Operations Research Perspectives最新文献

Dynamic flexible job shop co-scheduling optimization based on graph neural network and deep reinforcement learning 基于图神经网络和深度强化学习的动态柔性作业车间协同调度优化

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

Operations Research Perspectives

Pub Date : 2026-01-23 DOI: 10.1016/j.orp.2026.100379

Lan Yang, Zhong Yang, Li Bi, Xiaogang Jiao

With the continuous advancement of manufacturing, job shop scheduling faces complex dynamic disturbances that pose significant challenges to production. This paper investigates the dynamic flexible job shop scheduling problem with job priority and transportation time constraints (DFJSP-PT). The study considers two types of dynamic events: random job arrivals and urgent order insertions implemented through a predefined job priority mechanism. To address the limitations of traditional scheduling methods under complex dynamics, this paper proposes a hybrid scheduling framework based on graph neural network (GNN) and deep reinforcement learning (DRL). The method constructs a Markov Decision Process (MDP) and employs a heterogeneous graph neural network to model the job scheduling state. When new jobs arrive, it enables incremental dynamic expansion of the graph structure, thereby avoiding the need to reconstruct the entire state space. The framework integrates transport time and job urgency into the decision-making process. It dynamically adjusts priorities through a weighting mechanism to achieve joint optimization of operation sequencing and machine allocation. Furthermore, the method introduces a priority experience replay (PER) mechanism based on temporal difference error. This mechanism is combined with composite dispatching rules and a global elite retention strategy, which enhances the algorithm's adaptive learning capability for random job arrivals and emergency order insertion events. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in both convergence performance and solution quality. The algorithm provides an effective technical pathway for intelligent job shop scheduling in dynamic production environments.

随着制造业的不断发展，作业车间调度面临着复杂的动态扰动，对生产提出了重大挑战。研究了具有作业优先级和运输时间约束的动态柔性作业车间调度问题。该研究考虑了两种类型的动态事件：随机作业到达和通过预定义的作业优先级机制实现的紧急订单插入。针对传统调度方法在复杂动态环境下的局限性，提出了一种基于图神经网络（GNN）和深度强化学习（DRL）的混合调度框架。该方法构造了马尔可夫决策过程，并采用异构图神经网络对作业调度状态进行建模。当新作业到达时，它支持图结构的增量动态扩展，从而避免了重建整个状态空间的需要。该框架将运输时间和工作紧迫性纳入决策过程。通过加权机制动态调整优先级，实现作业排序和机器分配的联合优化。此外，该方法还引入了一种基于时间差误差的优先级体验重放机制。该机制与复合调度规则和全局精英保留策略相结合，增强了算法对随机作业到达和紧急订单插入事件的自适应学习能力。实验结果表明，该算法在收敛性能和解质量上都明显优于传统方法。该算法为动态生产环境下的智能作业车间调度提供了有效的技术途径。

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引用次数: 0

Multi-stage stochastic engine usage optimization for fighter jet fleet using nested decomposition algorithm 基于嵌套分解算法的战斗机机队多阶段随机发动机使用优化

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

Operations Research Perspectives

Pub Date : 2026-01-14 DOI: 10.1016/j.orp.2026.100376

Dung-Ying Lin, Cing-Chen Wu

Fighter jet squadrons face a critical challenge: meeting rigorous monthly flight-hour targets while managing strict engine service life limitations. This complex task necessitates the optimal allocation of engine resources and meticulous planning of flight hours for each aircraft, thereby balancing operational demands with maintenance imperatives. Our study addressed this multifaceted challenge by proposing a novel multi-stage stochastic programming (MSSP) model. Under uncertainty considerations, the model assists engine maintenance contractors in determining when to disassemble and reassemble fighter jet engines to ensure fighter jets meet the flight-hour requirements of the air force. Unlike previous deterministic approaches, our model incorporates random factors and uncertainties inherent in aviation operations, such as weather variability and mission changes. This comprehensive approach represents a considerable advancement in the field. To tackle the exponential increase in problem complexity at practical scales, we developed a nested decomposition algorithm. This innovative algorithm efficiently decomposes large-scale problems into manageable subproblems, utilizing tight lower bounds and problem-specific cuts to enhance computational efficiency. Empirical studies based on real world planning settings show that, when compared with existing manual planning practices, the proposed approach reduces the number of engines reaching their service life limits by 15.3 percent and increases available flight hours by 465.66 hours, thereby demonstrating clear and substantial operational benefits.

