Operations Research Perspectives最新文献

The interplay between learning effect and order acceptance in production planning 生产计划中学习效应与订单接受的相互作用

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-07-15 DOI: 10.1016/j.orp.2025.100350

Kuo-Ching Ying , Pourya Pourhejazy , Wei-Jie Zhou

{"title":"The interplay between learning effect and order acceptance in production planning","authors":"Kuo-Ching Ying , Pourya Pourhejazy , Wei-Jie Zhou","doi":"10.1016/j.orp.2025.100350","DOIUrl":"10.1016/j.orp.2025.100350","url":null,"abstract":"<div><div>Learning takes time and hence its effects should be considered in short-term production planning (i.e., scheduling). This is especially true when human involvement is high and the shop floor experiences changes in workflow, workforce, or technology. The Single-Machine Scheduling Problem (SMSP) with the learning effect is considered to explore this interplay. The study first proves that the shortest processing time scheduling rule can solve the mathematical problems. Pseudo-polynomial solution algorithms based on Dynamic Programming (DP) are developed to solve the SMSPs with learning effects and job rejection to minimize the maximum completion time (makespan), total completion time, and total tardiness, separately. We found that the algorithms tend to reject a small number of orders with longer production times and retain more of those with shorter production times when the objective is to minimize the average response time for the new orders. This is contrary to situations when the system’s resource utilization or the delays in fulfilling demand are sought to be minimized. The study also found that orders requiring longer processing times should be scheduled later to improve all three performance metrics with higher learning rates. Finally, we establish that all three extended problems are solvable in pseudo-polynomial time, with complexities of <span><math><mrow><mi>O</mi><mo>(</mo><mrow><msup><mi>n</mi><mn>2</mn></msup><mi>E</mi></mrow><mo>)</mo></mrow></math></span> for makespan and total completion time minimization, and <span><math><mrow><mi>O</mi><mo>(</mo><mrow><msup><mi>n</mi><mn>2</mn></msup><mi>P</mi><mi>E</mi></mrow><mo>)</mo></mrow></math></span> for total tardiness minimization. The DP algorithms efficiently solve practical-sized instances, as validated by numerical experiments.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100350"},"PeriodicalIF":3.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data 不确定性下的智能家居经济运行：使用高斯和基于kde的数据比较蒙特卡罗和随机优化

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-07-13 DOI: 10.1016/j.orp.2025.100348

Spyros Giannelos, Danny Pudjianto, Goran Strbac

{"title":"Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data","authors":"Spyros Giannelos, Danny Pudjianto, Goran Strbac","doi":"10.1016/j.orp.2025.100348","DOIUrl":"10.1016/j.orp.2025.100348","url":null,"abstract":"<div><div>This paper investigates optimal day-ahead operation of a building-scale energy hub equipped with photovoltaics and a battery. Electricity demand and PV availability are uncertain and are represented in two ways: (i) thin-tailed normal distributions and (ii) kernel density estimation (KDE) fitted to empirical CityLearn data. For each representation we evaluate (a) deterministic Monte Carlo analysis, where the hub is optimised separately for 1 000 daily scenarios, and (b) a two-stage stochastic optimisation that fixes one set of decisions for hours 0–11 and adapts for hours 12–23 after conditions are observed. Gaussian inputs yield clustered costs (mean= $51.6, σ= $0.2) and a 99 % CVaR below $52, suggesting negligible risk. KDE inputs raise the Monte Carlo mean to $80.6 and lift the 99 % CVaR to $114, exposing heavy-tailed risk. Within the stochastic model the identical first-stage policy costs $79.0 with Gaussian data but only $71.3 with KDE, as recourse exploits sunny scenarios and trims the 95 % CVaR from $106.4 to $93.5. Consequently, Gaussian assumptions obscure true operating costs and financial exposure, whereas incorporating empirically derived KDE uncertainty within stochastic optimisation both lowers the average cost and provides stronger protection against extreme cost outcomes.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100348"},"PeriodicalIF":3.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Concise Review of Home Health Care Routing and Scheduling Problem 家庭健康照护路线与排程问题简评

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-07-12 DOI: 10.1016/j.orp.2025.100347

