Operations Research Perspectives最新文献

英文中文

Dynamic pricing with waiting and price-anticipating customers

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

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

Fabian Lange , Rainer Schlosser

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.

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引用次数: 0

Deep reinforcement learning approach for real-time airport gate assignment 用于实时机场登机口分配的深度强化学习方法

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-04-11 DOI: 10.1016/j.orp.2025.100338

Haonan Li , Xu Wu , Marta Ribeiro , Bruno Santos , Pan Zheng

Assigning aircraft to gates is one of the most important daily decision problems that airport professionals face. The solution to this problem has raised a significant effort, with many researchers tackling many different variants of this problem. However, most existing studies on gate assignment contain only a static perspective without considering possible future disruptions and uncertainties. We bridge this gap by looking at gate assignments as a dynamic decision-making process. This paper presents the Real-time Gate Assignment Problem Solution (REGAPS) algorithm, an innovative method adept at resolving pre-assignment issues and dynamically optimizing gate assignments in real-time at airports through the integration of Deep Reinforcement Learning (DRL). This work represents the first time that DRL is used with real airport data and a configuration containing a large number of flights and gates. The methodology combines a tailored Markov Decision Process (MDP) formulation with the Asynchronous Advantage Actor–Critic (A3C) architecture. Multiple factors, such as flight schedules, gate availability, and passenger walking time, are considered. An empirical case study demonstrates that the REGAPS outperforms two classic deep Q-learning algorithms and a traditional Genetic Algorithm in terms of reducing passenger walking time and apron gate assignment. Finally, supplementary experiments highlight REGAPS’s adaptability under various gate assignment rules for international and domestic flights. The finding demonstrates that not only did REGAPS outperform COVID restrictions, but it can also produce considerable benefits under other policies.

将飞机分配到登机口是机场专业人员面临的最重要的日常决策问题之一。为解决这一问题，许多研究人员付出了巨大的努力，解决了这一问题的许多不同变体。然而，大多数现有的登机口分配研究都只从静态角度出发，没有考虑到未来可能出现的干扰和不确定性。我们将闸门分配视为一个动态决策过程，从而弥补了这一不足。本文介绍了实时登机口分配问题解决方案（REGAPS）算法，这是一种创新方法，通过集成深度强化学习（DRL），善于解决预先分配问题，并在机场实时动态优化登机口分配。这项研究首次将 DRL 应用于真实机场数据以及包含大量航班和登机口的配置。该方法将量身定制的马尔可夫决策过程（MDP）公式与异步优势行为批判者（A3C）架构相结合。该方法考虑了航班时刻表、登机口可用性和乘客步行时间等多种因素。一项实证案例研究表明，在减少乘客步行时间和停机坪登机口分配方面，REGAPS 优于两种经典的深度 Q-learning 算法和一种传统的遗传算法。最后，补充实验强调了 REGAPS 在国际和国内航班各种登机口分配规则下的适应性。实验结果表明，REGAPS 不仅优于 COVID 限制，而且在其他政策下也能产生可观的效益。

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引用次数: 0

Scheduling with sequence-dependent setup times in short-term production planning: A main path analysis-based review

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-04-09 DOI: 10.1016/j.orp.2025.100340

Kuo-Ching Ying , Pourya Pourhejazy , Zhi-Rong Lin

The role of setup times in production planning and control was recognised in the late 1960s. Since then, a growing number of scheduling problems have accounted for sequence-dependent setup time variables. This study aims to provide a systematic review of setup times in the short-term production planning literature, using an objective, algorithm-based approach. The Main Path Analysis (MPA) and Cluster Analysis (CA) methods are employed to identify patterns of knowledge development and the most significant advancements in the field. Over 2100 articles published between 1986 and 2024 were considered in the review. The seminal articles contributing to the advances in setup times for production scheduling are reviewed. Meanwhile, the core optimisation technologies, model characteristics, and emerging issues at different stages of literature development are discussed. The key extensions of the main path are further explored to identify secondary research interests in the field. Twenty-two research themes were identified to provide an overall perspective and shed light on the technical features and challenges. Finally, future research directions are suggested based on the outcomes of this systematic review.