战斗机中队面临着一个严峻的挑战：在满足严格的每月飞行小时目标的同时，还要管理严格的发动机使用寿命限制。这项复杂的任务需要对发动机资源进行最佳分配，并对每架飞机的飞行时间进行细致规划，从而平衡运行需求和维护需求。我们的研究通过提出一种新的多阶段随机规划（MSSP）模型来解决这一多方面的挑战。在考虑不确定性的情况下，该模型帮助发动机维修承包商确定何时拆卸和重新组装战斗机发动机，以确保战斗机满足空军的飞行小时要求。与以前的确定性方法不同，我们的模型包含了航空操作中固有的随机因素和不确定性，例如天气变化和任务变化。这种综合方法代表了该领域的一项重大进步。为了在实际规模上解决问题复杂性的指数增长，我们开发了一个嵌套分解算法。该算法有效地将大规模问题分解为可管理的子问题，利用紧下界和问题特定切割来提高计算效率。基于现实世界规划设置的实证研究表明，与现有的手动规划实践相比，所提出的方法将达到其使用寿命极限的发动机数量减少了15.3%，并将可用飞行小时数增加了465.66小时，从而显示出明显而实质性的运营效益。

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引用次数: 0

Assessing the impact of environmental factors on emergency healthcare quality: A benchmarking approach 评估环境因素对急救医疗质量的影响：一种基准方法

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

Operations Research Perspectives

Pub Date : 2026-01-13 DOI: 10.1016/j.orp.2026.100377

Marc Aliana , Diego Prior , Emili Tortosa-Ausina

Evaluating the quality of emergency departments in hospitals is crucial for optimizing healthcare and allocating resources effectively. Existing metrics predominantly focus on internal variables (e.g., bed occupancy or time to treatment), disregarding possible external environmental factors beyond their control. In order to address this issue, in this paper we have a threefold objective. First, we introduce a Quality Composite Indicator (QCI) for benchmarking emergency departments quality, considering the specific impact of demographic, socio-economic, and behavioral factors. This metric minimizes the influence of outliers, facilitating a comparison among emergency departments, and enabling the identification of top performers based on quality indicators. Second, our study, conducted across 85 health trusts in England, reveals that emergency departments in areas with higher population density, a larger elderly population, increased birth rates, and more disadvantaged households generally provide lower-quality services. Furthermore, a higher incidence of depression and anxiety, combined with elevated crime levels, further worsens the quality of urgent healthcare services in these regions. Third, our analysis highlights differences in how certain environmental factors affect overall hospital performance versus specialized units like emergency departments. These results uncovered significant regional disparities in healthcare quality, contradicting the goal of nationwide uniformity.

评价医院急诊科质量对优化医疗服务和有效配置资源至关重要。现有的指标主要关注内部变量（例如，床位占用或治疗时间），而忽略了可能超出其控制的外部环境因素。为了解决这个问题，在本文中我们有三个目标。首先，考虑到人口、社会经济和行为因素的具体影响，我们引入了一个质量综合指标（QCI）来衡量急诊科的质量。该指标最大限度地减少了异常值的影响，促进了急诊部门之间的比较，并根据质量指标确定了表现最好的部门。其次，我们对英格兰85家卫生信托机构进行的研究表明，在人口密度较高、老年人口较多、出生率较高和弱势家庭较多的地区，急诊科通常提供的服务质量较低。此外，抑郁和焦虑的发病率较高，加上犯罪率上升，进一步恶化了这些地区紧急保健服务的质量。第三，我们的分析强调了某些环境因素对医院整体绩效的影响与急诊室等专业单位的差异。这些结果揭示了医疗保健质量的显著地区差异，与全国统一的目标相矛盾。

