March 1, 2011): 523–70. http://dx.doi.org/10.1613/jair.3171.

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Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions. We present a formal analysis of our approach, and examine its performance on two very large simulation experiments: scientific exploration and a target monitoring domain. We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multi-step lookahead is required to achieve good performance.

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32

Lessan, Javad, and Amy M. Kim. "Planning evacuation orders under evacuee compliance uncertainty." Safety Science 156 (December 2022): 105894. http://dx.doi.org/10.1016/j.ssci.2022.105894.

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33

Sharma, Apoorva, James Harrison, Matthew Tsao, and Marco Pavone. "Robust and Adaptive Planning under Model Uncertainty." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 410–18. http://dx.doi.org/10.1609/icaps.v29i1.3505.

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Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planning (RAMCP) algorithm, which allows computation of risk-sensitive Bayes-adaptive policies that optimally trade off exploration, exploitation, and robustness. RAMCP formulates the risk-sensitive planning problem as a two-player zero-sum game, in which an adversary perturbs the agent’s belief over the models. We introduce two versions of the RAMCP algorithm. The first, RAMCP-F, converges to an optimal risksensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed. The second version, RAMCP-I, improves computational efficiency at the cost of losing theoretical guarantees, but is shown to yield empirical results comparable to RAMCP-F. RAMCP is demonstrated on an n-pull multi-armed bandit problem, as well as a patient treatment scenario.

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34

Scharpff, Joris, Matthijs Spaan, Leentje Volker, and Mathijs De Weerdt. "Planning under Uncertainty for Coordinating Infrastructural Maintenance." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 425–33. http://dx.doi.org/10.1609/icaps.v23i1.13592.

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We address efficient planning of maintenance activities in infrastructural networks, inspired by the real-world problem of servicing a highway network. A road authority is responsible for the quality, throughput and maintenance costs of the network, while the actual maintenance is performed by autonomous, third-party contractors. From a (multi-agent) planning and scheduling perspective, many interesting challenges can be identified. First, planned maintenance activities might have an uncertain duration due to unexpected delays. Second, since maintenance activities influence the traffic flow in the network, careful coordination of the planned activities is required in order to minimise their impact on the network throughput. Third, as we are dealing with selfish agents in a private-values setting, the road authority faces an incentive-design problem to truthfully elicit agent costs, complicated by the fact that it needs to balance multiple objectives. The main contributions of this work are: 1) multi-agent coordination on a network level through a novel combination of planning under uncertainty and dynamic mechanism design, applied to real-world problems, 2) accurate modelling and solving of maintenance-planning problems and 3) empirical exploration of the complexities that arise in these problems. We introduce a formal model of the problem domain, present experimental insights and identify open challenges for both the planning and scheduling as well as the mechanism design communities.

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35

Wagner, Glenn, and Howie Choset. "Path Planning for Multiple Agents under Uncertainty." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 577–85. http://dx.doi.org/10.1609/icaps.v27i1.13866.

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Multi-agent systems in cluttered environments require path planning that not only prevents collisions with static obstacles, but also safely coordinates the motion of many agents. The challenge of multi-agent path finding becomes even more difficult when the agents experience uncertainty in their pose. In this work, we develop a multi-agent path planner that considers uncertainty, called uncertainty M* (UM*), which is based on a prior multi-agent path approach called M*. UM* plans a path through the belief space for each individual agent and then uses a strategy similar to M* that coordinates only agents that are “likely” to collide. This approach has the same scalability advantages as M*. We then introduce an extension called Permuted UM* (PUM*) that uses randomized restarts to enhance performance. We finish by presenting a belief space representation appropriate for multi-agent path planning with uncertainty and validate the performance of UM* and PUM* in simulation and mixed-reality experiments.

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36

He, Ruijie, Emma Brunskill, and Nicholas Roy. "PUMA: Planning Under Uncertainty with Macro-Actions." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1089–95. http://dx.doi.org/10.1609/aaai.v24i1.7749.

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Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporally-extended sequences of actions, or "macro-actions." In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an epsilon-optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperforms existing state-of-the art solvers.

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37

Samoylenko, V., A. Firsov, A. Pazderin, and P. Ilyushin. "Distribution Grid Future Planning Under Uncertainty Conditions." Renewable Energy and Power Quality Journal 19 (September 2021): 499–504. http://dx.doi.org/10.24084/repqj19.329.

