Pub Date : 2025-12-01Epub Date: 2025-12-03DOI: 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.
{"title":"Group-scheduling with simultaneous learning effects and convex resource allocations","authors":"Xue Huang , Hongyu He , Hong-Bin Bei , Yanzhi Zhao , Ning Wang , Yu Chang","doi":"10.1016/j.orp.2025.100370","DOIUrl":"10.1016/j.orp.2025.100370","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100370"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747419","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 : 2025-12-01Epub Date: 2025-06-28DOI: 10.1016/j.orp.2025.100344
Lucas Assunção, Andréa Cynthia Santos
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 uncertainty budget 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 kernel search mat-heuristic and a simulated annealing 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.
{"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":"2025-12-01","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}
Pub Date : 2025-12-01Epub Date: 2025-07-23DOI: 10.1016/j.orp.2025.100349
Chunlai Yu, Xiaoming Wang, Qingxin Chen
In this paper we consider the resource-constrained project scheduling problem with a flexible project structure and continuous activity durations. A mathematical model based on the resource-flow formulation is developed to tackle this problem. Due to the NP-hard nature of the problem, this mathematical model can only be used to find the optimal solution to small-scale problems. To address this issue, an enhanced tabu search algorithm is proposed, which utilizes an outer loop for activity selection and an inner loop for activity sequencing. The algorithm introduces several innovative features, including the integration of filtering, elite, and perturbation strategies, as well as new neighborhood operators. The parameters of the algorithm are calibrated using orthogonal experiments, and its efficacy is evaluated through extensive computational experiments conducted on multiple benchmark datasets. The results indicate that the proposed tabu search algorithm not only performs significantly better and more stable than existing metaheuristics, but also surpasses the performance of the traditional mathematical model based on rounded durations.
{"title":"An enhanced tabu search algorithm for resource-constrained project scheduling with a flexible project structure","authors":"Chunlai Yu, Xiaoming Wang, Qingxin Chen","doi":"10.1016/j.orp.2025.100349","DOIUrl":"10.1016/j.orp.2025.100349","url":null,"abstract":"<div><div>In this paper we consider the resource-constrained project scheduling problem with a flexible project structure and continuous activity durations. A mathematical model based on the resource-flow formulation is developed to tackle this problem. Due to the NP-hard nature of the problem, this mathematical model can only be used to find the optimal solution to small-scale problems. To address this issue, an enhanced tabu search algorithm is proposed, which utilizes an outer loop for activity selection and an inner loop for activity sequencing. The algorithm introduces several innovative features, including the integration of filtering, elite, and perturbation strategies, as well as new neighborhood operators. The parameters of the algorithm are calibrated using orthogonal experiments, and its efficacy is evaluated through extensive computational experiments conducted on multiple benchmark datasets. The results indicate that the proposed tabu search algorithm not only performs significantly better and more stable than existing metaheuristics, but also surpasses the performance of the traditional mathematical model based on rounded durations.</div></div>","PeriodicalId":38055,"journal":{"name":"Operations Research Perspectives","volume":"15 ","pages":"Article 100349"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703948","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":"2025-12-01","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 : 2025-12-01Epub Date: 2025-06-28DOI: 10.1016/j.orp.2025.100345
Andrés Felipe Porto , Amaia Lusa , Sebastián A. Herazo , César Augusto Henao
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.
{"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":"2025-12-01","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}
Pub Date : 2025-12-01Epub Date: 2025-11-19DOI: 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 : 2025-12-01Epub Date: 2025-11-19DOI: 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 : 2025-12-01Epub Date: 2025-06-25DOI: 10.1016/j.orp.2025.100346
Muhai Hu , Yao Wang , Wendi Tian
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.
{"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":"2025-12-01","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}
Pub Date : 2025-12-01Epub 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":"2025-12-01","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}
Pub Date : 2025-12-01Epub 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":"2025-12-01","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}
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