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引用次数: 0

Integrated production and quality dynamics under organizational learning: A hybrid continuous–discrete framework 组织学习下的集成生产与质量动力学：一个连续-离散混合框架

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

Operations Research Perspectives

Pub Date : 2025-12-26 DOI: 10.1016/j.orp.2025.100374

Balázs Szabó , Zsolt Nemeskéri

This paper presents a hybrid continuous–discrete framework for optimizing production, pricing, quality, and learning investments under organizational learning, defined as efficiency gains through accumulated experience. Extending prior work on price-quality dynamics and production optimization, the model integrates real-time operational decisions with periodic strategic planning to address dynamic market challenges. It incorporates a stochastic initial learning rate to capture uncertainty in learning processes. In contrast to conventional discrete models that may exhibit qualitatively different dynamics (Vörös 2021), the hybrid framework is constructed to converge rigorously to the continuous-time optimum, ensuring consistent decision-making across planning horizons. Monte Carlo simulations provide insights into production increases, gradual price reductions, and quality improvements, with profit gains up to 14.7% in scenarios with high learning and quality sensitivity. The framework offers actionable guidance for industries like automotive, electronics, aerospace, and pharmaceuticals, enhancing cost efficiency and competitiveness through integrated strategic and operational optimization, though without explicit competitive interactions.

本文提出了一个连续-离散混合框架，用于优化组织学习下的生产、定价、质量和学习投资，定义为通过积累经验获得的效率收益。该模型扩展了先前关于价格质量动态和生产优化的工作，将实时运营决策与周期性战略规划相结合，以应对动态市场挑战。它采用随机初始学习率来捕捉学习过程中的不确定性。与可能表现出不同动力学性质的传统离散模型（Vörös 2021）相比，混合框架的构造严格收敛于连续时间最优，确保跨规划范围的一致决策。蒙特卡罗模拟提供了对产量增加、价格逐步下降和质量改进的见解，在具有高学习和质量敏感性的情况下，利润增长高达14.7%。该框架为汽车、电子、航空航天和制药等行业提供了可操作的指导，通过整合战略和运营优化来提高成本效率和竞争力，尽管没有明确的竞争互动。

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引用次数: 0

A diagonal Hestenes–Stiefel conjugate gradient algorithm with iterative complexity analysis and its application in robotic model 具有迭代复杂度分析的对角线Hestenes-Stiefel共轭梯度算法及其在机器人模型中的应用

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

Operations Research Perspectives

Pub Date : 2025-12-20 DOI: 10.1016/j.orp.2025.100375

Hassan Mohammad , Sulaiman Mohammed Ibrahim , Hayoung Choi , Rabiu Bashir Yunus

This paper presents a novel diagonal Hestenes–Stiefel conjugate gradient (DHS-CG) algorithm for solving large-scale unconstrained optimization problems. Building on the approaches presented in Dong et al. (2015) and Mohammad and Santos (2018), the proposed algorithm integrates a computationally efficient diagonal Hessian approximation into the HS-CG scheme. The algorithm constructs search directions that enjoy sufficient descent, trust region, and conjugacy properties without imposing restrictive line search conditions, thus ensuring efficiency robustness under Wolfe and Armijo-type line search strategies. We establish the global convergence and iterative complexity of the proposed algorithm under standard bounds and Lipschitz gradient assumptions. Extensive numerical experiments on benchmark problems, including large-scale cases with up to 500,000 variables, demonstrated that DHS-CG consistently outperformed state-of-the-art CG variants, such as CG_DESCENT and classical Hestenes–Stiefel (HS). In both low and high dimensional settings, the proposed algorithm achieves faster convergence, fewer function evaluations, and lower CPU time, highlighting its suitability for high performance scientific and engineering computations.