Soumen Atta , Vítor Basto-Fernandes , Michael Emmerich

{"title":"A Concise Review of Home Health Care Routing and Scheduling Problem","authors":"Soumen Atta , Vítor Basto-Fernandes , Michael Emmerich","doi":"10.1016/j.orp.2025.100347","DOIUrl":"10.1016/j.orp.2025.100347","url":null,"abstract":"<div><div>The Home Health Care Routing and Scheduling Problem (HHCRSP) plays a crucial role in optimizing the delivery of home-based healthcare services by efficiently allocating caregivers to patient locations while adhering to logistical, operational, and regulatory constraints. This concise review provides an analysis of HHCRSP, discussing its key objectives, constraints, and solution methodologies. The study examines various optimization approaches, including exact algorithms, heuristics, and metaheuristic techniques. Furthermore, the impact of HHCRSP on healthcare delivery efficiency is explored, highlighting its role in reducing operational costs, improving service quality, and ensuring continuity of care. The article also discusses the regulatory requirements affecting HHCRSP, addressing compliance with legal and organizational requirements, quality assurance frameworks, economic constraints, and patient prioritization mandates. The challenges associated with HHCRSP, including logistical complexities, workload balancing, and technological barriers, are also reviewed. To align HHCRSP with regulatory frameworks, this review discusses various strategies such as adaptive scheduling, advanced algorithmic solutions, and the integration of environmental and social sustainability considerations. Additionally, emerging technological advancements, including the use of Artificial Intelligence (AI), Internet of Things (IoT), and intelligent transport systems, are evaluated for their potential to enhance HHCRSP efficiency. The article concludes by summarizing key findings, discussing the practical implications of HHCRSP for healthcare providers, and outlining future research directions. Addressing existing gaps, such as AI explainability, blockchain integration for secure scheduling, and sustainable healthcare logistics, remains a critical avenue for further exploration. As the demand for home healthcare services grows, innovative HHCRSP solutions will be essential to ensuring high-quality, cost-effective, and patient-centered care.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100347"},"PeriodicalIF":3.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty 预算不确定性下优先级递减的鲁棒斯坦纳团队定向问题

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-06-28 DOI: 10.1016/j.orp.2025.100344

Lucas Assunção, Andréa Cynthia Santos

{"title":"The Robust Steiner Team Orienteering Problem with Decreasing Priorities under budgeted uncertainty","authors":"Lucas Assunção, Andréa Cynthia Santos","doi":"10.1016/j.orp.2025.100344","DOIUrl":"10.1016/j.orp.2025.100344","url":null,"abstract":"<div><div>Post-disaster relief operations have gained attention over the past decade, focusing on enhancing resilience in labor and social environments. This work introduces the Robust Steiner Team Orienteering Problem with Decreasing Priorities (R-STOP-DP) to model emergency rescue operations where several locations might need relief shuttles, but exact demands cannot be foreseen. R-STOP-DP is a variation of the vehicle routing problem with location priorities that applies robust optimization to model the variability on service times incurred by visiting locations. Locations are sub-divided into mandatory and optional, being the latter linked to priorities that linearly decrease over time. The goal is to find robust feasible routes maximizing the priorities collected, while considering the worst-case conditions of service times within an <em>uncertainty budget</em> and a routes’ duration limit. We propose two compact formulations – reinforced by valid inequalities adapted from the literature – and solve them in a cut-and-branch fashion. In addition, we propose a <em>kernel search</em> mat-heuristic and a <em>simulated annealing</em> heuristic. Computational experiments suggest the strict dominance of one formulation, improving dual bounds by 12.29% on average over the 360 instances tested. The cut-and-branch algorithm based on the stronger model also stands out, solving 20 more instances than the other. The simulated annealing heuristic obtains a remarkable performance by improving over and/or reaching the best-known bounds for the complete benchmark, within an average execution time of 2.52 s. In turn, the kernel search mat-heuristic reaches or improves the bounds for 81% of the instances within 4.5 min of average running time.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100344"},"PeriodicalIF":3.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving the robustness of retail workforce management with a labor flexibility strategy and consideration of demand uncertainty 基于劳动力灵活性策略和需求不确定性的零售劳动力管理稳健性研究

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-06-28 DOI: 10.1016/j.orp.2025.100345

