Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107000
André Luís Barroso Almeida , Joubert de Castro Lima , Marco Antonio Moreira Carvalho
This study explores the parallel tempering method, an approach that has shown promising results in simulation and is suited for modern multiprocessor platforms. Though relatively unexplored in operations research, the algorithm has significant potential. The study evaluates a newly developed single-node CPU-based parallel implementation of parallel tempering in three case studies involving challenging operations research problems. The evaluation considers both the quality of solutions and response time. The study also proposes an API containing the implementation of multi-core parallel tempering to encourage its use and facilitate future implementations. Evaluation results confirm the effectiveness of parallel tempering, demonstrating significant performance against state-of-the-art methods.
{"title":"Revisiting the parallel tempering algorithm: High-performance computing and applications in operations research","authors":"André Luís Barroso Almeida , Joubert de Castro Lima , Marco Antonio Moreira Carvalho","doi":"10.1016/j.cor.2025.107000","DOIUrl":"10.1016/j.cor.2025.107000","url":null,"abstract":"<div><div>This study explores the parallel tempering method, an approach that has shown promising results in simulation and is suited for modern multiprocessor platforms. Though relatively unexplored in operations research, the algorithm has significant potential. The study evaluates a newly developed single-node CPU-based parallel implementation of parallel tempering in three case studies involving challenging operations research problems. The evaluation considers both the quality of solutions and response time. The study also proposes an API containing the implementation of multi-core parallel tempering to encourage its use and facilitate future implementations. Evaluation results confirm the effectiveness of parallel tempering, demonstrating significant performance against state-of-the-art methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107000"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107008
Juntao Zhao , Mhand Hifi
In scenarios where different groups of users need to receive the same broadcast or in social media platforms where user interactions form distinct clusters, addressing the problems of minimizing communication channels or understanding user communities can be critical. These problems are modeled as the -clustering minimum biclique completion problem, which is recognized as an NP-hard combinatorial optimization problem. This paper presents a novel approach to solving such a problem through reinforcement learning-enhanced variable neighborhood search. The proposed method features an innovative strategy based on probability learning. It integrates a range of neighborhood techniques with a tabu strategy to enable an exploration of the search space. A key aspect of the method is its implementation of a perturbation procedure through probability learning, which significantly enhances the iterative process by guiding the search towards previously unexplored and promising regions. Experimental evaluations on benchmark instances from existing literature highlight the method’s robustness and high competitiveness. The results reveal its superior performance compared to leading solvers such as Cplex and other recently published methods. Additionally, statistical analyses, including the sign test and the Wilcoxon signed-rank test, are conducted to determine the most effective approach among those tested. These analyses confirm that the method not only achieves new performance bounds but also shows its ability to deliver promising solutions.
{"title":"Reinforcement learning-enhanced variable neighborhood search strategies for the k-clustering minimum biclique completion problem","authors":"Juntao Zhao , Mhand Hifi","doi":"10.1016/j.cor.2025.107008","DOIUrl":"10.1016/j.cor.2025.107008","url":null,"abstract":"<div><div>In scenarios where different groups of users need to receive the same broadcast or in social media platforms where user interactions form distinct clusters, addressing the problems of minimizing communication channels or understanding user communities can be critical. These problems are modeled as the <span><math><mi>k</mi></math></span>-clustering minimum biclique completion problem, which is recognized as an NP-hard combinatorial optimization problem. This paper presents a novel approach to solving such a problem through reinforcement learning-enhanced variable neighborhood search. The proposed method features an innovative strategy based on probability learning. It integrates a range of neighborhood techniques with a tabu strategy to enable an exploration of the search space. A key aspect of the method is its implementation of a perturbation procedure through probability learning, which significantly enhances the iterative process by guiding the search towards previously unexplored and promising regions. Experimental evaluations on benchmark instances from existing literature highlight the method’s robustness and high competitiveness. The results reveal its superior performance compared to leading solvers such as Cplex and other recently published methods. Additionally, statistical analyses, including the sign test and the Wilcoxon signed-rank test, are conducted to determine the most effective approach among those tested. These analyses confirm that the method not only achieves new performance bounds but also shows its ability to deliver promising solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107008"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107009
