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Empirical dynamic programming for model-free ecosystem-based management

METHODS IN ECOLOGY AND EVOLUTION 2024年第4期15卷 769-778页

作者： Munch, Stephan B. Brias, Antoine NOAA Natl Marine Fisheries Serv Southwest Fisheries Sci Ctr Santa Cruz CA 95060 USA

Quantitative ecosystem-based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best-studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near-optimal management policies can be constructed from time-series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical dynamic programming approach performs well in cases we examined and outperformed a commonly used single-species alternative. We expect model-free ecosystem-based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.

关键词： approximate dynamic programming ecosystem management Gaussian process regression nonlinear methods temporal difference learning time-delay embedding

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Real-time dispatch management of shared autonomous vehicles with on-demand and pre-booked requests

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2024年 181卷

作者： Chen, Yao Liu, Yang Baia, Yun Mao, Baohua Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol Co Beijing Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

Autonomous vehicle technology is poised to revolutionize shared vehicle systems, offering the potential for increased efficiency and convenience. To better devise management strategies for shared autonomous vehicles, this paper addresses a real-time dispatch problem with hybrid requests, where on -demand (immediate) and pre -booked (reserved) trip requests coexist. The coexistence of these two types of request behaviors introduces considerable complexity to realtime dispatch due to the uncertainty in trip demand. We design an approximate dynamic programming (ADP) approach for making vehicle-trip assignments and vehicle relocation decisions. We first formulate the real-time vehicle dispatch problem as a dynamic program and decompose it into time -staged subproblems. To effectively handle the high -dimensional state space, we replace the value functions with tractable approximations and propose a piecewiselinear functional approximation method that captures the spatiotemporal value of vehicles. To calibrate the parameters in the approximations, we propose DualT and DualNext algorithms to provide precise dual information, thereby enhancing the accuracy of our approach. Furthermore, we propose a lookahead strategy that incorporates pre -booked request information into the ADP approach for improving real-time decision -making. We validate the effectiveness of the ADP approach through numerical experiments conducted using taxi data from Brooklyn, New York. The ADP approach outperforms benchmark policies in solution quality while maintaining computational efficiency, and the incorporation of the lookahead strategy significantly enhances the performance of the ADP approach, yielding substantial improvements. Numerical results demonstrate that integrating pre -booked requests into vehicle dispatch management can greatly enhance the system efficiency.

关键词： Shared autonomous vehicles dynamic fleet management approximate dynamic programming Vehicle dispatch Reservation Lookahead

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Constrained-Cost Adaptive dynamic programming for Optimal Control of Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 3251-3264页

作者： Wei, Qinglai Li, Tao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

For discrete-time nonlinear systems, this research is concerned with optimal control problems (OCPs) with constrained cost, and a novel value iteration with constrained cost (VICC) method is developed to solve the optimal control law with the constrained cost functions. The VICC method is initialized through a value function constructed by a feasible control law. It is proven that the iterative value function is nonincreasing and converges to the solution of the Bellman equation with constrained cost. The feasibility of the iterative control law is proven. The method to find the initial feasible control law is given. Implementation using neural networks (NNs) is introduced, and the convergence is proven by considering the approximation error. Finally, the property of the present VICC method is shown by two simulation examples.

关键词： Costs Cost function Performance analysis Optimal control Nonlinear systems dynamic programming Convergence Adaptive dynamic programming (ADP) approximate dynamic programming constrained cost optimal control reinforcement learning value iteration (VI)

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Learning-based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第4期34卷 2365-2383页

作者： Li, Ning Wang, Hongbin Luo, Qianda Zheng, Wei Yanshan Univ Coll Elect Engn Qinhuangdao Peoples R China Yanshan Univ Coll Elect Engn Qinhuangdao 066004 Hebei Peoples R China

This paper investigates the robust H-infinity optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two-player zero-sum differential game theory, the H-infinity optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton-JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust H-infinity optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios.