Andrés Felipe Porto , Amaia Lusa , Sebastián A. Herazo , César Augusto Henao

{"title":"Improving the robustness of retail workforce management with a labor flexibility strategy and consideration of demand uncertainty","authors":"Andrés Felipe Porto , Amaia Lusa , Sebastián A. Herazo , César Augusto Henao","doi":"10.1016/j.orp.2025.100345","DOIUrl":"10.1016/j.orp.2025.100345","url":null,"abstract":"<div><div>This article examines the challenge of personnel scheduling problem by incorporating a labor flexibility approach that integrates annualized hours, multiskilled employees, and overtime within an uncertain demand environment. To address this problem, a two-stage stochastic optimization model is developed to determine the optimal workforce size, structure a targeted training program using a 2-chaining approach, and allocate weekly working hours, both regular and overtime, while explicitly considering demand variability. The proposed approach is assessed through multiple experiments to analyze the impact of incorporating multiskilling and different levels of demand fluctuations. Furthermore, the workforce configuration—comprising staff size and training structure— resulting from the stochastic model is compared with that obtained using a deterministic framework. The findings indicate that the stochastic model yields more robust and cost-effective solutions under demand uncertainty, significantly reducing training costs and minimizing expected labor costs related to overstaffing, understaffing, and wages. Additionally, the results reinforce the synergistic relationship between multiskilling and overtime in mitigating workforce imbalances caused by demand uncertainty. Finally, this research offers strategic insights for managers in retail and service industries aiming to optimize workforce planning and adaptability while maintaining cost efficiency in the face of fluctuating and uncertain demand.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100345"},"PeriodicalIF":3.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive hybrid optimization for integrated project scheduling and staffing problem with time/resource trade-offs 具有时间/资源权衡的综合项目调度和人员配置问题的自适应混合优化

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-06-25 DOI: 10.1016/j.orp.2025.100346

Muhai Hu , Yao Wang , Wendi Tian

{"title":"Adaptive hybrid optimization for integrated project scheduling and staffing problem with time/resource trade-offs","authors":"Muhai Hu , Yao Wang , Wendi Tian","doi":"10.1016/j.orp.2025.100346","DOIUrl":"10.1016/j.orp.2025.100346","url":null,"abstract":"<div><div>The integration of project scheduling and human resource allocation is crucial in modern project management, particularly in complex and resource-constrained environments. This study addresses the Integrated Project Scheduling and Personnel Staffing Problem (IPSPSP) with time/resource trade-offs by proposing a dual-objective optimization model that minimizes both project duration and personnel cost. To solve this problem, we introduce an adaptive hybrid algorithm combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The algorithm employs hybrid encoding for activity modes, activity priority lists and personnel allocation plans, coupled with a hypervolume-based adaptive search mechanism to improve solution quality. Experimental results demonstrate that the adaptive hybrid algorithm outperforms standalone NSGA-II and MOPSO in generating schedules and optimizing resource allocation. This study makes significant contributions by presenting a novel integrated model tailored for projects, an effective adaptive hybrid optimization algorithm and a comprehensive performance evaluation, thereby advancing the field of integrated scheduling and staffing in project management.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100346"},"PeriodicalIF":3.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A branch-and-price solution strategy for integrated process planning and scheduling problems 集成工艺计划和调度问题的分支和价格解决策略

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-06-04 DOI: 10.1016/j.orp.2025.100343

Dung-Ying Lin, Che-Hao Chen

{"title":"A branch-and-price solution strategy for integrated process planning and scheduling problems","authors":"Dung-Ying Lin, Che-Hao Chen","doi":"10.1016/j.orp.2025.100343","DOIUrl":"10.1016/j.orp.2025.100343","url":null,"abstract":"<div><div>This research investigates the integrated process planning and scheduling (IPPS) problem that considers process planning and production scheduling simultaneously with the aim of minimizing makespan. To solve the IPPS problem, we propose a branch-and-price (B&P) solution strategy that decomposes the problem according to the Dantzig-Wolfe principle and searches for integer solutions with a branch-and-bound framework. The decomposed master problem solves the scheduling problem and determines the corresponding timing information. The subproblem finds the optimal processing route and machine assignment based on the pricing information passed from the master problem. One of the critical features of the decomposition strategy is that the resulting subproblem can be reduced to a shortest path problem and can be solved with a proposed linear time algorithm. Numerical results show that the proposed B&P solution strategy can effectively and efficiently solve benchmark problem instances. Managerial insights are drawn based on the numerical results and sensitivity analysis to demonstrate the practical use of the proposed framework.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100343"},"PeriodicalIF":3.7,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The selective multiple depot pickup and delivery problem with multiple time windows and paired demand 具有多个时间窗口和成对需求的选择性多仓库取货问题