Zhaoyang Zhang, Jianwu Xue
Feature selection (FS) in high-dimensional classification is challenging due to the exponential increase in the number of possible feature subsets and the increased risk of model overfitting. Focusing on this challenge, the cooperative co-evolutionary algorithm (CCEA), which adopts a divide-and-conquer strategy, has been successfully applied to FS. However, existing CCEA based FS methods risk getting trapped in pseudo-optimum. To address this issue, we propose a novel CCEA based FS method, which can manipulate the context vector to escape pseudo-optimum. Specifically, competitive swarm optimizer (CSO), a variant of particle swarm optimization (PSO), is extended into CCEA based CSO due to its high performance in high-dimensional problems. This forms the foundation of the proposed method. Subsequently, a non-parametric space division strategy is proposed, which enables the proposed method to adaptively handle both low-dimensional and high-dimensional data. Most importantly, a context vector enhancement strategy is proposed, which performs search across all subspaces of the context vector, enabling proposed method to escape pseudo-optimum. Following this, a subpopulation enhancement strategy is proposed, which generates new individuals according to the search results of context vector enhancement strategy to replace individuals with poor fitness, accelerating the evolution of subpopulations. We compare the proposed method with a few state-of-the-art FS methods on 18 datasets with up to 12600 features. Experimental results show that, in most cases, the proposed method is able to find a smaller feature subset with higher classification accuracy.
{"title":"A novel cooperative co-evolutionary algorithm with context vector enhancement strategy for feature selection on high-dimensional classification","authors":"Zhaoyang Zhang, Jianwu Xue","doi":"10.1016/j.cor.2025.107009","DOIUrl":"10.1016/j.cor.2025.107009","url":null,"abstract":"<div><div>Feature selection (FS) in high-dimensional classification is challenging due to the exponential increase in the number of possible feature subsets and the increased risk of model overfitting. Focusing on this challenge, the cooperative co-evolutionary algorithm (CCEA), which adopts a divide-and-conquer strategy, has been successfully applied to FS. However, existing CCEA based FS methods risk getting trapped in pseudo-optimum. To address this issue, we propose a novel CCEA based FS method, which can manipulate the context vector to escape pseudo-optimum. Specifically, competitive swarm optimizer (CSO), a variant of particle swarm optimization (PSO), is extended into CCEA based CSO due to its high performance in high-dimensional problems. This forms the foundation of the proposed method. Subsequently, a non-parametric space division strategy is proposed, which enables the proposed method to adaptively handle both low-dimensional and high-dimensional data. Most importantly, a context vector enhancement strategy is proposed, which performs search across all subspaces of the context vector, enabling proposed method to escape pseudo-optimum. Following this, a subpopulation enhancement strategy is proposed, which generates new individuals according to the search results of context vector enhancement strategy to replace individuals with poor fitness, accelerating the evolution of subpopulations. We compare the proposed method with a few state-of-the-art FS methods on 18 datasets with up to 12600 features. Experimental results show that, in most cases, the proposed method is able to find a smaller feature subset with higher classification accuracy.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107009"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107002
Sarah E. Marshall , Mahsa Mohaghegh
Women remain significantly underrepresented in the fields of science, technology, engineering and mathematics (STEM), but positive mentoring relationships can help mitigate the challenges they face when studying and working in these areas. To support female university students in STEM, the Auckland University of Technology (AUT) established the Women in Tech mentorship programme in 2019. Initially, the matching of mentees and mentors was achieved manually, but as the programme’s popularity grew, this process became increasingly time consuming. This study addresses the challenges associated with assigning mentees to mentors by automating the matching process based on mentee and mentor attributes. A rule-based heuristic is proposed and compared with a linear programming (LP) approach. Numerical experiments were conducted to evaluate the performance of these algorithms across various scenarios. The rule-based heuristic provides a simple and easily understandable way to allocate mentees and mentors that performs nearly as well as an optimal matching provided by the LP approach. Applying these algorithms to real data from the AUT Women in Tech mentorship programme, it was found that they outperformed manual matching in several performance metrics.
