Pub Date : 2025-12-01DOI: 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.
{"title":"A cost and emission optimization framework for strategic intermodal freight transportation infrastructure development","authors":"Ayoub Abusalih, Zeyu Liu","doi":"10.1016/j.orp.2025.100369","DOIUrl":"10.1016/j.orp.2025.100369","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100369"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690179","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}
Pub Date : 2025-12-01DOI: 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.
{"title":"A systematic review of machine learning approaches in inventory control optimization","authors":"Ritsaart Bergsma , Corné de Ruijt , Sandjai Bhulai","doi":"10.1016/j.orp.2025.100367","DOIUrl":"10.1016/j.orp.2025.100367","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100367"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623416","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}
Pub Date : 2025-12-01DOI: 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.
{"title":"Cross-border multi-level warehouse network optimization: Modeling and application based on mixed-integer linear programming","authors":"Yun Gu","doi":"10.1016/j.orp.2025.100366","DOIUrl":"10.1016/j.orp.2025.100366","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100366"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623417","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}
Pub Date : 2025-12-01DOI: 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.
{"title":"Robust optimization model for closed-loop supply chain planning with collected material quality uncertainty","authors":"Joonrak Kim , Seunghoon Lee","doi":"10.1016/j.orp.2025.100368","DOIUrl":"10.1016/j.orp.2025.100368","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100368"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623418","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}
Pub Date : 2025-11-19DOI: 10.1016/j.orp.2025.100363
Luigi Pescio, Marta Ribeiro, Bruno F. Santos
Flight and maintenance scheduling pose conflicting objectives: while maintenance is vital for ensuring aircraft airworthiness, it comes at the cost of taking aircraft out of operation. In current operations, airlines manually handle tail assignment and maintenance task scheduling separately, missing an opportunity to strike a better balance. This division leads to wasted maintenance resources, restricted fleet availability for schedule flexibility, inconsistent planning, and neglect of schedule resilience. This study presents a novel approach that integrates tail assignment and maintenance scheduling into a unified decision-support framework. An integer program, tailored to meet airline-specific requirements and constraints, is combined with an innovative time-space network (TSN). The TSN incorporates two distinct spaces for maintenance and network activities. The primary objective is to generate feasible plans that increase schedule efficiency (i.e., no cancellations, high fleet availability, high fleet health, and optimal use of maintenance resources) and schedule stability (i.e., limited number of late arrival disruptions during operations) the day before operation. Additionally, this framework addresses overlooked aspects in the literature: it treats maintenance tasks as variable interval activities based on aircraft-specific needs, departing from the traditional fixed interval approach. The performance of the framework is tested with real-data provided by a major European single hub-to-spoke airline, with a heterogeneous fleet of over 50 wide-body aircraft. Historical data from arrival delays is used to create robust buffers that mitigate delay propagation. A 17% reduction in maintenance time was achieved compared to the airline’s current plans, resulting in a 10% increase in fleet availability on the day of operations. This improvement is attributed to higher labour and task interval utilization, indicating the framework’s superior efficiency in scheduling maintenance tasks. Lastly, the framework produced plans more resilient to arrival delays, reducing the number of disruptions and delay propagation over 40%. This framework can be used as a decision-support tool for airlines, enabling the creation of schedules that are both robust against delays and optimized for fleet utilization.