针对大规模无约束优化问题，提出了一种新的对角Hestenes-Stiefel共轭梯度（DHS-CG）算法。基于Dong等人（2015）和Mohammad和Santos（2018）提出的方法，提出的算法将计算效率高的对角线Hessian近似集成到HS-CG方案中。该算法构建的搜索方向在不施加限制性线搜索条件的情况下具有足够的下降、信任域和共轭性质，从而保证了Wolfe和armijo型线搜索策略下的效率鲁棒性。在标准界和Lipschitz梯度假设下，证明了算法的全局收敛性和迭代复杂度。在基准问题上进行的大量数值实验，包括多达500,000个变量的大规模案例，表明DHS-CG始终优于最先进的CG变体，如CG_DESCENT和经典的Hestenes-Stiefel （HS）。在低维和高维环境下，该算法均能实现更快的收敛速度、更少的函数求值和更低的CPU时间，突出了其对高性能科学和工程计算的适用性。

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引用次数: 0

A cost and emission optimization framework for strategic intermodal freight transportation infrastructure development 战略性多式联运基础设施发展的成本与排放优化框架

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

Operations Research Perspectives

Pub Date : 2025-12-01 DOI: 10.1016/j.orp.2025.100369

Ayoub Abusalih, Zeyu Liu

The freight transportation sector is critical to the economic prosperity in the US, but it also constitutes a major source of carbon emissions. Although intermodal transportation has been shown to increase operating efficiency and reduce carbon emissions, research on infrastructural support for intermodal transportation is still insufficient. In this study, we establish a mixed integer programming model to jointly optimize strategic infrastructure development decisions and freight transportation decisions over a long horizon. Our model features a mixture of traditional single-mode facilities and hybrid hubs that facilitate rail–water transportation integration. A branch-and-cut decomposition algorithm is developed to solve large-scale problems. We collect real-world freight, infrastructure, and operations data to conduct computational studies on the model performance and algorithm efficiency. We provide insights for practitioners to address infrastructure planning and budgetary concerns. A case study using the established model at the national scale shows that well-optimized transportation infrastructure investment could have over 300% return during a 25-year horizon. In addition, fully capitalizing on the maturing clean vehicle technologies could reduce carbon emissions by 73.56 million tons at an annual rate.

货运部门对美国的经济繁荣至关重要，但它也是碳排放的主要来源。虽然多式联运已被证明可以提高运营效率和减少碳排放，但对多式联运基础设施支持的研究仍然不足。在本研究中，我们建立了一个混合整数规划模型来共同优化战略基础设施发展决策和长期货运决策。我们的模型的特点是混合了传统的单模设施和混合枢纽，促进了铁路和水运的整合。针对大规模问题，提出了一种分支切割分解算法。我们收集真实世界的货运、基础设施和运营数据，对模型性能和算法效率进行计算研究。我们为从业者提供解决基础设施规划和预算问题的见解。在全国范围内使用已建立模型的案例研究表明，在25年内，优化的交通基础设施投资可以获得超过300%的回报。此外，充分利用成熟的清洁汽车技术，每年可减少碳排放7356万吨。

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引用次数: 0

Group-scheduling with simultaneous learning effects and convex resource allocations 具有同步学习效应和凸资源分配的群调度

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

Operations Research Perspectives

Pub Date : 2025-12-01 DOI: 10.1016/j.orp.2025.100370

Xue Huang , Hongyu He , Hong-Bin Bei , Yanzhi Zhao , Ning Wang , Yu Chang

In this article, we investigate the resource allocations group-scheduling with position-based learning effects. Under a single-machine, the purpose is to determine an optimal group sequence, job sequence within each group, and convex resource allocations (i.e., second partial derivatives of resources are not negative) assigned to the jobs. For the total resource consumption minimization with limited makespan constraint, we certify that the problem is polynomially solvable for some special situations. For the general situation, we establish a heuristic and a branch-and-bound algorithm. Computation experiments are given to test the effectiveness of solution algorithms. The proposed model can be probably applied to green manufacturing scenarios, supporting sustainable production by considering controllable processing time.

