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Offline-Online approximate dynamic programming for dynamic Vehicle Routing with Stochastic Requests

TRANSPORTATION SCIENCE 2019年第1期53卷 185-202页

作者： Ulmer, Marlin W. Goodson, Justin C. Mattfeld, Dirk C. Hennig, Marco Tech Univ Carolo Wilhelmina Braunschweig D-38106 Braunschweig Germany St Louis Univ Richard A Chaifetz Sch Business St Louis MO 63103 USA

Although increasing amounts of transaction data make it possible to characterize uncertainties surrounding customer service requests, few methods integrate predictive tools with prescriptive optimization procedures to meet growing demand for small-volume urban transport services. We incorporate temporal and spatial anticipation of service requests into approximate dynamic programming (ADP) procedures to yield dynamic routing policies for the single-vehicle routing problem with stochastic service requests, an important problem in city-based logistics. We contribute to the routing literature as well as to the field of ADP. We combine offline value function approximation (VFA) with online rollout algorithms resulting in a high-quality, computationally tractable policy. Our offline-online policy enhances the anticipation of the VFA policy, yielding spatial and temporal anticipation of requests and routing developments. Our combination of VFA with rollout algorithms demonstrates the potential benefit of using offline and online methods in tandem as a hybrid ADP procedure, making possible higher-quality policies with reduced computational requirements for real-time decision making. Finally, we identify a policy improvement guarantee applicable to VFA-based rollout algorithms, showing that base policies composed of deterministic decision rules lead to rollout policies with performance at least as strong as that of their base policy.

关键词： dynamic vehicle routing stochastic customer requests approximate dynamic programming

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An approximate dynamic programming approach for sequential pig marketing decisions at herd level

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第3期276卷 1056-1070页

作者： Pourmoayed, Reza Nielsen, Lars Relund Aarhus Univ Dept Econ & Business Econ Fuglesangs Alle 4 DK-8210 Aarhus V Denmark

One of the most important operations in the production of growing/finishing pigs is the marketing of pigs for slaughter. While pork production can be managed at different levels (animal, pen, section, or herd), it is beneficial to consider the herd level when determining the optimal marketing policy due to inter-dependencies, such as those created by fixed transportation costs and cross-level constraints. In this paper, we consider sequential marketing decisions at herd level. A high-dimensional infinite horizon Markov decision process (MDP) is formulated which, due to the curse of dimensionality, cannot be solved using standard MDP optimization techniques. Instead, approximate dynamic programming (ADP) is applied to solve the model and find the best marketing policy at herd level. Under the total expected discounted reward criterion, the proposed ADP approach is first compared with a standard solution algorithm for solving an MDP at pen level to show the accuracy of the solution procedure. Next, numerical experiments at herd level are given to confirm how the marketing policy adapts itself to varying costs (e.g., transportation cost) and cross-level constraints. Finally, a sensitivity analysis for some parameters in the model is conducted and the marketing policy found by ADP is compared with other well-known marketing polices, often applied at herd level. (C) 2019 Elsevier B.V. All rights reserved.

关键词： OR in agriculture approximate dynamic programming Markov decision process Herd management Stochastic dynamic programming

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Scheduling elective surgeries with Markov decision process and approximate dynamic programming

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IFAC-PapersOnLine 2019年第13期52卷 1831-1836页

作者： Zhang, Jian Dridi, Mahjoub Moudni, Abdellah El Laboratoire de Nanomédecine Imagerie et Thérapeutique Université Bourgogne Franche-Comté UTBM rue Thierry Mieg Belfort cedex90010 France

This paper deals with the dynamic advance scheduling of elective surgeries with multiple sources of uncertainties taken into consideration. A waiting list is established to facilitate the management of elective patients from different specialties. Each patient in the waiting list is assigned a dynamic priority which is dependent on the relative importance of specialty, urgency level, and actual waiting time. At the end of each week, the number and type of elective surgeries to be performed in the following week should be properly determined to minimize an integrated cost function, including the costs incurred by performing and delaying surgeries as well as the penalties for overuse of operating rooms and shortage of recovery beds. The studied problem is formulated as an infinite-horizon Markov decision process (MDP) model. Considering that conventional dynamic programming algorithms cannot efficiently solve MDP models for real-sized problems, we develop an approximate dynamic programming (ADP) approach that combines recursive least-squares temporal difference learning and mixed integer programming. Results of numerical experiments validate the efficiency and accuracy of the proposed ADP approach and indicate that this approach can be employed by hospital managers in the future to efficiently solve real-sized surgery scheduling problems. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： dynamic programming Cost functions Integer programming Markov processes Scheduling Surgery approximate dynamic programming dynamic programming algorithm Elective surgeries Markov Decision Processes Mixed integer programming Numerical experiments Recursive least square (RLS) Scheduling problem

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Demand side energy management of EV charging stations by approximate dynamic programming

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ENERGY CONVERSION AND MANAGEMENT 2019年 196卷 878-890页

