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Scheduling EV Charging Having Demand With Different Reliability Constraints

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 11018-11029页

作者： Shao, Shiping Sartipizadeh, Hossein Gupta, Abhishek Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ford Motor Co Dearborn MI 48126 USA

We develop a new algorithm, based on approximate dynamic programming, to efficiently schedule the charging processes of a large number of electric vehicles (EVs) with flexibility, which allows the EV charging service provider to charge the EVs between their minimum and target state of charge (SoC) within a time window. We assume that the charging demands have known distributions and the charging process of EVs can be scheduled preemptively. We model the scheduling problem as a dynamic program with state-dependent constraints and solve it utilizing the fitted value iteration. We propose a multi-stage decoupled algorithm to reduce the dimensions in the fitted value iteration to reduce the computation complexity, and provide sufficient conditions for achieving optimality.

关键词： dynamic scheduling Electric vehicle charging Heuristic algorithms Costs Schedules Approximation algorithms Reliability EV charging approximate dynamic programming fitted value iteration

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Approximation Benefits of Policy Gradient Methods with Aggregated States

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MANAGEMENT SCIENCE 2023年第11期69卷 6898-6911页

作者： Russo, Daniel Columbia Univ Grad Sch Business New York NY 10027 USA

Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper studies the case of state-aggregated representations, in which the state space is partitioned and either the policy or value function approximation is held constant over partitions. This paper shows a policy gradient method converges to a policy whose regret per period is bounded by epsilon, the largest difference between two elements of the state-action value function belonging to a common partition. With the same representation, both approximate policy iteration and approximate value iteration can produce policies whose per-period regret scales as epsilon/(1-gamma), where. is a discount factor. Faced with inherent approximation error, methods that locally optimize the true decision objective can be far more robust.

关键词： reinforcement learning approximate dynamic programming policy gradient methods state aggregation

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Solving large-scale dynamic vehicle routing problems with stochastic requests

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期306卷 596-614页

作者： Zhang, Jian Luo, Kelin Florio, Alexandre M. Van Woensel, Tom South China Univ Technol Dept Elect Business Guangzhou 510006 Peoples R China Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands Univ Bonn Inst Comp Sci D-53115 Bonn Germany Eindhoven Univ Technol Dept Math & Comp Sci NL-5600 MB Eindhoven Netherlands

dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to ap-proximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knap-sack with a capacity given by the remaining service time available along the vehicle's route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed meth-ods in prescribing high-quality offline route plans and online scheduling decisions. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Routing Markov decision processes Column generation Stochastic requests approximate dynamic programming

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Capacity planning in a decentralized autologous cell therapy manufacturing network for low-cost resilience

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2023年第2期35卷 295-319页

作者： Li, Junxuan White, Chelsea C. Microsoft Corp Redmond WA 98052 USA Georgia Inst Technol Atlanta GA 30332 USA

The goals for increased patient access and fast fulfillment have motivated considerable interest in autologous cell therapy manufacturing networks having multiple and geographically distributed manufacturing facilities. However, the cost of safety manufacturing capacity to mitigate supplier disruption risk-a significant risk in the emerging cell manufacturing industry-would be lower if manufacturing is centralized. In this paper, we analyze a decentralized network that has as its objective to minimize the cost of network resilience for mitigating supplier disruption by making use of the fact that bioreactors for autologous therapy manufacturing are small enough to be relocatable. We model this problem as a Markov decision process and develop efficient algorithms that are based on real-time demand data to minimize safety manufacturing capacity and determine how relocatable capacity should be distributed while satisfying resilience constraints. In case studies, based in part on data collected from a Chimeric antigen receptor T cell therapy manufacturing facility at the University of Pennsylvania, we compare decentralized network models with different heuristic algorithms. Results indicate that transshipment in a decentralized network can result in a significant reduction of required safety capacity, reducing the cost of network resilience.

关键词： Autologous cell therapy dynamic resilience Decentralized manufacturing netwrok Capacity planning approximate dynamic programming

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Overbooking with Bounded Loss

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MATHEMATICS OF OPERATIONS RESEARCH 2023年第3期48卷 1344-1363页

作者： Freund, Daniel Zhao, Jiayu (Kamessi) MIT Sloan Sch Management Cambridge MA 02142 USA MIT Operat Res Ctr Cambridge MA 02142 USA

We study a classic problem in revenue management: quantity-based, single resource revenue management with no-shows. In this problem, a firm observes a sequence of T customers requesting a service. Each arrival is drawn independently from a known distribution of k different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources B and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than B, the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, that is, its expected loss is inde omega(root T) pendent of T. This improves upon prior works achieving omega(T) guarantees.

关键词： online stochastic decision making approximate dynamic programming revenue management overbooking capacity management

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GOPS:A general optimal control problem solver for autonomous driving and industrial control applications

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Communications in Transportation Research 2023年第1期3卷 92-106页

作者： Wenxuan Wang Yuhang Zhang Jiaxin Gao Yuxuan Jiang Yujie Yang Zhilong Zheng Wenjun Zou Jie Li Congsheng Zhang Wenhan Cao Genjin Xie Jingliang Duan Shengbo Eben Li School of Vehicle and Mobility Tsinghua UniversityBeijing100084China

Solving optimal control problems serves as the basic demand of industrial control *** methods like model predictive control often suffer from heavy online computational *** learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible,high-accuracy *** Reinforcement learning(RL)solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming ***,many of them only support Python-based environments and limit to model-free *** address this gap,this paper develops General Optimal control Problems Solver(GOPS),an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial *** is built with a highly modular structure that retains a flexible framework for secondary *** the diversity of industrial control tasks,GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction,controller design,and performance *** handle large-scale problems,GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and *** offers a variety of common approximate functions for policy and value functions,including polynomial,multilayer perceptron,convolutional neural network,***,constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into *** examples,including linear quadratic control,inverted double pendulum,vehicle tracking,humanoid robot,obstacle avoidance,and active suspension control,are tested to verify the performances of GOPS.