{"title":"Unified tail assignment and maintenance task scheduling: A decision support framework for improved efficiency and stability","authors":"Luigi Pescio, Marta Ribeiro, Bruno F. Santos","doi":"10.1016/j.orp.2025.100363","DOIUrl":"10.1016/j.orp.2025.100363","url":null,"abstract":"<div><div>Flight and maintenance scheduling pose conflicting objectives: while maintenance is vital for ensuring aircraft airworthiness, it comes at the cost of taking aircraft out of operation. In current operations, airlines manually handle tail assignment and maintenance task scheduling separately, missing an opportunity to strike a better balance. This division leads to wasted maintenance resources, restricted fleet availability for schedule flexibility, inconsistent planning, and neglect of schedule resilience. This study presents a novel approach that integrates tail assignment and maintenance scheduling into a unified decision-support framework. An integer program, tailored to meet airline-specific requirements and constraints, is combined with an innovative time-space network (TSN). The TSN incorporates two distinct spaces for maintenance and network activities. The primary objective is to generate feasible plans that increase schedule efficiency (i.e., no cancellations, high fleet availability, high fleet health, and optimal use of maintenance resources) and schedule stability (i.e., limited number of late arrival disruptions during operations) the day before operation. Additionally, this framework addresses overlooked aspects in the literature: it treats maintenance tasks as variable interval activities based on aircraft-specific needs, departing from the traditional fixed interval approach. The performance of the framework is tested with real-data provided by a major European single hub-to-spoke airline, with a heterogeneous fleet of over 50 wide-body aircraft. Historical data from arrival delays is used to create robust buffers that mitigate delay propagation. A 17% reduction in maintenance time was achieved compared to the airline’s current plans, resulting in a 10% increase in fleet availability on the day of operations. This improvement is attributed to higher labour and task interval utilization, indicating the framework’s superior efficiency in scheduling maintenance tasks. Lastly, the framework produced plans more resilient to arrival delays, reducing the number of disruptions and delay propagation over 40%. This framework can be used as a decision-support tool for airlines, enabling the creation of schedules that are both robust against delays and optimized for fleet utilization.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100363"},"PeriodicalIF":3.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579142","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}
In this paper, we address the problem of Column Generation (CG) for routing problems using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to find the columns with most negative reduced cost in the Pricing Problem (PP). Unlike previous Machine Learning (ML) applications for CG, our model deploys an end-to-end mechanism that independently solves the pricing problem without the help of any heuristic. We consider a variant of Vehicle Routing Problem (VRP) as a case study for our method. Through a series of experiments comparing our approach with a Dynamic Programming (DP)-based heuristic for solving the PP, we demonstrate that the proposed method obtains solutions for the linear relaxation up to a reasonable objective gap and significantly faster than the DP-based heuristic for the PP.
{"title":"Reinforcement learning for solving the pricing problem in column generation for routing","authors":"Abdo Abouelrous , Laurens Bliek , Adriana F. Gabor , Yaoxin Wu , Yingqian Zhang","doi":"10.1016/j.orp.2025.100364","DOIUrl":"10.1016/j.orp.2025.100364","url":null,"abstract":"<div><div>In this paper, we address the problem of Column Generation (CG) for routing problems using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to find the columns with most negative reduced cost in the Pricing Problem (PP). Unlike previous Machine Learning (ML) applications for CG, our model deploys an end-to-end mechanism that independently solves the pricing problem without the help of any heuristic. We consider a variant of Vehicle Routing Problem (VRP) as a case study for our method. Through a series of experiments comparing our approach with a Dynamic Programming (DP)-based heuristic for solving the PP, we demonstrate that the proposed method obtains solutions for the linear relaxation up to a reasonable objective gap and significantly faster than the DP-based heuristic for the PP.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100364"},"PeriodicalIF":3.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525286","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}
Pub Date : 2025-11-12DOI: 10.1016/j.orp.2025.100365
Hui Yu , Yu Gong , Xiaoli Yan
The newsvendor problem with periodic demand (PFNV) is a common and significant challenge in practice, where traditional methods such as optimization, statistical analysis, and artificial intelligence often struggle to balance effectiveness and operability. We propose the Probability-based Adaptive Strategy (PAS) for the PFNV problem, which formulates decisions through the dual reference points and probabilities. The decision-making process comprises four steps that simulate human behavior based on bounded rationality. The design of reference points is data-driven, using either a linear method or a multi-armed bandit (MAB), while probability calculation is guided by an optimization objective that reflects human regret psychology. The final decision is made through either a random sampling (RS) or an expectation construction (EC) scheme. Experiments with both simulated and real-world data show that PAS effectively captures periodic trends in both stable and volatile datasets. The PAS combining classification, MAB, and the EC scheme performs better in average cost in most cases, while other variants exhibit different characteristics under varying conditions. Compared with several benchmarks, PAS demonstrates potential for cost optimization in certain scenarios while maintaining both operability and interpretability.