作者： Wu, Yu Ravey, Alexandre Chrenko, Daniela Miraoui, Abdellatif Univ Bourgogne Franche Comte UTBM CNRS FEMTO ST Inst Belfort France Univ Bourgogne Franche Comte UTBM CNRS FCLAB Belfort France

The high operation cost of the EV charging station (EVCS) is a severe challenge for the development of electric vehicles, which lead to the general shortage of the EVCS. In order to reduce the operation costs of the EVCS, an approximate dynamic programming (ADP) based energy management system (EMS) is proposed for the EVCS equipped with multiple types of chargers (EVCS-MTC). A fuzzy logic guiding system has been designed to allocate each vehicle an appropriate charging spot based on its charging urgency. Multiple EVs can acquire the charging service through a common charger in the EVCS-MTC. In the proposed EMS, the approximate dynamic programming (ADP) and the evolution algorithm (EA) are combined to determine the optimal charging start time for each EV. This characteristic provides the charging device with the maximum autonomy to select the preferred flexible charging pattern, which can prolong the battery lifetime and reduce the communication requirements of the control system. With taking the dynamic electricity price and uncertain future charging demand into account, the proposed EMS can achieve a total cost reduction of over 50% compared with the conventional charging scheme in the numerical studies.

关键词： EV charging station Demand side energy management approximate dynamic programming Flexible charging pattern Multiple types of EV charger

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A data-driven approximate dynamic programming approach based on association rule learning: Spacecraft autonomy as a case study

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INFORMATION SCIENCES 2019年 504卷 501-519页

作者： D'Angelo, Gianni Tipaldi, Massimo Palmieri, Francesco Glielmo, Luigi Univ Salerno Dept Comp Sci Salerno Italy Univ Sannio Dept Engn Benevento Italy

dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for formulating, modeling, and solving decision making problems under uncertainty. In real-world applications, the applicability of DP is limited by severe scalability issues. These issues can be addressed by approximate dynamic programming (ADP) techniques. ADP methods are based on the assumption of having either a proper estimation of the underlying state transition probability distributions or a simulation mechanism with the capability of generating samples according to such probability distributions. In this paper, we present a data-driven ADP-based approach, which can offer an alternative in case such assumption cannot be guaranteed. In particular, when varying the set-up of the MDP state transition probability matrix, different policies can be calculated through exact DP or ADP methods. Such policies are then processed by an Apriori-based algorithm to find frequent association rules within them. A pruning procedure is used to select the most suitable association rules, and finally an Association Classifier infers the optimal policy in all the possible circumstances. We show a detailed application of the proposed approach for the calculation of a proper mission operations plan for spacecrafts with a high level of on-board autonomy. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Stochastic optimal control Markov decision process approximate dynamic programming Association rules Association classifier Apriori classifier Spacecraft autonomy

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A new strategy based on approximate dynamic programming to maximize the net revenue of IaaS cloud providers with limited resources

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 1168-1186页

作者： Yue, Wenying Du, Peng Quan, Xiongwen Chen, Ying Hebei Univ Coll Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intelli Baoding 071002 Peoples R China Tianjin Univ Technol Sch Management Tianjin Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China China Mobile Online Serv Co LTD Zhejiang Branch Hangzhou Zhejiang Peoples R China

With rapid development of cloud computing, more conventional Internet Data Centers get involved into this business model. Providers of small and medium sized data centers have relatively limited computing resources compared to global IaaS providers such as Amazon. The demand for computing at these service providers may be above the available capacity especially during peak shopping periods (e.g. mother's day or singles' day). However, it is undesirable to blindly expand capacity, which will not only incur the purchase cost of new machines but also the maintenance costs associated with resource over-provisioning. For these service providers, the most important thing is how to effectively manage resources during peak demand periods to maximize their net revenue. In this context, we focus on the joint admission control and virtual machine placement problem, and propose a dynamic optimization model considering that customers dynamically create and terminate virtual machines in cloud computing environment. In terms of problem complexity, the developed dynamic optimization model typically suffers from "curse of dimensionality" and is computationally intractable. So we resort to approximate dynamic programming framework and propose a new strategy to yield a tractable model for real-time decision-making. Extensive simulations are conducted to compare our proposed strategy with other strategies including existing methods of virtual machine placement. The simulation results demonstrate that our customized strategy achieves substantial revenue improvements and the improvement is more significant as resource provisioning is tighter. In addition, it can be scalable to solve large cases up to 1000 physical machines and yield a real-time decision in a reasonable time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cloud computing Net revenue maximization Admission control Virtual machine placement approximate dynamic programming

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A LINEAR programming METHODOLOGY FOR approximate dynamic programming

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2020年第2期30卷 363-375页

作者： Diaz, Henry Sala, Antonio Armesto, Leopoldo Univ Politecn Valencia Univ Inst Ind Control Syst & Comp Camino Vera S-N Valencia 46022 Spain Univ Politecn Valencia Design & Mfg Inst Camino Vera S-N Valencia 46022 Spain

The linear programming (LP) approach to solve the Bellman equation in dynamic programming is a well-known option for finite state and input spaces to obtain an exact solution. However, with function approximation or continuous state spaces, refinements are necessary. This paper presents a methodology to make approximate dynamic programming via LP work in practical control applications with continuous state and input spaces. There are some guidelines on data and regressor choices needed to obtain meaningful and well-conditioned value function estimates. The work discusses the introduction of terminal ingredients and computation of lower and upper bounds of the value function. An experimental inverted-pendulum application will be used to illustrate the proposal and carry out a suitable comparative analysis with alternative options in the literature.

