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Horizontal combinations of online and offline approximate dynamic programming for stochastic dynamic vehicle routing

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2020年第1期28卷 279-308页

作者： Ulmer, Marlin W. Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

Stochastic and dynamic vehicle routing problems gain increasing attention in the research community. In these problems, routing plans are dynamically updated based on realizations of stochastic information. Due to the complexity of the corresponding Markov decision processes (MDPs), the calculation of optimal policies for these problems is usually not possible and researchers draw on heuristical methods of approximate dynamic programming (ADP). These methods use simulation to approximate the value of a state and decision in the MDP. The simulations are either conducted offline or online. Offline methods such as value function approximations (VFAs) generally neglect the full detail of the state space due to aggregation. Online methods such as rollout algorithms (RAs) are often not able to capture decision and transition space sufficiently due to runtime limitations. In this paper, we alleviate this tradeoff by combining two methods of ADP, an online RA and an offline VFA in two ways. In addition to the integration of the VFA as a base policy into the online RA to strengthen the RA's simulations, we also limit the RA's simulation horizon, estimating the remaining reward-to-go again via the VFA. For two stochastic dynamic routing problems from the literature, we show how this combination outperforms state-of-the-art solutions while simultaneously reducing the required time for online calculations.

关键词： Approximate dynamic programming rollout algorithm Value function approximation Dynamic vehicle routing Stochastic requests Multi-period

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Dynamic programming and suboptimal control: A survey from ADP to MPC

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EUROPEAN JOURNAL OF CONTROL 2005年第4-5期11卷 310-334页

作者： Bertsekas, DP MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

We survey some recent research directions within the field of approximate dynamic programming, with a particular emphasis on rollout algorithms and model predictive control (MPC). We argue that while they are motivated by different concerns, these two methodologies are closely connected, and the mathematical essence of their desirable properties (cost improvement and stability, respectively) is couched on the central dynamic programming idea of policy iteration. In particular, among other things, we show that the most common MPC schemes can be viewed as rollout algorithms and are related to policy iteration methods. Furthermore, we embed rollout and MPC within a new unifying suboptimal control framework, based on a concept of restricted or constrained structure policies, which contains these schemes as special cases.

关键词： dynamic programming stochastic optimal control model predictive control rollout algorithm

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Energy management of airport service electric vehicles to match renewable generation through rollout approach

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Wei, Renjie Zhou, Fei Wang, Yishen State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing Peoples R China

Traditional diesel-based airport service vehicles are characterized by a heavy-duty, high-usage-frequency nature and a high carbon intensity per vehicle per hour. Transforming these vehicles into electric vehicles would reduce CO2 emissions and potentially save energy costs in the context of rising fuel prices, if a proper energy management of airport service electric vehicles (ASEVs) is performed. To perform such an energy management, this paper proposes a new customized rollout approach, as an optimal control method for a new ASEV dynamics model, which models the ASEV states, their transitions over time, and how control decisions affect them. The rollout approach yields an optimal control strategy for the ASEVs to transport luggage and to charge batteries, with the objective to minimize the operation cost, which incentivizes the charging of the ASEVs to match renewable generation. Case studies demonstrate that the rollout approach effectively overcomes the "curse of dimensionality" challenge. The rollout algorithm results in a total cost approximately 10 % less than that of the underlying "greedy charging" heuristic, which charges a battery whenever its state of charge is not the maximum. The rollout algorithm is proven to be adaptive towards flight schedule changes at short notice.

关键词： Airport service electric vehicle Electric vehicle Energy management Heuristic control Optimal control rollout algorithm

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Study on Fault Diagnostic Strategy under Unreliable Test 35

Study on Fault Diagnostic Strategy under Unreliable Test

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第35届中国控制会议

作者： DONG Haidi HE Bing LIU Gang HE Huafeng QI Haozhen JI Yiwei Department of Space Engineering High-Tech Institute of Xi'an Department of Automation High-Tech Institute of Xi'an

ISBN: (纸本)9781509009107

A new diagnostic strategy for complex system under unreliable test is ***,a system mean detection cost function is established to evaluate the test cost and misdiagnosis price at the same *** a rollout algorithm based on information entropy theory is proposed,which takes an average detection cost as the heuristic evaluation *** case and engineering example verify the proposed *** show that the proposed algorithm meets the requirements for high search speed and acceptable test cost in engineering application.

关键词： diagnostic strategy unreliable test information entropy rollout algorithm

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Dynamic programming and suboptimal control: A survey from ADP to MPC

Dynamic programming and suboptimal control: A survey from AD...

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44th IEEE Conference on Decision Control/European Control Conference (CCD-ECC)

作者： Bertsekas, DP MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

关键词： dynamic programming stochastic optimal control model predictive control rollout algorithm

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SURVEILLANCE VERSUS RECONNAISSANCE: AN ENTROPY BASED MODEL

SURVEILLANCE VERSUS RECONNAISSANCE: AN ENTROPY BASED MODEL

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作者： Tamilyn S. Dismukes Air University

学位级别：硕士

With the advancing capabilities of Intelligence, Surveillance, and Reconnaissance (ISR) assets and sensors, effective utilization of these resources continues to pose a challenge to military decision makers. The methodology developed explores allocation of ISR assets while balancing detection of new targets versus surveillance of already detected targets (discovery vs. persistence) using entropy as a measure of effectiveness. Scenarios with an unknown number of static and moving targets in a bounded geographical region are considered. A baseline model was built to examine four different search algorithms: random, raster, greedy, and a rollout algorithm based on dynamic programming. A space-filling Nearly Orthogonal Latin Hypercube (NOLH) experimental design was applied to generate data to examine four Measures of Effectiveness (MOEs): step entropy, average entropy, number of targets found, and time steps to completion. Based on statistical analysis and time series plots, the rollout algorithm's performance dominated others algorithms considered for all MOEs. In addition to minimizing uncertainty in the first 100 time steps of the run, the rollout algorithm also produced the highest number of targets found within the fixed time step scenario, and, for the exhaustive target detection scenario, discovered all of the targets within the region in less time steps. Based on these results, the rollout algorithm provides superior performance in the allocation of ISR assets while balancing detection of new targets versus surveillance of already detected targets.