本文研究了具有位置学习效应的资源分配群调度问题。在单机情况下，目的是确定最优的组序列、每组内的作业序列以及分配给作业的凸资源分配（即资源的二阶偏导数不为负）。对于有限完工时间约束下的总资源消耗最小化问题，我们证明了在某些特殊情况下问题是多项式可解的。针对一般情况，建立了启发式算法和分支定界算法。通过计算实验验证了求解算法的有效性。该模型可以应用于绿色制造场景，通过考虑加工时间可控来支持可持续生产。

引用次数: 0

A systematic review of machine learning approaches in inventory control optimization 库存控制优化中机器学习方法的系统综述

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

Operations Research Perspectives

Pub Date : 2025-12-01 DOI: 10.1016/j.orp.2025.100367

Ritsaart Bergsma , Corné de Ruijt , Sandjai Bhulai

This systematic review investigates the applications of machine learning (ML) in inventory control, analyzing 122 articles to provide a comprehensive overview of the state of the art and identify future research directions. The study proposes a typology to classify the integration of ML into the inventory optimization framework, distinguishing three primary approaches: (1) separate estimation and optimization, where ML is applied to demand forecasting before optimization, (2) static ML-integrated optimization, where ML is directly embedded into optimization models, and (3) dynamic ML-integrated optimization, where reinforcement learning (RL) is employed to derive optimal inventory policies. The findings highlight that while RL applications are gaining prominence, significant research gaps remain, particularly in scaling algorithms to real-world problems, handling large action spaces, and developing RL algorithms that are tailored to inventory control. The review also assesses the operational dynamics of inventory systems addressed in the literature, such as single/multi-item models, lead time assumptions, and echelon structures. Underexplored areas include stochastic lead times, complementary items, quantity discounts, product obsolescence, and multi-echelon networks. The study concludes by outlining key research gaps and offering directions for future research to advance the integration of ML in inventory control.

本系统综述调查了机器学习（ML）在库存控制中的应用，分析了122篇文章，以提供最新技术的全面概述并确定未来的研究方向。该研究提出了一种分类方法，将机器学习集成到库存优化框架中，区分出三种主要方法：(1)单独的估计和优化，其中机器学习应用于优化前的需求预测；(2)静态机器学习集成优化，其中机器学习直接嵌入到优化模型中；(3)动态机器学习集成优化，其中使用强化学习（RL）来推导最优库存策略。研究结果强调，虽然强化学习的应用越来越突出，但仍存在重大的研究差距，特别是在将算法扩展到现实世界问题、处理大型动作空间以及开发针对库存控制的强化学习算法方面。本文还评估了文献中提到的库存系统的运行动态，如单/多项目模型、交货时间假设和梯队结构。未开发的领域包括随机交货时间、互补项目、数量折扣、产品过时和多层网络。该研究总结了关键的研究差距，并为未来的研究提供了方向，以推进机器学习在库存控制中的整合。

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引用次数: 0

Cross-border multi-level warehouse network optimization: Modeling and application based on mixed-integer linear programming 跨境多层次仓库网络优化：基于混合整数线性规划的建模与应用