{"title":"A Probabilistic and adaptive strategy for the newsvendor problem with periodic demand","authors":"Hui Yu , Yu Gong , Xiaoli Yan","doi":"10.1016/j.orp.2025.100365","DOIUrl":"10.1016/j.orp.2025.100365","url":null,"abstract":"<div><div>The newsvendor problem with periodic demand (PFNV) is a common and significant challenge in practice, where traditional methods such as optimization, statistical analysis, and artificial intelligence often struggle to balance effectiveness and operability. We propose the Probability-based Adaptive Strategy (PAS) for the PFNV problem, which formulates decisions through the dual reference points and probabilities. The decision-making process comprises four steps that simulate human behavior based on bounded rationality. The design of reference points is data-driven, using either a linear method or a multi-armed bandit (MAB), while probability calculation is guided by an optimization objective that reflects human regret psychology. The final decision is made through either a random sampling (RS) or an expectation construction (EC) scheme. Experiments with both simulated and real-world data show that PAS effectively captures periodic trends in both stable and volatile datasets. The PAS combining classification, MAB, and the EC scheme performs better in average cost in most cases, while other variants exhibit different characteristics under varying conditions. Compared with several benchmarks, PAS demonstrates potential for cost optimization in certain scenarios while maintaining both operability and interpretability.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100365"},"PeriodicalIF":3.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579141","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}
Pub Date : 2025-11-03DOI: 10.1016/j.orp.2025.100362
Julius Hoffmann , Janis S. Neufeld , Udo Buscher
Various recent scheduling literature has studied the so called customer order scheduling problem. In this class of scheduling problems, there are multiple customer orders, and each of them consists of several jobs. The order finishes and is ready to be shipped when the last job of the order finishes. In this paper, we consider the customer order scheduling problem in a permutation flow shop environment with machines. There are orders and each order has jobs. The objective is to minimize the total completion time of the orders. We present multiple problem properties and a MINLP formulation of the problem. Furthermore, four heuristics which follow the Iterated Greedy Algorithm are developed. In a computational experiment, we evaluated the four heuristics on their practicability. They showed good results in short calculation time when compared with the MINLP solution from a solver. Afterwards, we compared the four heuristics with each other for different problem sizes.
{"title":"Customer order scheduling in a permutation flow shop environment","authors":"Julius Hoffmann , Janis S. Neufeld , Udo Buscher","doi":"10.1016/j.orp.2025.100362","DOIUrl":"10.1016/j.orp.2025.100362","url":null,"abstract":"<div><div>Various recent scheduling literature has studied the so called customer order scheduling problem. In this class of scheduling problems, there are multiple customer orders, and each of them consists of several jobs. The order finishes and is ready to be shipped when the last job of the order finishes. In this paper, we consider the customer order scheduling problem in a permutation flow shop environment with <span><math><mi>m</mi></math></span> machines. There are <span><math><mi>n</mi></math></span> orders and each order has <span><math><mi>o</mi></math></span> jobs. The objective is to minimize the total completion time of the orders. We present multiple problem properties and a MINLP formulation of the problem. Furthermore, four heuristics which follow the Iterated Greedy Algorithm are developed. In a computational experiment, we evaluated the four heuristics on their practicability. They showed good results in short calculation time when compared with the MINLP solution from a solver. Afterwards, we compared the four heuristics with each other for different problem sizes.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100362"},"PeriodicalIF":3.7,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473768","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}
Pub Date : 2025-11-01DOI: 10.1016/j.orp.2025.100361
Li Zhang, Jianqin Zhou, Xufeng Yang
To ensure the timely supply of relief materials at a low cost, many countries have adopted the Joint Government and Enterprises Storage (JGES) mode to prepositioning relief materials, where some enterprises replace the government in stockpiling emergency supplies for disasters. A critical problem faced by the enterprise is how to manage its inventory considering its daily business demand and the possible emergency demand. The government also wants to know the performance of the mode and how to subsidize the enterprise. To address these questions, we first consider the single-period problem and formulate it as a newsvendor-type model. We obtain the optimal conditions and analyze the impacts of some parameters on the optimal policy. Furthermore, we consider the multi-period case and the government’s optimal subsidy for the enterprise. For the former, we show that the optimal inventory policy is still the base-stock policy if the fixed ordering cost is zero, and is the policy if the cost is positive. The government’s subsidy to the firm increases first and then decreases as the occurrence probability of the emergency increases. Finally, we conduct numerical experiments to compare the performance of the mode with that of the Separate Government-Enterprise Storage (SGES) mode, to demonstrate its advantages and the impacts of some parameters on its performance.
