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An approximate dynamic programming Approach to Vehicle Platooning Coordination in Networks

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 16536-16547页

作者： Xiong, Xi Wang, Maonan Sun, Dengfeng Jin, Li NYU Tandon Sch Engn New York NY 10012 USA Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Shanghai Jiao Tong Univ UM Joint Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the availability of pre-determined plans, which is not feasible in real-time scenarios. In this paper, we address this issue in time-dependent networks by formulating a Markov decision process at each junction, aiming to minimize travel time and fuel consumption. Initially, we analyze coordinated platooning without routing to explore the cooperation among controllers on an identical path. We propose two novel approaches based on approximate dynamic programming, offering suboptimal control in the context of a stochastic finite horizon problem. The results demonstrate the superiority of the approximation in the policy space. Furthermore, we investigate platooning in a network setting, where speed profiles and routes are determined simultaneously. To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation. We subsequently employ the aforementioned policy approximation to determine speed profiles, considering essential parameters such as travel times. Our simulation results in SUMO indicate that our method yields better performance than conventional approaches, leading to potential travel cost savings of up to 38% . Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.

关键词： Junctions Fuels Routing dynamic programming Costs Roads Real-time systems Vehicle platooning multi-agent systems approximate dynamic programming approximate dynamic programming Bellman equation Bellman equation Routing dynamic programming Costs Roads Real-time systems Vehicle platooning multi-agent systems approximate dynamic programming Bellman equation

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An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期281卷 286-298页

作者： Voelkel, Michael A. Sachs, Anna-Lena Thonemann, Ulrich W. Univ Cologne Dept Supply Chain Management & Management Sci D-50923 Cologne Germany Univ Lancaster Dept Management Sci Lancaster England

A manufacturer places orders periodically for products that are shipped from a supplier. During transit, orders get damaged with some probability, that is, the order is subject to random yield. The manufacturer has the option to track orders to receive information on damages and to potentially place additional orders. Without tracking, the manufacturer identifies potential damages after the order has arrived. With tracking, the manufacturer is informed about the damage when it occurs and can respond to this information. We model the problem as a dynamic program with stochastic demand, tracking cost, and random yield. For small problem sizes, we provide an adjusted value iteration algorithm that finds the optimal solution. For moderate problem sizes, we propose a novel aggregation-based approximate dynamic programming (ADP) algorithm and provide solutions for instances for which it is not possible to obtain optimal solutions. For large problem sizes, we develop a heuristic that takes tracking costs into account. In a computational study, we analyze the performance of our approaches. We observe that our ADP algorithm achieves savings of up to 16% compared to existing heuristics. Our heuristic outperforms existing ones by up to 8.1%. We show that dynamic tracking reduces costs compared to tracking always or never and identify savings of up to 3.2%. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Inventory approximate dynamic programming Random yield Tracking Value of information

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An approximate dynamic programming method for the optimal control of Alkai-Surfactant-Polymer flooding

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JOURNAL OF PROCESS CONTROL 2018年 64卷 15-26页

作者： Ge, Yulei Li, Shurong Chan, Peng China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Peoples R China Beijing Univ Posts & Telecommun Automat Sch Beijing 100876 Peoples R China Jiangsu Automat Res Inst Lianyungang 222006 Peoples R China

Since the complexity, coupling, distributed parameter, etc. of alkali-surfactant-polymer (ASP) flooding, common optimization methods cannot acquire the optimal solutions well. This paper brings an optimal control method for ASP flooding based on approximate dynamic programming (ADP). At first, take the net present value (NPV) as the performance index. Then the Actor-Critic algorithm based on gradient descent method is adopted to get the optimal injection strategy, in which Actor and Critic are used to approximate the control and value function, respectively. To improve the approximation performance, the linear approximation basis function based on system characteristic is constructed. Furthermore, to train and predict the control and value function in next step, a temporal difference (TD) learning algorithm is introduced to update the weight coefficients. Then, the control in ADP is generated according to the Gauss function and its weight is updated according to the sigmoid function of TD error, so that the optimal control can be searched. At last, the enhanced oil recovery problem of ASP flooding with four injection wells and nine production wells is solved by the proposed method to test the effect of proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： ASP flooding approximate dynamic programming Actor-Critic algorithm Temporal difference learning algorithm TD error Linear basis function