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引用次数: 0

The berth allocation problem in bulk terminals under uncertainty

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-04-05 DOI: 10.1016/j.orp.2025.100334

Filipe Rodrigues

Uncertainty is critical in bulk terminals because it is inherent to many operations. In particular, the berth allocation problem (BAP) is greatly affected by the uncertain arrival times of the vessels. In this paper, we propose the first distributionally robust optimization (DRO) model for the BAP in bulk terminals, where the probability distribution of the arrival times is assumed to be unknown but belongs to an ambiguity set. To solve the model, we use an exact decomposition algorithm (DA) in which the probability distribution information is iteratively included in the master problem through optimal dual cuts. The DA is then enhanced with two improvement strategies to reduce the associated computational time; however, with these strategies, the DA may no longer be exact and is still inefficient for solving large-scale instances. To overcome these issues, we propose a modified exact DA where the dual cuts used in the original DA are replaced by powerful primal cuts that drastically reduce the time required to solve the DRO model, making it possible to handle large-scale instances. The reported computational experiments also show clear benefits of using DRO to tackle uncertainty compared to stochastic programming and robust optimization.

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引用次数: 0

How to improve the key component's quality: the impact of overconfident manufacturer's R&D investment

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-04-04 DOI: 10.1016/j.orp.2025.100339

Linghong Zhang , Huimin Pan

In the EV supply chain, R&D cooperation for key components between the manufacturer and tier 2 supplier is common. This paper primarily explores the effects of cross-echelon R&D cooperation and overconfidence on the quality and price of key components, as well as the profits of the manufacturer and suppliers. We assume that the supply chain consists of one tier 2 supplier, one tier 1 supplier, and one overconfident manufacturer. We first present the wholesale price contract as a benchmark, followed by the two-part contract and the equity contract between the tier 2 supplier and the manufacturer. Additionally, we compare above three contracts and provide numerical examples. We find that (1) the manufacturer's overconfidence level and the contract type jointly affect the quality of the key component and the profit of the tier 1 supplier. When the overconfidence level is low or the manufacturer's shareholding ratio is low, the two-part contract is more effective in improving the key component's quality and the tier 1 supplier's profit. Or else, the equity cooperation contract is more effective in improving the key component's quality and the tier 1 supplier's profit. (2) Under the equity contract, the tier 2 supplier can obtain higher profit, while the manufacturer may achieve higher overestimated expected profit under the two-part contract. (3) Under the two-part contract, the manufacturer's overconfidence increases the component's quality and the profit of the tier 1 supplier, but decreases the profit of the tier 2 supplier. In the other two contracts, the manufacturer's overconfidence leads to a decline in the key component's quality and the profits of both the tier 1 and tier 2 suppliers.

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引用次数: 0

Enhancing industrial maintenance planning: Optimization of human error reduction and spare parts management 加强工业维护规划：优化减少人为错误和备件管理

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-04-01 DOI: 10.1016/j.orp.2025.100336

Vahideh Bafandegan Emroozi , Mostafa Kazemi , Mahdi Doostparast

Maintenance is pivotal in the industrial sector, influencing efficiency, reliability, safety, and profitability. An organized spare parts inventory supports maintenance efforts by minimizing downtime, ensuring safety, and optimizing maintenance budgets. Effective spare parts management enhances maintenance operations and improves cash flow. Conversely, human error can greatly diminish the effectiveness of maintenance efforts. This paper presents a mathematical model aimed at minimizing costs through optimized preventive maintenance (PM) planning, effective spare parts inventory control, and reduction of human error. The study provides decision-makers with crucial insights for strategically managing maintenance procedures while accounting for the effect of human error. The model is validated in real-world scenarios through sensitivity analysis, focusing on the shape parameter of the Weibull distribution, and the equipment's effective rate. Findings reveal that as the number of periods increases, maintenance operations follow a specific, predictable cycle. Moreover, the optimal human error probability (HEP) for cost minimization is identified as 0.02. These insights guide decision-makers in recognizing factors influencing human error and implementing proactive strategies to enhance maintenance performance.

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引用次数: 0

Neighbourhood search-based metaheuristics for the bi-objective Pareto optimization of total weighted earliness-tardiness and makespan in a JIT single machine scheduling problem

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-03-22 DOI: 10.1016/j.orp.2025.100335

Sona Babu, B.S. Girish

This paper studies the simultaneous minimization of total weighted earliness-tardiness (TWET) and makespan in a just-in-time single-machine scheduling problem (JIT-SMSP) with sequence-dependent setup times and distinct due windows, allowing idle times in the schedules. Multiple variants of variable neighbourhood descent (VND) based metaheuristic algorithms are proposed to generate Pareto-optimal solutions for this NP-hard problem. An optimal timing algorithm (OTA) is presented that generates a piecewise linear convex trade-off curve between the two objectives for a given sequence of jobs. The trade-off curves corresponding to the sequences of jobs generated in the proposed metaheuristics are trimmed and merged using a Pareto front generation procedure to generate the Pareto-optimal front comprising line segments and points. The computational performance of the proposed VND-based metaheuristic algorithms is compared with state-of-the-art metaheuristic algorithms from the literature on test instances of varying sizes using four performance metrics devised to compare Pareto fronts comprising line segments and points. The performance comparisons reveal that a proposed variant of backtrack-based iterated VND with multiple neighbourhood structures outperforms the other algorithms in most performance metrics.