关键词： linear programming approximate dynamic programming control applications neural networks

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Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 116卷 105542-000页

作者： Pan, Z. N. Yu, T. Chen, L. P. Yang, B. Wang, B. Guo, W. X. South China Univ Technol Coll Elect Power Guangzhou 510640 Guangdong Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China Guangdong Power Grid Co Ltd Elect Dispatch & Control Ctr Guangzhou 510600 Guangdong Peoples R China

This paper studies the real-time charging optimization (RTCO) of large-scale electric vehicles (EVs), which is a multistage and multidimensional stochastic resource allocation problem. In order to handle the complex RTCO of large-scale EVs, a multidimensional approximate dynamic programming (ADP-RTCO) is designed for the sequent optimal decision makings. The hierarchy of ADP-RTCO contains two layers. In the upper layer, RTCO of large-scale EVs is formulated as a multidimensional energy storage problem by classifying EVs into several virtual EV clusters (EVCs). Then, temporal difference learning based policy iteration method is used to obtain the value function approximation for each EVC. In the lower layer, priority based reallocation algorithm is employed to obtain the detailed charging power for connected EVs. ADP-RTCO is designed with on-line learning ability to enhance its adaptability and robustness to ambiguous environment. Comprehensive simulation results demonstrate the optimality and robustness of ADP-RTCO in both cost-saving and load-flattening problems. Furthermore, ADP-RTCO can obtain higher-quality solution compared with other algorithms and is applicable for RTCO of large-scale EVs under uncertainties due to its high computation efficiency.

关键词： Electric vehicle Charging optimization Real-time optimization Stochastic optimization approximate dynamic programming

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Risk-averse real-time dispatch of integrated electricity and heat system using a modified approximate dynamic programming approach

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ENERGY 2020年 198卷 117347-000页

作者： Pan, Zhenning Yu, Tao Li, Jie Qu, Kaiping Yang, Bo South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China

Coordinated operation of integrated electricity and heat system can improve operation flexibility and reduce cost. However, multiple uncertainties challenge its optimal operation. This paper aims at developing a risk-averse and computationally efficient policy for real-time stochastic dispatch of integrated electricity and heat system, which improves the economy as well as avoiding the risk of high costs in critical scenarios. First, real-time dispatch of integrated electricity and heat system is formulated as a multistage risk-averse stochastic sequential optimization problem with dynamic risk measure, where combined heat and power unit, energy storage, flexible electricity and heat load are jointly utilized to minimize the risk-adjusted total costs. Next, a risk-averse dynamic programming formulation of the original problem is presented, upon which a data-driven risk-averse approximate dynamic programming is employed to address computational challenge, and develop almost optimal and computationally efficient policy. By exploiting information from training samples in off-line learning, the proposed algorithm can efficiently responses to the stochastic exogenous information. Comparative simulations with different risk-aversion preferences and different methods verify the effectiveness of the proposed algorithm. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Integrated electricity and heat system Real-time optimization Risk-averse optimization Stochastic optimization approximate dynamic programming

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An approximate dynamic programming approach for comparing firing policies in a networked air defense environment

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COMPUTERS & OPERATIONS RESEARCH 2020年第0期117卷 104890-000页

作者： Summers, Daniel S. Robbins, Matthew J. Lunday, Brian J. Air Force Inst Technol Dept Operat Sci Wright Patterson AFB OH 45433 USA

An objective for effective air defense is to identify the firing policy for interceptor allocation to incoming missiles that minimizes the expected total damage to defended assets over a sequence of engagements. We formulate this dynamic weapon target assignment problem as a Markov decision process and utilize a simulation-based, approximate dynamic programming (ADP) approach to solve problem instances based on a representative scenario. Least squares policy evaluation and least squares temporal differences algorithms are developed to determine approximate solutions. A designed experiment investigates problem features such as conflict duration, attacker and defender weapon sophistication, and defended asset values. An empirical comparison of the ADP policies and two baseline policies (i.e., firing either one or two interceptors at each incoming theater ballistic missile (TBM)) yields several insights: the ADP policies outperform both baseline polices when conflict duration is short and attacker weapons are sophisticated;firing one interceptor at each TBM (regardless of inventory status) outperforms the tested ADP policies when conflict duration is long and attacker weapons are less sophisticated;and firing two interceptors at each TBM (regardless of inventory status), which is the United States Army's currently implemented policy, is never the superlative policy for the test instances investigated. Published by Elsevier Ltd.

关键词： Military Air and missile defense dynamic weapon target assignment problem Markov decision processes approximate dynamic programming

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