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

Operations Research Perspectives

Pub Date : 2025-12-01 DOI: 10.1016/j.orp.2025.100366

Yun Gu

With the rapid development of global trade and cross-border e-commerce, optimizing cross-border multi-level warehouse networks has become a critical challenge to enhance supply chain efficiency and reduce operational costs. Traditional logistics planning methods struggle to address complex multi-level network structures, heterogeneous big data, and multi-dimensional influencing factors. This study proposes a mixed-integer linear programming model based on real-world operational requirements to optimize the layout of cross-border multi-level warehouse networks. The model integrates transportation costs, warehousing costs, tariff costs, and service lead time as key considerations. Through the incorporation of heuristic constraints and relaxation strategies, the model significantly improves computational efficiency and stability. Experimental results using real data from a cross-border e-commerce enterprise demonstrate that compared to existing solutions, the MILP model reduces total costs by 20.7 %, outperforms heuristic algorithms by >8 %, achieves faster computational speed, and maintains stable results. Furthermore, in 16 perturbation experiments, the model retained optimal solutions in 15 instances, showcasing strong robustness. This research provides critical theoretical and practical guidance for the scientific planning of cross-border logistics networks.

随着全球贸易和跨境电子商务的快速发展，优化跨境多层次仓储网络已成为提高供应链效率和降低运营成本的关键挑战。传统的物流规划方法难以解决复杂的多层次网络结构、异构的大数据和多维的影响因素。本文提出了一种基于实际操作需求的混合整数线性规划模型，用于优化跨境多层次仓库网络的布局。该模型将运输成本、仓储成本、关税成本和服务交付时间作为关键考虑因素。通过引入启发式约束和松弛策略，该模型显著提高了计算效率和稳定性。利用某跨境电子商务企业的真实数据进行的实验结果表明，与现有的解决方案相比，MILP模型的总成本降低了20.7%，比启发式算法高8%，计算速度更快，结果保持稳定。此外，在16个扰动实验中，模型在15个实例中保留了最优解，显示出较强的鲁棒性。本研究为跨境物流网络的科学规划提供了重要的理论和实践指导。

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引用次数: 0

Robust optimization model for closed-loop supply chain planning with collected material quality uncertainty 具有采集物料质量不确定性的闭环供应链规划鲁棒优化模型

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

Operations Research Perspectives

Pub Date : 2025-12-01 DOI: 10.1016/j.orp.2025.100368

Joonrak Kim , Seunghoon Lee

This study develops a robust optimization framework for closed-loop supply chain (CLSC) planning that explicitly accounts for uncertainty in the quality of recycled and remanufactured inputs. While such materials are critical for sustainability, their variable quality poses risks to production feasibility and supply reliability. To address this challenge, we propose an ordering-proportion-based robust model that distributes uncertainty across sourcing proportions and leverages the Bertsimas–Sim budget of uncertainty to balance conservatism and flexibility. A reformulation ensures tractability and preserves robust feasibility. Computational experiments demonstrate that the proposed model reduces shortages and stabilizes performance under independently realized uncertainties, while quantity-based robust models are more effective when uncertainties are correlated. Additional scalability tests confirm that the model remains computationally tractable for medium-sized networks. The findings highlight practical implications for managers, showing how proportion-based sourcing improves resilience, supports reliable demand fulfillment, and strengthens sustainability in CLSCs facing quality risks.

本研究为闭环供应链（CLSC）规划开发了一个强大的优化框架，该框架明确考虑了回收和再制造投入质量的不确定性。虽然这些材料对可持续发展至关重要，但它们的质量变化不定对生产可行性和供应可靠性构成了风险。为了应对这一挑战，我们提出了一个基于排序比例的鲁棒模型，该模型将不确定性分配到采购比例中，并利用不确定性的Bertsimas-Sim预算来平衡保守性和灵活性。重新制定确保可追溯性和保持稳健的可行性。计算实验表明，该模型在独立实现的不确定性下减少了不足并稳定了性能，而基于数量的鲁棒模型在不确定性相关时更有效。额外的可伸缩性测试证实，该模型在计算上仍然适用于中型网络。研究结果强调了对管理者的实际意义，显示了基于比例的采购如何提高弹性，支持可靠的需求满足，并加强面临质量风险的CLSCs的可持续性。

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引用次数: 0