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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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Explainable and Safety Aware Deep Reinforcement Learning-Based Control of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13556-13568页

作者： Farzanegan, Behzad Jagannathan, Sarangapani Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

This paper presents an explainable deep-reinforcement learning (DRL)-based safety-aware optimal adaptive tracking (SOAT) scheme for a class of nonlinear discrete-time (DT) affine systems subject to state inequality constraints. The DRL-based SOAT utilizes a multilayer neural network (MNN)-based actor-critic to estimate the cost function and optimal policy while the MNN update laws are tuned both using the singular value decomposition (SVD) of activation function gradient in order to mitigate the vanishing gradient issue and safety-aware Bellman error at each layer. An approximate safety-aware optimal policy is developed using Karush-Kuhn-Tucker (KKT) conditions by incorporating the higher-order control barrier function (HOCBF) into the Hamiltonian through the Lagrangian multiplier. The resulting safety-aware Bellman error helps with safe exploration both during online learning phase and at steady state without any explicit actor-critic MNN update law changes. To study the explainability and gain insights, we employ the Shapley Additive Explanations (SHAP) method to construct an explainer model for the DRL-based SOAT scheme in order to identify the important features in determining the optimal policy. The overall stability is established. Finally, the effectiveness of the proposed method is demonstrated on Shipboard Power Systems (SPS), achieving over a 35% reduction in cumulative cost compared to the existing actor-critic MNN optimal control policy. Note to Practitioners-In practical control systems, meeting safety constraints is often critical since ignoring constraints can lead to degraded performance or damage to equipment. This paper addresses the challenge of a safe DRL-based control approach that not only optimizes performance but also integrates robust safety assurances. Our DRL-based SOAT scheme specifically targets nonlinear discrete-time systems that must satisfy state inequality constraints. The successful proposed control performance in simulations on a

关键词： Safety Multi-layer neural network Optimal control Artificial neural networks Trajectory Cost function Tuning Computational modeling Training Steady-state Deep reinforcement learning approximate dynamic programming singular value decomposition-based weight tuning online safety-aware control explainability

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Sourcing strategies in supply risk management: An approximate dynamic programming approach

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COMPUTERS & OPERATIONS RESEARCH 2013年第5期40卷 1371-1382页

作者： Fang, Jiarui Zhao, Lei Fransoo, Jan C. Van Woensel, Tom Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Eindhoven Univ Technol Sch Ind Engn NL-5600 MB Eindhoven Netherlands

In recent years, supply chains have become increasingly globalized. As a consequence, the world's supply of all types of parts has become more susceptible to disruptions. Some of these disruptions are extreme and may have global implications. Our research is based on the supply risk management problem faced by a manufacturer. We model the problem as a dynamic program, design and implement approximate dynamic programming (ADP) algorithms to solve it, to overcome the well-known curses of dimensionality. Using numerical experiments, we compare the performance of different ADP algorithms. We then design a series of numerical experiments to study the performance of different sourcing strategies (single, dual, multiple, and contingent sourcing) under various settings, and to discover insights for supply risk management practice. The results show that, under a wide variety of settings, the addition of a third or more suppliers brings much less marginal benefits. Thus, managers can limit their options to a backup supplier (contingent sourcing) or an additional regular supplier (dual sourcing). Our results also show that, unless the backup supplier can supply with zero lead time, using dual sourcing appears to be preferable. Lastly, we demonstrate the capability of the proposed method in analyzing more complicated realistic supply chains. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Supply disruptions Recurrent risks Sourcing strategies approximate dynamic programming

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Intelligent Local Area Signals Based Damping of Power System Oscillations Using Virtual Generators and approximate dynamic programming

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IEEE TRANSACTIONS ON SMART GRID 2013年第1期4卷 498-508页

作者： Molina, Diogenes Venayagamoorthy, Ganesh Kumar Liang, Jiaqi Harley, Ronald G. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA ABB US Corp Res Ctr Raleigh NC 27606 USA Univ KwaZulu Natal Durban South Africa

This paper illustrates the development of an intelligent local area signals based controller for damping low-frequency oscillations in power systems. The controller is trained offline to perform well under a wide variety of power system operating points, allowing it to handle the complex, stochastic, and time-varying nature of power systems. Neural network based system identification eliminates the need to develop accurate models from first principles for control design, resulting in a methodology that is completely data driven. The virtual generator concept is used to generate simplified representations of the power system online using time-synchronized signals from phasor measurement units at generating stations within an area of the system. These representations improve scalability by reducing the complexity of the system "seen" by the controller and by allowing it to treat a group of several synchronous machines at distant locations from each other as a single unit for damping control purposes. A reinforcement learning mechanism for approximate dynamic programming allows the controller to approach optimality as it gains experience through interactions with simulations of the system. Results obtained on the 68-bus New England/New York benchmark system demonstrate the effectiveness of the method in damping low-frequency inter-area oscillations without additional control effort.

