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Understanding the Value of Fulfillment Flexibility in an Online Retailing Environment

M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2023年第2期25卷 391-408页

作者： DeValve, Levi Wei, Yehua Wu, Di Yuan, Rong Univ Chicago Booth Sch Business Chicago IL 60637 USA Duke Univ Fuqua Sch Business Durham NC 27708 USA JD Com Amer Technol Corp Mountain View CA 94043 USA Stitch Fix San Francisco CA 94104 USA

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks.

关键词： supply chain management e-commerce OM practice asymptotic analysis approximate dynamic programming

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dynamic resource levelling in projects under uncertainty

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第1期61卷 198-218页

作者： Li, Hongbo Zhang, Xianchao Sun, Jinshuai Dong, Xuebing Shanghai Univ Sch Management Shangda Rd 99 Shanghai 200114 Peoples R China China Acad Elect & Informat Technol Beijing Peoples R China Tianjin Univ Commerce Sch Accountancy Tianjin Peoples R China

In the resource levelling problem (RLP) under uncertainty, existing studies focus on obtaining an open-loop Bellman activity list that is not updated during project execution. In project management practice, it is also necessary to address more situations, such as activity overlaps and resource breakdowns. In this paper, we extend the uncertain RLP by proposing a resource levelling problem with multiple uncertainties (RLP-MU) that simultaneously considers uncertainties in activity durations, activity overlaps and resource availabilities. We formulate the RLP-MU as a Markov decision process model. Aimed at levelling resource usage by dynamically scheduling activities at each decision point based on the observed information, we develop a hybrid open-closed-loop approximate dynamic programming algorithm (HOC-ADP). In the HOC-ADP, we devise a closed-loop rollout policy to approximate the cost-to-go function and use the concept of the average project to avoid time-consuming simulation. A greedy-decoding-based estimation of distributed algorithm is also devised to construct an open-loop policy that is embedded in the HOC-ADP to further improve it. We additionally develop a simulation algorithm to evaluate the resource levelling performance of the HOC-ADP. Computational experiments on a benchmark dataset consisting of 540 problem instances are conducted to analyze the performance of the HOC-ADP, and the impact of various factors on resource levelling are investigated. The comparison experimental results indicate that our HOC-ADP outperforms the state-of-the-art meta-heuristics.

关键词： approximate dynamic programming uncertain project scheduling resource levelling activity overlaps resource breakdowns

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dynamic demand management and online tour planning for same-day delivery

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期307卷 860-886页

作者： Klein, Vienna Steinhardt, Claudius Univ Bundeswehr Munich Chair Business Analyt & Management Sci Munich Germany

For providers to stay competitive in a context of continued growth in e-retail sales and increasing cus-tomer expectations, same-day delivery options have become very important. Typically, with same-day delivery, customers purchase online and expect to receive their ordered goods within a narrow deliv-ery time span. Providers thus experience substantial operational challenges to run profitable tours and generate sufficiently high contribution margins to cover overhead costs. We address these challenges by combining a demand-management approach with an online tour-planning approach for same-day deliv-ery. More precisely, in order to reserve capacity for high-value customer orders and to guide customer choices toward efficient delivery operations, we propose a demand-management approach that explicitly optimizes the combination of delivery spans and prices which are presented to each incoming customer request. The approach includes an anticipatory sample-scenario based value approximation, which in-corporates a direct online tour-planning heuristic. It does not require extensive offline learning and is scalable to realistically sized instances with multiple vehicles. In a comprehensive computational study, we show that our anticipatory approach can improve the contribution margin by up to 50% compared to a myopic benchmark approach. We also show that solving an explicit pricing optimization problem is a beneficial component of our approach. More precisely, it outperforms both a pure availability control and a simple pricing rule based on opportunity costs. The latter idea is one used in other approaches for related dynamic pricing problems dealt with in the literature. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Logistics Same -day delivery Last -Mile logistics Demand management approximate dynamic programming

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Scheduling EV Charging Having Demand With Different Reliability Constraints

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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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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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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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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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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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The Exponomial Choice Model for Assortment Optimization: An Alternative to the MNL Model?

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MANAGEMENT SCIENCE 2023年第5期69卷 2814-2832页

作者： Aouad, Ali Feldman, Jacob Segev, Danny London Business Sch London NW1 4SA England Washington Univ Olin Business Sch St Louis MO 63130 USA Tel Aviv Univ Dept Stat & Operat Res IL-69978 Tel Aviv Israel

In this paper, we consider the yet-uncharted assortment optimization problem under the exponomial choice model, in which the objective is to determine the revenue-maximizing set of products that should be offered to customers. Ourmain algorithmic contribution comes in the form of a fully polynomial-time approximation scheme, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. We synthesize several ideas related to approximate dynamic programming, intended to construct a compact discretization of the continuous state space by keeping track of "key statistics" in rounded form and by operating with a suitable bit precision complexity. We complement this result by a number of NP-hardness reductions to natural extensions of this problem. Moreover, we conduct empirical and computational evaluations of the exponomial choice model and our solution method. Focusing on choice models with a simple parametric structure, we provide new empirical evidence that the exponomial choice model can achieve higher predictive accuracy than the multinomial logit (MNL) choice model on several real-world data sets. We uncover that this predictive performance correlates with certain characteristics of the choice instance-namely, the entropy and magnitude of choice probabilities. Finally, we leverage fully ranked preference data to simulate the expected revenue of optimal assortments prescribed using the fitted exponomial and MNL models. On semisynthetic data, the exponomial-based approach can lift revenues by 3%-4% on average against the corresponding MNL benchmark.

关键词： assortment optimization FPTAS approximate dynamic programming case study

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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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