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approximate dynamic programming for network recovery problems with stochastic demand

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 151卷 102358-102358页

作者： Ulusan, Aybike Ergun, Ozlem NE Univ Dept Mech & Ind Engn Boston MA 02115 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

Immediately after a disruption, in order to minimize the negative impact inflicted on the society, its imperative to re-establish the interdicted critical services enabled by the infrastructure networks. In this paper, we study the stochastic network recovery problem that tackles the planning of restoration activities (considering limited resources) on interdicted infrastructure network links so that the pre-disruption critical service flows can be re-established as quickly as possible. As an illustrative case study, we consider a disaster scenario on a road infrastructure network that obstructs the flow of relief-aid commodities and search-and-rescue teams between critical service providing facilities and locations in need of these critical services. As in the case of many realistic applications, we consider the amount of demand for critical services as stochastic. First, we present a Markov decision process (MDP) formulation for the stochastic road network recovery problem (SRNRP), then we propose an approximate dynamic programming (ADP) approach to heuristically solve SRNRP. We develop basis functions to capture the important complex network interactions that can be used to approximate cost-to-go values for the MDP states. We conduct computational experiments on a set of small-scale randomly generated instances and demonstrate that the ADP approach provides near-optimal results regardless of the demand distribution and network topology. In order to develop a practical approach suitable for solving real world sized instances, we propose a framework where we first develop an ADP model and derive a policy on a spatially aggregated network of large scale instance. Next, we show the performance of this policy through computational testing on the large scale disaggregated network. Moreover, we provide managerial insights by assessing the importance of each basis function in the ADP model contributing to the recovery policies. We test this approach on a case study based

关键词： Network recovery problem Stochastic networks Demand uncertainty Post-disaster response approximate dynamic programming

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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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An approximate dynamic programming Algorithm for Monotone Value Functions

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OPERATIONS RESEARCH 2015年第6期63卷 1489-1511页

作者： Jiang, Daniel R. Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08540 USA

Many sequential decision problems can be formulated as Markov decision processes (MDPs) where the optimal value function (or cost-to-go function) can be shown to satisfy a monotone structure in some or all of its dimensions. When the state space becomes large, traditional techniques, such as the backward dynamic programming algorithm (i. e., backward induction or value iteration), may no longer be effective in finding a solution within a reasonable time frame, and thus we are forced to consider other approaches, such as approximate dynamic programming (ADP). We propose a provably convergent ADP algorithm called Monotone-ADP that exploits the monotonicity of the value functions to increase the rate of convergence. In this paper, we describe a general finite-horizon problem setting where the optimal value function is monotone, present a convergence proof for Monotone-ADP under various technical assumptions, and show numerical results for three application domains: optimal stopping, energy storage/allocation, and glycemic control for diabetes patients. The empirical results indicate that by taking advantage of monotonicity, we can attain high quality solutions within a relatively small number of iterations, using up to two orders of magnitude less computation than is needed to compute the optimal solution exactly.

关键词： approximate dynamic programming monotonicity optimal stopping energy storage glycemic control

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Route-based approximate dynamic programming for dynamic pricing in attended home delivery

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期287卷 633-652页

作者： Koch, Sebastian Klein, Robert Univ Augsburg Chair Analyt & Optimizat Univ Str 16 D-86159 Augsburg Germany