关键词： approximate dynamic programming generator coherency inter-area oscillations power system equivalents power system stabilizer virtual generator

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Incorporating network considerations into pavement management systems: A case for approximate dynamic programming

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2013年 33卷 134-150页

作者： Medury, Aditya Madanat, Samer Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA

The objective of infrastructure management is to provide optimal maintenance, rehabilitation and replacement (MR&R) policies for a system of facilities over a planning horizon. While most approaches in the literature have studied the decision-making process as a finite resource allocation problem, the impact of construction activities on the road network is often not accounted for. The state-of-the-art Markov decision process (MDP)-based optimization approaches in infrastructure management, while optimal for solving budget allocation problems, become internally inconsistent upon introducing network constraints. In comparison, approximate dynamic programming (ADP) enables solving complex problem formulations by using simulation techniques and lower dimension value function approximations. In this paper, an ADP framework is proposed, wherein capacity losses due to construction activities are subjected to an agency-defined network capacity threshold. A parametric study is conducted on a stylized network configuration to infer the impact of network-based constraints on the decision-making process. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Infrastructure management Markov decision process approximate dynamic programming

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An approximate dynamic programming method for multi-input multi-output nonlinear system

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OPTIMAL CONTROL APPLICATIONS & METHODS 2013年第1期34卷 80-95页

作者： Huang, Zhijian Ma, Jie Huang, He Shanghai Jiao Tong Univ Ocean Engn State Key Lab Shanghai 200240 Peoples R China Shanghai Maritime Univ Merchant Marine Coll Shanghai 200135 Peoples R China IMS Automot Elect Syst Co Ltd Shanghai 200335 Peoples R China

approximate dynamic programming is a useful tool in solving multi-stage decision optimal control problems. In this work, we first promote the action-dependent heuristic dynamic programming method to multi-input multi-output control system by extending its action network to multi-output form. The detailed derivation is also given. We then apply this method to the fluctuation control of a spark engine idle speed. An engine idling model is set up to verify the control effect of this method. Results here show that this method requires several iterations to suppress unbalanced combustion by manipulating spark ignition timing. This method provides an alternative for a simpler multi-input multi-output approximate dynamic programming scheme. Moreover, it has a faster iteration convergence effect. The derivation of this method also has a rigorous mathematical basis. Although illustrated for engines, this control system framework should also be applicable to general multi-input multi-output nonlinear system. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： approximate dynamic programming adaptive critic designs fluctuation control of an engine idling neural network multi-input multi-output system

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Solving a Real-Life Stochastic Car Batching and Sequencing Problem With dynamic programming Approaches

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5012-5028页

作者： Liu, Ran Zhang, Yue Zhou, Bo Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China

This paper addresses a real-life stochastic car batching and sequencing problem in the body shop of a vehicle plant. Unlike previous research on similar problems in the paint shop or assembly shop, our problem primarily focuses on production planning in the body shop, with specific constraints on the production sequence (e.g., the bodies of each model cannot be produced individually but must be produced in batches of a specific size) and considers the impact of sampling inspection. Solving this large-scale car batching and sequencing problem in the body shop within an acceptable computation time is challenging. In this paper, several efficient dynamic programming-based algorithms are designed to solve the problem. First, a dynamic programming model is established for the deterministic version of the problem, and the optimal solution can be obtained by the dynamic programming approach. Furthermore, faced with the uncertainty introduced by sampling inspection, a more difficult and practical stochastic car batching and sequencing problem is modeled as a discrete-time Markov decision process. A rollout method-based approximate dynamic programming algorithm is designed to solve this complex problem. Finally, the proposed algorithms' effectiveness is examined using real-life production data. Note to Practitioners-This paper is motivated by our collaboration with a vehicle production plant in Shanghai, China. The plant mainly produces 3 similar to 4 electrified models, with an annual output of up to 300,000 units/year. The plant consists of three workshops: the body shop, the paint shop, and the assembly shop. A multi-model flexible production line has been set up in the body shop. In practice, as multiple models are produced on the production line simultaneously, the plant adopts a mode of batch production to batch and sequence the bodies to be produced. Due to the specialty of the batch production mode and the uncertainty brought by the sampling inspection, the stochasti