关键词： approximate dynamic programming collision avoidance control quadrotor UAVs robust H-infinity control zero-sum differential game

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approximate optimal and scalable control for collision-free formation of uncertain nonholonomic robots

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NONLINEAR dynamicS 2024年第24期112卷 22145-22160页

作者： Guo, Yaohua Huang, He Xu, Jingwen Northwestern Polytech Univ Sch Astronaut West Youyi Rd Xian 710000 Shaanxi Peoples R China

This paper studies the formation tracking problem for nonholonomic multirobot systems under model uncertainties. A new local variable is designed to transform the collision-free formation control objective into an optimization problem through integrating several filtered signals into tracking errors. The optimal control policies for nominal kinematic model are learned by approximate dynamic programming (ADP) technique using a simplified critic-only neural network (NN) based algorithm, which scales well since the computational complexity is independent with the number of robots and obstacles. Then, to handle the effects from robots' uncertainties, the ADP control policies are redesigned by adding a two time-scale based compensator. It is shown that under proper conditions, the NN weights' estimation errors are uniformly ultimately bounded, while the robust formation tracking and collision avoidance can be achieved. Numerical simulations finally illustrate the effectiveness of the proposed controller.

关键词： Nonholonomic multirobot systems Model uncertainties approximate dynamic programming Collision-free formation

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State Aggregation and Lower Bound-Based ADP Approach for dynamic Pick-Up Routing Problem With Capacity Constraint

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 9932-9944页

作者： Wu, Yu Jian, Ming Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China

The significant surge in e-commerce sales has made parcel delivery services exceedingly popular. In this paper, we investigate a dynamic parcel pick-up problem where a vehicle with limited capacity collects customers' parcels on a closed tour. We consider stochastic demand, stochastic service requests, and stochastic service cancellations simultaneously. Aimed at minimizing the total travel distance of the vehicle, we formulate the problem as a Markov decision process and jointly apply state aggregation and approximate dynamic programming (ADP) to overcome the curse of dimensionality in solution solving. First, we introduce the concept of aggregation granularity, which refers to the number of states that are combined into a single common vector. Based on the cardinalities of the post-decision state's component sets, we propose a state aggregation method that reduces the size of the state searching space from exponential to quartic. By utilizing this aggregation, our routing policy is represented implicitly by state value functions that are stored in a lookup table in memory. Second, we develop a novel and well-performing ADP-based routing policy that differs from the general ADP-based policy. We derive a lower bound of the post-decision state value function and use it in policy development. Third, during online execution, we apply a rollout algorithm with the lower bound-based policy as its base policy, resulting in an online rollout policy. Finally, through numerical analysis, we examine the impact of aggregation granularity and the advantages of the online rollout policy.

关键词： Routing Vehicle dynamics Costs Uncertainty Monitoring Decision making Logistics Parcel pick-up approximate dynamic programming vehicle routing aggregation granularity curse of dimensionality

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A dynamic and Stochastic Cumulative Capacitated Vehicle Routing Problem

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2025年第2期42卷

作者： Wu, Yu Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Natl United Engn Lab Integrated & Intelligent Tran Chengdu Peoples R China Natl Engn Lab Big Data Applicat Integrated Transpo Chengdu Peoples R China

The cumulative capacitated vehicle routing problem (CCVRP) is a variant of the classical capacitated vehicle routing problem in which the objective is the minimization of customers' total waiting time prior to service. Inspired by the first-mile pick-up service within 3rd-Party Logistics operations, we study a dynamic and stochastic variant of the CCVRP. In this variant, a capacitated vehicle undertakes a single tour to collect customers' parcels, taking into account stochastic service requests, stochastic service cancellations, and stochastic demand (i.e., changes in parcel size) simultaneously. Conditional on maximizing the usage of the vehicle's capacity, we minimize the total waiting time of both customers who are served and those who request services but are not served due to cancellation or capacity violation. We formulate this problem as a Markov decision process and solve it using a hybrid approach that combines approximate dynamic programming and rollout algorithm. Our main contributions include (i) the construction of the stage cost that leverages a property of the Poisson process, and (ii) consideration of service fairness-based acceptance/rejection rule in response to excessive stochastic service requests. In the numerical study, our approach is examined to outperform an 2-Opt-based re-optimization policy in terms of both customers' waiting time and service-request satisfying rate. More importantly, computational results heuristically show that there is no cost to adopting the service fairness-based rule, which is obviously a valuable managerial insight.