{"title":"Inventory prepositioning of relief material under the Joint Government-Enterprise Storage mode","authors":"Li Zhang, Jianqin Zhou, Xufeng Yang","doi":"10.1016/j.orp.2025.100361","DOIUrl":"10.1016/j.orp.2025.100361","url":null,"abstract":"<div><div>To ensure the timely supply of relief materials at a low cost, many countries have adopted the Joint Government and Enterprises Storage (JGES) mode to prepositioning relief materials, where some enterprises replace the government in stockpiling emergency supplies for disasters. A critical problem faced by the enterprise is how to manage its inventory considering its daily business demand and the possible emergency demand. The government also wants to know the performance of the mode and how to subsidize the enterprise. To address these questions, we first consider the single-period problem and formulate it as a newsvendor-type model. We obtain the optimal conditions and analyze the impacts of some parameters on the optimal policy. Furthermore, we consider the multi-period case and the government’s optimal subsidy for the enterprise. For the former, we show that the optimal inventory policy is still the base-stock policy if the fixed ordering cost is zero, and is the <span><math><mrow><mo>(</mo><mi>s</mi><mo>,</mo><mi>S</mi><mo>)</mo></mrow></math></span> policy if the cost is positive. The government’s subsidy to the firm increases first and then decreases as the occurrence probability of the emergency increases. Finally, we conduct numerical experiments to compare the performance of the mode with that of the Separate Government-Enterprise Storage (SGES) mode, to demonstrate its advantages and the impacts of some parameters on its performance.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100361"},"PeriodicalIF":3.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473767","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}
Pub Date : 2025-10-29DOI: 10.1016/j.orp.2025.100357
Alejandro Arenas-Vasco , Daniela Alcázar , Juan G. Villegas
The Vehicle Routing Problem (VRP) is a cornerstone of operations research with broad real-world relevance. As variants increase in complexity, researchers increasingly adopt metaheuristics and matheuristics to find high-quality solutions. We focus on set partitioning (SP) and set covering (SC) formulations as enhancement mechanisms in matheuristics for VRPs. These methods exploit previously generated routes by decomposing and recombining solutions, either as a post-optimization step or iteratively. We conducted a meta-analysis of 30 implementations, selected from 54 eligible studies identified via systematic review of Web of Science and Scopus, complemented by backward snowballing. Using a random-effects model, we quantified the effect of SP/SC enhancements on solution quality. On average, SP/SC improved solutions by 0.51% (95% CI: 0.41%–0.61%). Although the numerical gains are modest, they are consistent and significant, highlighting the practical value of these classical formulations in hybrid heuristic frameworks.
车辆路径问题（VRP）是运筹学的基础，具有广泛的现实意义。随着变量复杂性的增加，研究人员越来越多地采用元启发式和数学来寻找高质量的解决方案。我们关注集划分（SP）和集覆盖（SC）公式作为vrp数学中的增强机制。这些方法通过分解和重组解决方案来利用先前生成的路线，要么作为后优化步骤，要么迭代。我们对从Web of Science和Scopus系统综述中选出的54项符合条件的研究进行了30项实施的荟萃分析，并辅以反向滚雪球法。使用随机效应模型，我们量化了SP/SC增强对溶液质量的影响。SP/SC平均改善溶液0.51% （95% CI: 0.41%-0.61%）。虽然数值上的收益是适度的，但它们是一致的和重要的，突出了这些经典公式在混合启发式框架中的实用价值。
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