关键词： Production Sequential analysis Automobiles Assembly Paints Heuristic algorithms Switches Car batching and sequencing problem body shop sampling inspection approximate dynamic programming

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Markov-Decision-Process-Based Reinforcement Learning for Emergency Vehicles of Dispatch and Reallocation Strategies through the Lens of Efficiency and Fairness: Evidence from Chinese Emergency Medical Services System

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TRANSPORTATION RESEARCH RECORD 2025年第4期2679卷 1015-1030页

作者： Luan, Siliang Pan, Fuquan Chen, Xiufeng Bing, Qichun Yang, Jinshun Jin, Xin Qingdao Univ Technol Sch Civil Engn Qingdao Peoples R China Minist Educ Engn Res Ctr Concrete Technol Marine Environm Qingdao Peoples R China

Efficient proactive emergency vehicle planning is crucial for effective emergency response management systems. This paper develops a flexible deep reinforcement learning framework to address uncertainties in dynamic emergency requests and complex traffic conditions. The Markov decision process model is proposed to optimize ambulance dispatch and reallocation, focusing on minimizing average response time, reducing delays from traffic congestion and patient severity misclassification, and enhancing general fairness. To tackle the challenges of the unbounded MDP state space, we employ a least-squares-based approximate policy iteration model for the upper bound and a linear programming model for the lower bound. We use Chinese emergency medical services system data for computational experiments and select average response time, fraction of delay, risk level, and Gini coefficient to evaluate the performance of our model. The numerical results demonstrate our optimal flexible policy outperforms the first-in-first-served rule, improving both efficiency and equity in medical decision-making.

关键词： emergency medical service emergency vehicle operations Markov decision process dispatch and relocation approximate dynamic programming

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Reinforcement Learning for Process Control: Review and Benchmark Problems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第1期23卷 1-40页

作者： Park, Joonsoo Jung, Hyein Kim, Jong Woo Lee, Jong Min Seoul Natl Univ Sch Chem & Biol Engn 1 Gwanak ro Seoul 08826 South Korea Incheon Natl Univ Dept Energy & Chem Engn Innovat Ctr Chem Engn 119 Acad Ro Incheon 22012 South Korea

The success of reinforcement learning (RL) combined with deep neural networks has led to the development of numerous RL algorithms that have demonstrated remarkable performance across various domains. However, despite its potential in process control, relatively limited research has explored RL in this field. This paper aims to bridge the gap between RL and process control, providing potential applications and insights for process control engineers. In this review, we first summarize previous efforts to apply RL to process control. Next, we provide an overview of RL concepts and categorize recent RL algorithms, analyzing the strengths and weaknesses of each category. We implement fourteen RL algorithms and apply them to six relevant benchmark environments, conducting quantitative analyses to identify the most suitable approaches for specific process control problems. Finally, we draw conclusions and outline future research directions to advance RL's application in process control.

关键词： approximate dynamic programming optimal control process control reinforcement learning

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dynamic container slot allocation with empty container repositioning under stochastic demand

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 187卷

作者： Liang, Jinpeng Ma, Zhongyuan Wang, Shuang Liu, Haitao Tan, Zhijia Dalian Maritime Univ Sch Transportat Engn Dalian 116026 Peoples R China Sichuan Univ Business Sch Chengdu 610065 Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

Container slot allocation represents a critical operational decision -making challenge within the liner shipping industry, which necessitates making decisions on the transportation of loaded containers and the repositioning of empty ones under stochastic demand. We study novel dynamic allocation policy that leverage sequentially -revealed demand information to determine the slot allocation decision for both loaded and empty containers at each stage. In this paper, we develop a stochastic dynamic programming (DP) model to optimize the slot allocation decision for maximizing the expected total revenue over the planning horizon. To solve this model, we design an efficient allocation policy that makes slot allocations at each stage based on current empty container stocks, realized demand, and the mean demand at future stages. Comprehensive numerical experiments on both synthetic and realistic data demonstrate substantial revenue improvement of our approach over the commonly -used benchmark policies in practice and literature.

关键词： Container slot allocation Empty container repositioning Stochastic demand Stochastic dynamic programming approximate dynamic programming

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