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The paper presents an approach for making decisions about the future development of a distribution grid under uncertainty conditions. The levels of a grid hosting capacity and adequacy are examined using probabilistic approach compared to the conventional deterministic fit-andforget approach. It is shown that the probabilistic approach according to the 99 % confidence probability saves significant costs in comparison with the deterministic approach. The probabilistic calculations prove the use of an equipment rated capacity downsized by 2 points of a typical IEC scale, and in some cases to refuse the construction of a parallel circuit. The main contribution of the paper is a method for choosing an effective rated voltage of a distribution grid in a probabilistic interpretation based on the conventional formulas of Still, Zalessky and Illarionov. The technique includes obtaining the probability of loads location at different distances from power supply centre and the probability of load power distribution in a given range of values. It is shown that the calculation using the developed method makes possible to prefer grid rated voltage at least 1 point downsized by IEC scale with sufficient savings due to the difference in the equipment price compared with the deterministic fit-and-forget approach.

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38

Pollack-Johnson, Bruce, and Matthew J. Liberatore. "Project Planning under Uncertainty Using Scenario Analysis." Project Management Journal 36, no. 1 (March 2005): 15–26. http://dx.doi.org/10.1177/875697280503600103.

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An important component of risk management relates to project schedule uncertainty. To address this issue, a scenario (i.e., macro-level approach) for modeling and analyzing projects with significant uncertainty in their network structure and/or durations of some activities is presented. This approach requires that a set of project network scenarios is able to be identified, each with an assessed probability of occurrence. These scenarios might differ according to the results of uncertain events that could occur during the course of the project, uncertain activity durations (whether independent or dependent), finite loops where repeated activities can have different durations, or a combination of these. Advantages of our approach include the use of standard methods and software, as well as greater accessibility to, and likelihood of, the use of uncertainty analysis in project planning. Several examples are used to illustrate the suggested approach.

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39

Luo, Yuanfu, Haoyu Bai, David Hsu, and Wee Sun Lee. "Importance sampling for online planning under uncertainty." International Journal of Robotics Research 38, no. 2-3 (June 19, 2018): 162–81. http://dx.doi.org/10.1177/0278364918780322.

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The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT’s performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution.

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40

Modiano, Eduardo Marco. "Derived Demand and Capacity Planning Under Uncertainty." Operations Research 35, no. 2 (April 1987): 185–97. http://dx.doi.org/10.1287/opre.35.2.185.

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41

Marti, K. "Path planning for robots under stochastic uncertainty*." Optimization 45, no. 1-4 (January 1999): 163–95. http://dx.doi.org/10.1080/02331939908844432.

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42

Choobineh, F., and E. Mohebbi. "Material planning for production kits under uncertainty." Production Planning & Control 15, no. 1 (January 2004): 63–70. http://dx.doi.org/10.1080/09537280310001659164.

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43

Ichimura, Masakazu, and Junko Nakanishi. "Capacity planning of sewerage systems under uncertainty." International Journal of Environmental Studies 29, no. 4 (July 1987): 239–59. http://dx.doi.org/10.1080/00207238708710365.

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44

Masmoudi, Malek, Erwin W. Hans, and Alain Haït. "Tactical project planning under uncertainty: fuzzy approach." European J. of Industrial Engineering 10, no. 3 (2016): 301. http://dx.doi.org/10.1504/ejie.2016.076381.

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45

List, George F., Bryan Wood, Linda K. Nozick, Mark A. Turnquist, Dean A. Jones, Edwin A. Kjeldgaard, and Craig R. Lawton. "Robust optimization for fleet planning under uncertainty." Transportation Research Part E: Logistics and Transportation Review 39, no. 3 (May 2003): 209–27. http://dx.doi.org/10.1016/s1366-5545(02)00026-1.

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46

Dolgui, Alexandre, and Mohamed-Aly Ould-Louly. "Optimization of supply chain planning under uncertainty." IFAC Proceedings Volumes 33, no. 20 (July 2000): 303–7. http://dx.doi.org/10.1016/s1474-6670(17)38067-9.

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47

Linninger, Andreas A., Aninda Chakraborty, and Richard D. Colberg. "Planning of waste reduction strategies under uncertainty." Computers & Chemical Engineering 24, no. 2-7 (July 2000): 1043–48. http://dx.doi.org/10.1016/s0098-1354(00)00530-5.

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48

Taneja, P., B. E. Van Turnhout, and M. E. Aartsen. "Infrastructure planning under uncertainty: a case study." International Journal of Critical Infrastructures 8, no. 2/3 (2012): 134. http://dx.doi.org/10.1504/ijcis.2012.049033.

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49

Karabuk, S. "Production planning under uncertainty in textile manufacturing." Journal of the Operational Research Society 59, no. 4 (April 2008): 510–20. http://dx.doi.org/10.1057/palgrave.jors.2602370.

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50

Majercik, Stephen M., and Michael L. Littman. "Contingent planning under uncertainty via stochastic satisfiability." Artificial Intelligence 147, no. 1-2 (July 2003): 119–62. http://dx.doi.org/10.1016/s0004-3702(02)00379-x.

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