Attended home delivery describes the delivery of goods by e-grocers or e-tailers to customers within an agreed time window. Because customers expect narrow time windows, offering such services may lead to expensive fulfillment operations. This has led to research on how to influence customers' bookings using time window pricing or slotting. In this paper, we reconsider the problem of demand management through dynamic pricing for attended home delivery services. This problem is usually modeled as a stochastic dynamic program, but even small instances cannot be solved to optimality due to the curses of dimensionality. The major challenges consist of finding feasible time windows for an incoming customer, estimating the opportunity costs, i.e., the future monetary loss due to accepting a booking, and optimizing the time window prices in real time. In this paper, we propose a route-based approximate dynamic programming approach to tackle these challenges. The approach carefully combines and partially extends state-of-the-art methods in attended home delivery, dynamic pricing, and dynamic vehicle routing. In an extensive simulation study, we compare its performance with state-of-the-art benchmark heuristics. The results indicate a superior performance of our approach in terms of both profit and number of customers served. (C) 2020 Elsevier B.V. All rights reserved.

关键词： E-commerce Attended home delivery Revenue management dynamic pricing approximate dynamic programming

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Production control in a complex production system using approximate dynamic programming

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第8期54卷 2419-2432页

作者： Wu, Han Evans, Gerald Bae, Ki-Hwan Univ Louisville Dept Ind Engn Louisville KY 40292 USA

Development of an efficient production and inventory control policy for a production system with multiple working stations, intermediate components and end products is difficult. In particular, uncertain demand and large changeover times at the work stations cause significant problems. In this paper, we consider an assembly line for dishwashers which require multiple types of wire racks that must be fabricated and coated at different work centres before supplying the assembly lines. An approximate dynamic programming (ADP) method is proposed to address the complexities associated with such a system. In addition, an Artificial Neural Network model is designed to approximate state values of the system, thus helping the system to make decisions at particular states. A near optimal production and inventory control policy is developed through an ADP algorithm. The proposed method can be extended to any similar system.

关键词： artificial neural network approximate dynamic programming production system inventory control

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Joint Admission Control and Routing Via approximate dynamic programming for Streaming Video Over Software-Defined Networking

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第3期19卷 619-631页

作者： Yang, Jian Zhu, Kunjie Ran, Yongyi Cai, Weizhe Yang, Enzhong Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

This paper considers the optimization problem of joint admission control and routing for the video streaming service in wired software-defined networking (SDN). With the aid of the network operating system, SDN is able to support the dynamic nature of future network functions and intelligent applications. Against this changing network landscape, we rely on Flow Visor-based virtualization in the context of OpenFlow-based wired SDN to design an open optimization architecture for the joint admission control and routing, which supports flexible and agile deployment of advanced joint admission control and routing strategies. Following this architecture, we interpret the joint admission control and routing problem into the Markov decision process for maximizing the overall " revenue." In order to solve the issue of the curses of dimensionality, we invoke the function approximation technique in the context of approximate dynamic programming to conceive an online learning framework. By applying kernel-based autonomous feature extraction into the function approximation, we develop an approximate dynamic programming-based joint admission control and routing for video streaming service, which is apt to be implemented in the proposed open architecture. An emulation platform based on Flow Visor, POX, and Mininet is constructed for demonstrating the success of the proposed solution. The experimental results are presented to show the performance improvement of the proposed scheme by comparing it with the Q-learning algorithm and open shortest path first-based benchmark scheme.

关键词： Admission control approximate dynamic programming routing algorithm software-defined networking (SDN) video streaming service

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An Optimal approximate dynamic programming Algorithm for the Lagged Asset Acquisition Problem

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MATHEMATICS OF OPERATIONS RESEARCH 2009年第1期34卷 210-237页

作者： Nascimento, Juliana M. Powell, Warren B. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

We consider a multistage asset acquisition problem where assets are purchased now, at a price that varies randomly over time, to be used to satisfy a random demand at a particular point in time in the future. We provide a rare proof of convergence for an approximate dynamic programming algorithm using pure exploitation, where the states we visit depend on the decisions produced by solving the approximate problem. The resulting algorithm does not require knowing the probability distribution of prices or demands, nor does it require any assumptions about its functional form. The algorithm and its proof rely on the fact that the true value function is a family of piecewise linear concave functions.

关键词： stochastic learning and adaptive control stochastic approximation approximate dynamic programming

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