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-06-01 DOI: 10.1016/j.orp.2025.100342

Daniël Roelink , Giovanni Campuzano , Martijn Mes , Eduardo Lalla-Ruiz

{"title":"The selective multiple depot pickup and delivery problem with multiple time windows and paired demand","authors":"Daniël Roelink , Giovanni Campuzano , Martijn Mes , Eduardo Lalla-Ruiz","doi":"10.1016/j.orp.2025.100342","DOIUrl":"10.1016/j.orp.2025.100342","url":null,"abstract":"<div><div>A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (<em>i</em>) profit maximization including CO<sub>2</sub> costs, (<em>ii</em>) soft time windows, and (<em>iii</em>) soft time windows including CO<sub>2</sub> costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO<sub>2</sub> costs shows a slight sensitivity of the routing schedules to the CO<sub>2</sub> emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100342"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized message passing algorithm for heterogeneous multi-depot vehicle routing problems 异构多车场车辆路由问题的分散消息传递算法

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-04-28 DOI: 10.1016/j.orp.2025.100341

Byeong-Min Jeong , Dae-Sung Jang , Han-Lim Choi

{"title":"Decentralized message passing algorithm for heterogeneous multi-depot vehicle routing problems","authors":"Byeong-Min Jeong , Dae-Sung Jang , Han-Lim Choi","doi":"10.1016/j.orp.2025.100341","DOIUrl":"10.1016/j.orp.2025.100341","url":null,"abstract":"<div><div>In this paper, a novel message-passing algorithm, named AMP-R, based on belief propagation is proposed to solve the heterogeneous multi-depot vehicle routing problem (HMDVRP) in a distributed manner. Unlike traditional approaches, this is the first attempt to decentralize the solution process for the HMDVRP at the depot level, enabling each depot to independently compute and exchange messages to derive conflict-free solutions. The HMDVRP requires assigning customers to depots and determining routes that minimize total travel cost. By reformulating the problem as a maximum a posteriori estimation in a graphical model comprising depot and customer nodes, The proposed approach enables decentralized message calculation and exchange between depots, effectively addressing various types of the HMDVRP. In this process, it is derived that each message calculation can be reduced to a subset-visit traveling salesman problem or a capacitated vehicle routing problem, and approximation techniques are proposed to address these computational challenges. Furthermore, to ensure solution convergence and feasibility, message buffers and a refinement process are introduced. Extensive simulations demonstrate that the proposed AMP-R algorithm yields near-optimal solutions with computational efficiency, offering practical performance for complex large-scale instances where finding optimal solutions is challenging.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100341"},"PeriodicalIF":3.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic pricing with waiting and price-anticipating customers 动态定价与等待和价格预期的客户

IF 3.7 4区管理学

Operations Research Perspectives Pub Date : 2025-04-11 DOI: 10.1016/j.orp.2025.100337

Fabian Lange , Rainer Schlosser

{"title":"Dynamic pricing with waiting and price-anticipating customers","authors":"Fabian Lange , Rainer Schlosser","doi":"10.1016/j.orp.2025.100337","DOIUrl":"10.1016/j.orp.2025.100337","url":null,"abstract":"<div><div>Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers’ and competitors’ behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems with incomplete knowledge. RL algorithms have shown to outperform rule-based competitor heuristics if the underlying Markov decision process is kept simple and customers are myopic. However, the myopic assumption is becoming increasingly unrealistic since technology like price trackers allows customers to act more strategically. To counteract unknown strategic behavior is difficult as pricing policies and consumers buying patterns influence each other and hence, approaches to iteratively update both sides sequentially are time consuming and convergence is unclear. In this work, we show how to use RL algorithms to optimize prices in the presence of different types of strategic customers that may wait and time their buying decisions. We consider strategic customers that (i) compare current prices against past prices and that (ii) anticipate future price developments. To avoid frequently updating pricing policies and consumer price forecasts, we endogenize the impact of current price decisions on the associated changes in forecast-based consumer behaviors. Besides monopoly markets, we further investigate how the interaction with strategic consumers is affected by additional competing vendors in duopoly markets and present managerial insights for all market setups and customer types.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"14 ","pages":"Article 100337"},"PeriodicalIF":3.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0