关键词： Intelligence Surveillance Reconnaissance (ISR) Entropy Uncertainty rollout algorithm

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Optimal dynamic empirical therapy in a health care facility: A Monte-Carlo look-ahead method

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 198卷 105767-105767页

作者： Houy, Nicolas Flaig, Julien Univ Lyon F-69007 Lyon France CNRS GATE Lyon St Etienne F-69130 Ecully France

Background and objectives: Empirical antimicrobial prescription strategies have been proposed to counteract the selection of resistant pathogenic strains. The respective merits of such strategies have been debated. Rather than comparing a finite number of policies, we take an optimization approach and propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. Methods: We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a rollout algorithm. Results: In our example, the optimal policy computed with this method allows to reduce the average cumulative infected patient-days over two years by 22% compared to the best standard therapy. Considering regularity constraints, we could derive a policy with a fixed period and a performance close to that of the optimal policy. The average cumulative infected patient-days over two years obtained with the optimal policy is 6% lower (significantly at the 95% threshold) than that obtained with the fixed period policy. Conclusion: Our results illustrate the performance of a highly flexible solution method that will contribute to the development of implementable empirical therapy policies. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Health care associated infection Antimicrobial resistance Antibiotic cycling Empirical therapy rollout algorithm Artificial intelligence Monte-Carlo methods

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Approximate dynamic programming methods in Bayesian preference elicitation

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QUALITY ENGINEERING 2024年

作者： Fisher, William Zhang, Qiong Song, Yongjia Clemson Univ Sch Math & Stat Sci Clemson SC 29634 USA Clemson Univ Dept Ind Engn Clemson SC USA

Bayesian preference elicitation is a statistical framework which can aid in complex decision making processes such as engineering design by offering preference-informed recommendations to a decision maker (DM). In this framework, a DM is asked to answer a sequence of queries, called a questionnaire, where in each query they must select their preferred option among two alternatives. A common method to construct the questionnaire is through the D-optimality criterion, which seeks to select queries which lead to more precise estimates of utility model parameters. Past works have proposed and investigated adaptive methods for questionnaire construction using D-optimality as the query selection criterion. All of these adaptive methods have been based on greedy strategies to the best of our knowledge. In this work, we propose and compare various non-greedy methods for adaptive questionnaire construction in the preference elicitation setting. Our methods utilize a combination of enumeration and mixed integer programming techniques to select queries to present to the DM. Through our simulation studies, we find only a marginal improvement in D-efficiency through the use of non-greedy methods, suggesting that greedy methods are sufficient for this problem.

关键词： Choice design D-error mixed integer programming rollout algorithm

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New closed-loop approximate dynamic programming for solving stochastic decentralized multi-project scheduling problem with resource transfers

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115593-115593页

作者： Zhao, Song Xu, Zhe Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China

Resource transfers and uncertain activity durations are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized multi-project scheduling problem with both resource transfers and uncertain activity durations, we name it the stochastic decentralized multi-project scheduling problem with resource transfers (SDRCMPSPTT). To tackle the curse-of-dimensionality of an exact solution approach, we develop a new and effective rollout policy-based approximate dynamic programming (ADP) algorithm for SDRCMPSPTT. Based on the benchmark instances, we build a new data set and examine the performance of 12 priority rule heuristics, then select the two best ones as the base policy for our rollout algorithm. The proposed approach is verified under a stochastic environment by assessing different base heuristic policies, computational results show that the rollout algorithm can further improve the solution quality of the base heuristics. Compared with the state-of-the-art algorithm for solving the decentralized multi project scheduling problem under a deterministic environment, our rollout policy-based ADP algorithm can also obtain competitive solutions.

关键词： Decentralized multi-project scheduling Resource transfers Uncertain activity durations Approximate dynamic programming rollout algorithm

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Stochastic optimal control methodologies in risk-informed community resilience planning

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STRUCTURAL SAFETY 2020年 84卷 101920-101920页

作者： Nozhati, Saeed Ellingwood, Bruce R. Chong, Edwin K. P. Univ Calif Los Angeles B John Garrick Inst Risk Sci Los Angeles CA 90095 USA Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA

The absorptive and restorative abilities of a community are two key elements of community resilience following disasters. The recovery of communities relies on an efficient restoration planning of damaged critical infrastructure systems, household units, and impaired supporting social and economic functions. These interdependent systems form a dynamic system of systems that changes continuously during restoration. Therefore, an effective and practical recovery planning process for a community can be modeled as a sequential dynamic optimization problem under uncertainty. This paper seeks to enhance our understanding of dynamic optimization concepts and their role in formulating post-disaster, community-level recovery strategies. Various methods of classic dynamic programming and reinforcement learning are examined and applied. Simulation-based approximate dynamic programming techniques are introduced to overcome the curse of dimensionality, which is characteristic of large-scale and multi-state systems of systems. The paper aims not only to study the unexplored topic of dynamic optimization in community resilience, but also to be a practical reference for policymakers, practitioners, engineers, and operations analysts to harness the power of dynamic optimization toward assessing and achieving community resilience.

关键词： Community resilience Dynamic programming Optimal control Reinforcement learning rollout algorithm Stochastic optimization

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