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引用次数: 0

Simplicity or flexibility? Dual sourcing in multi-echelon systems under disruption

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-03-21 DOI: 10.1016/j.orp.2025.100333

Sadeque Hamdan , Youssef Boulaksil , Kilani Ghoudi , Younes Hamdouch

Disruptive events like the COVID-19 pandemic have exposed supply chain vulnerabilities. This study focuses on dual sourcing as a resilient strategy and examines a stochastic, single-item, multi-echelon, multi-period, dual sourcing inventory system under backorders. In each echelon, the decision-maker faces a dual-sourcing situation wherein the item can be replenished from a slow regular supplier or a more expensive and faster emergency supplier. We compare two inventory management policies: the Dual-Index Policy (DIP) and the Tailored Base-Surge (TBS) Policy, while also investigating how various factors influence policy effectiveness and the role of demand disruptions. Our findings indicate that the TBS policy generally relies more on upstream suppliers than the DIP. However, in scenarios of high demand uncertainty, upstream suppliers are seldom used. DIP is more effective for short networks facing sudden demand drops, whereas TBS excels when experiencing demand spikes.

引用次数: 0

Evaluating metaheuristic solution quality for a hierarchical vehicle routing problem by strong lower bounding

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-03-15 DOI: 10.1016/j.orp.2025.100332

Marduch Tadaros , Athanasios Migdalas , Nils-Hassan Quttineh , Torbjörn Larsson

We study a vehicle routing problem that originates from a Nordic distribution company and includes the essential decision-making components of the company’s logistics operations. The problem considers customer deliveries from a depot using heavy depot vehicles, swap bodies, optional switch points, and lighter local vehicles; a feature is that deliveries are made by both depot and local vehicles. The problem has earlier been solved by a fast metaheuristic, which does however not give any quality guarantee. To assess the solution quality, two strong formulations of the problem based on the column generation approach are developed. In both of these the computational complexity is mitigated through an enumeration of the switch point options. The formulations are evaluated with respect to the quality of the linear programming lower bounds in relation to the bounds obtained from a compact formulation. The strong lower bounding quality enables a significant reduction of the optimality gap compared to the compact formulation. Further, the bounds verify the high quality of the metaheuristic solutions, and for several problem instances the optimality gap is even closed.

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引用次数: 0

The impact of information disclosure and smart technology integration on e-retailing performance: A production delivery policy framework

IF 3.7 4区管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Perspectives

Pub Date : 2025-02-27 DOI: 10.1016/j.orp.2025.100328

Muhammad Tayyab , Hira Tahir , Muhammad Salman Habib

The electronic retailers face distinct challenges in information sharing compared to their purely offline counterparts, particularly in transparently communicating their environmental practices to increasingly eco-conscious consumers. This complexity increases in e-retailing due to the absence of direct interaction and makes it difficult for consumers to evaluate the sustainability efforts of retailing channel’s stakeholders. In response to it, manufacturers and e-retailers are leveraging social media and blockchain technology for personalized advertising to bridge this information transparency gap. This research presents a sustainable multi-item integrated model for manufacturer–retailer collaboration in e-retailing by incorporating multiple delivery policies and investments in technology aimed at information disclosure and environmental footprint reduction. The manufacturer adopts a smart production system and reuse returned goods in the manufacturing process while investing in Green Emissions Reduction Technology. Meanwhile, the e-retailer enhances product demand through Information Disclosure Technology on social media and blockchain by showcasing their environmental protection efforts. By employing a hybrid analytic-metaheuristic approach, we determine optimal production and delivery policies to improve green consumer service under varying budgetary and spatial constraints. The results demonstrate a 4.39% increase in online consumer demand through information sharing and a 3.86% improvement in profitability of the collaborative retailing system under single-setup multi-delivery policy that confirms robustness of the proposed model. Scenario analysis further provides decision-makers with actionable insights by showcasing 8.44% increment in the system profit by converting traditional production into smart production system. Moreover, the sensitivity of the proposed model to balancing the technology investments among emission control and information disclosure efforts suggests keeping track of the efficiency parameters of these investment options before making technology budget allocations.

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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Operations Research Perspectives

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