关键词： Capacitated vehicle routing problem parcel pick-up service fairness approximate dynamic programming

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The dynamic Freight Routing Problem for Less-Than-Truckload Carriers

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TRANSPORTATION SCIENCE 2023年第3期57卷 717-740页

作者： Baubaid, Ahmad Boland, Natashia Savelsbergh, Martin King Fahd Univ Petr & Minerals Ind & Syst Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

Less-than-truckload (LTL) carriers transport freight shipments from origins to destinations by consolidating freight using a network of terminals. As daily freight quantities are uncertain, carriers dynamically decide freight routes on the day of operations. We introduce the dynamic freight routing problem (DFRP) and model this problem as a Markov decision process (MDP). To overcome the curses of dimensionality of the MDP model, we introduce an approximate dynamic programming (ADP) solution approach that uses a lookup table to store value function approximations and present and compares a number of aggregation approaches that use features of the postdecision states (PDSs) to aggregate the PDS space and reduce the number of entries in the lookup table. Furthermore, because the decision subproblems are integer programs (IPs), we present a framework for integrating lookup tables into the decision subproblem IPs. This framework consists of (1) a modeling approach for the integration of lookup table value function approximations into subproblem IPs to form extended subproblem IPs;(2) a solution approach, PDS-IP-bounding, which decomposes the extended subproblem IPs into many smaller IPs and uses dynamic bounds to reduce the number of small IPs that have to be solved;and (3) an adaptation of the e-greedy exploration-exploitation algorithm for the IP setting. Our computational experiments show that despite the DFRP having high-dimensional PDS vectors, a two-dimensional aggregation of the space can produce policies that outperform standard myopic policies. Moreover, they demonstrate that the PDS-IP-bounding algorithm provides computational advantages over solving the extended subproblem IPs using a commercial solver.

关键词： less-than-truckload freight transportation approximate dynamic programming value function approximation load planning

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Unlocking the value in product return data: Inventory management with sales dependent stochastic product return flows from multiple periods

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 285卷

作者： Gokbayrak, Esra Kayis, Enis Gullu, Refik Ozyegin Univ Dept Ind Engn Nisantepe MahOrman Sok TR-34794 Istanbul Turkiye Bogazici Univ Dept Ind Engn TR-34342 Istanbul Turkiye

In the fast fashion retail sector, handling product returns has become a significant challenge due to rapidly changing consumer preferences and high product return rates. These retailers are now inclined to consider product return flows in managing product inventories using detailed product return data. This study investigates an optimal inventory control policy for a retailer facing stochastic product returns from multiple previous sale periods to maximize expected profit during a single selling season. The problem is formulated using dynamic programming, and due to its computational complexity, we propose an approximate dynamic programming value iteration algorithm using basis functions. Our proposed algorithm reduces the solution time drastically without a significant sacrifice from optimality. We quantify the value of leveraging detailed return information and demonstrate that our proposed model increases the retailer's profit by 9% in the base case and up to 31% considering other cases compared to a model ignoring such information, especially under decreasing product prices over time or per period order capacity constraints. Finally, using an extensive computational study, we propose managerial insights on how to best leverage the value in the product return data using advanced analytics for fast-fashion retailers.

关键词： Inventory management Stochastic product returns approximate dynamic programming Retail operations

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MADP-Based Explicit Online Operation Policy for Robust Energy Management of ADN

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IEEE TRANSACTIONS ON SMART GRID 2024年第4期15卷 3795-3807页

作者： Chen, Jiajun Zhu, Jianquan Liu, Wenhao Fang, Langsen Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

This paper proposes a multiparametric approximate dynamic programming (MADP)-based explicit online operation policy for the robust energy management problem of active distribution network (ADN). First, the online energy management problem is described from the perspective of distributionally robust optimization (DRO). Then, the risk-averse concept is introduced to quantitatively control the conservativeness of DRO. Considering that it is a complex online optimal problem, which is intractable for traditional algorithms, an MADP-based explicit online operation policy is proposed in this paper. Specifically, the approximate dynamic programming (ADP) algorithm is improved based on the multiparametric programming theory, according to which an explicit online operation policy can be further derived. Compared to traditional online decision-making policies, which need to execute an optimization program, the proposed policy is able to directly obtain solutions through an explicit expression. In this way, the calculation efficiency can be enhanced significantly. Cases on several test systems validate the effectiveness of the proposed method.

关键词： Optimization Heuristic algorithms Uncertainty Mathematical models programming Energy management dynamic programming approximate dynamic programming multiparametric programming distributionally robust optimization energy management active distribution network

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