关键词： Industrial control Reinforcement learning approximate dynamic programming Optimal control Neural network Benchmark

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dynamic surgery management under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期309卷 832-844页

作者： Gokalp, E. Gulpinar, N. Doan, V. X. Univ Bath Sch Management Bath BA2 7AY England Univ Warwick Warwick Business Sch Coventry CV4 7AL England

Real-time surgery management involves a complex and dynamic decision-making process. The duration of surgeries in many cases cannot be known until the surgery has actually been completed. Furthermore, disruptions such as equipment failure or the arrival of a non-elective surgery can occur simultaneously. Thus, the assignment of surgeries needs to be updated, as and when disruptions occur, to minimize their effects. In this paper, we present a stochastic dynamic programming approach to the surgery allocation problem with multiple operating rooms under uncertainty. Given an elective list for the day, the dy-namic optimization model minimizes the number of surgeries not carried out by the end of the shift and the total waiting times of patients during the day weighted according to their urgency level. Due to the curse of dimensionality, we apply an approximate dynamic programming algorithm to solve the stochastic dynamic surgery management model. Computational experiments are designed to demonstrate the performance of the proposed algorithm and its applicability to practical settings. The results show that the approximate dynamic programming algorithm provides a good approximation to the optimum policy and leads to some managerial insights. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Reactive scheduling Uncertainty modelling Surgery management approximate dynamic programming

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Optimal Tracking Control for Ship Course Using approximate dynamic programming Method

Optimal Tracking Control for Ship Course Using Approximate D...

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第三十二届中国控制会议

作者： XIE Qingqing LUO Bin TAN Fuxiao School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing ＆ Signal Processing Ministry of EducationAnhui University School of Computer and Information Fuyang Teachers college

ISBN: (纸本)9781479900305

dynamic programming(DP) is not a useful tool for solving many control problems because of its complexity in computation. In this paper,we propose approximate dynamic programming(ADP) optimal control strategy for ship course trajectory tracking control *** system transformation,we convert the optimal tracking problem into designing a infinite-horizon optimal regulator for the tracking error ***-dependent Heuristic dynamic programming(ADHDP) technique,as one form of ADR is presented to obtain the infinite-horizon optimal tracking *** the ship course optimal tracking control simulation results,we can see that the ADHDP controller makes the performance index and the control sequence for the error dynamics converge to the optimal *** BP neural networks are used as parametric structures to implement ADHDP *** two neural networks aim at approximating the cost function and the control law,respectively.

关键词： Optimal Tracking Control approximate dynamic programming ADHDP Ship Course

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approximate dynamic programming Based Controller Design Using an Improved Learning Algorithm with Application to Tracking Control of Aircraft

Approximate Dynamic Programming Based Controller Design Usin...

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2013年中国智能自动化学术会议

作者： Xiong Luo Yuchao Zhou Zengqi Sun School of Computer and Communication Engineering University of Science and Technology Beijing(USTB) Beijing Key Laboratory of Knowledge Engineering for Materials Science Key Laboratory of Knowledge Engineering for Materials Science Department of Computer Science and Technology Tsinghua University

The strategy using approximate/adaptive dynamic programming(ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent *** paper aims at handling an important problem in the design of ADP learning controllers,which is the improvement of learning algorithm for its convergence *** analyze ADP controller implementation framework according to the requirement of tracking control task,with emphasis on providing an improved weight-updating gradient descent approach in optimizing connection weights in network structures.A comparison of the proposed method and classic ADP design for tracking and controlling pitch angle of aircraft is *** verifies the feasibility in the design of the proposed ADP based controller.

关键词： approximate dynamic programming Controller Learning algorithm Aircraft control

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Microgrid Energy Management based on approximate dynamic programming

Microgrid Energy Management based on Approximate Dynamic Pro...

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4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

作者： Strelec, Martin Berka, Jan Honeywell Spol Sro Honeywell Prague Lab Prague Czech Republic

ISBN: (纸本)9781479929849

Microgrid energy management stands for challenging optimization problem where continuous (economic dispatch) and discrete optimization (unit commitment) tasks are solved. Often Microgrid optimization leads to complex problem where optimization methods usually meet curse of dimensionality. We adopt approximate dynamic programming (ADP) as the promising optimization technique which can overcome curse of dimensionality. In this paper, energy management system based on ADP is introduced and its behavior is demonstrated on small scale Microgrid which is connected to distribution network and includes wind turbine, chiller plant, thermal storage and cooling load. The paper describes policy search approach to ADP and selected approximation architectures in the context of energy optimization. The ADP results are compared with the results of the solution based on dynamic programming approach.

关键词： Microgrid energy management approximate dynamic programming

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