{"title":"Comparison of a rule-based heuristic and a linear programming model for assigning mentees and mentors in a women in technology mentoring programme","authors":"Sarah E. Marshall , Mahsa Mohaghegh","doi":"10.1016/j.cor.2025.107002","DOIUrl":"10.1016/j.cor.2025.107002","url":null,"abstract":"<div><div>Women remain significantly underrepresented in the fields of science, technology, engineering and mathematics (STEM), but positive mentoring relationships can help mitigate the challenges they face when studying and working in these areas. To support female university students in STEM, the Auckland University of Technology (AUT) established the Women in Tech mentorship programme in 2019. Initially, the matching of mentees and mentors was achieved manually, but as the programme’s popularity grew, this process became increasingly time consuming. This study addresses the challenges associated with assigning mentees to mentors by automating the matching process based on mentee and mentor attributes. A rule-based heuristic is proposed and compared with a linear programming (LP) approach. Numerical experiments were conducted to evaluate the performance of these algorithms across various scenarios. The rule-based heuristic provides a simple and easily understandable way to allocate mentees and mentors that performs nearly as well as an optimal matching provided by the LP approach. Applying these algorithms to real data from the AUT Women in Tech mentorship programme, it was found that they outperformed manual matching in several performance metrics.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107002"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107005
Sara Ali , António Galrão Ramos , José Fernando Oliveira
In online three-dimensional packing problems where items are received one by one and require immediate packing decisions without prior knowledge of upcoming items, considering the static stability constraint is crucial for safely packing each arriving item in real time. Unstable loading patterns can result in risks of potential damage to items, containers, and operators during loading/unloading operations. Nevertheless, static stability constraints have often been neglected or oversimplified in existing online heuristic methods in the literature, undermining the practical implementation of these methods in real-world scenarios. In this study, we analyze how different static stability constraints affect solutions’ efficiency and cargo stability, aiming to provide valuable insights and develop heuristic algorithms for real-world online problems, thus increasing the applicability of this research field. To this end, we embedded four distinct static stability constraints in online heuristics, including full-base support, partial-base support, center-of-gravity polygon support, and novel partial-base polygon support. Evaluating the impact of these constraints on the efficiency of a wide range of heuristic methods on real instances showed that regarding the number of used bins, heuristics with polygon-based stabilities have superior performance against those under full-base and partial-base support stabilities. The “static mechanical equilibrium” approach offers a necessary and sufficient condition for the cargo static stability, and we employed it as a benchmark in our study to assess the quality of the four studied stability constraints. Knowing the number of stable items under each of these constraints provides valuable managerial insight for decision-making in real-world online packing scenarios.
{"title":"Static stability versus packing efficiency in online three-dimensional packing problems: A new approach and a computational study","authors":"Sara Ali , António Galrão Ramos , José Fernando Oliveira","doi":"10.1016/j.cor.2025.107005","DOIUrl":"10.1016/j.cor.2025.107005","url":null,"abstract":"<div><div>In online three-dimensional packing problems where items are received one by one and require immediate packing decisions without prior knowledge of upcoming items, considering the static stability constraint is crucial for safely packing each arriving item in real time. Unstable loading patterns can result in risks of potential damage to items, containers, and operators during loading/unloading operations. Nevertheless, static stability constraints have often been neglected or oversimplified in existing online heuristic methods in the literature, undermining the practical implementation of these methods in real-world scenarios. In this study, we analyze how different static stability constraints affect solutions’ efficiency and cargo stability, aiming to provide valuable insights and develop heuristic algorithms for real-world online problems, thus increasing the applicability of this research field. To this end, we embedded four distinct static stability constraints in online heuristics, including full-base support, partial-base support, center-of-gravity polygon support, and novel partial-base polygon support. Evaluating the impact of these constraints on the efficiency of a wide range of heuristic methods on real instances showed that regarding the number of used bins, heuristics with polygon-based stabilities have superior performance against those under full-base and partial-base support stabilities. The “static mechanical equilibrium” approach offers a necessary and sufficient condition for the cargo static stability, and we employed it as a benchmark in our study to assess the quality of the four studied stability constraints. Knowing the number of stable items under each of these constraints provides valuable managerial insight for decision-making in real-world online packing scenarios.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107005"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.cor.2025.107007
Hongyuan Luo , Tao Ma , Zhendong Li
This paper addresses a cold chain transportation problem derived from a real-life situation, namely a time-dependent pickup and delivery problem with commodity incompatibility constraints (TDPDPI). In TDPDPI, the travel speed of these vehicles varies with the departure time, that is, the time-dependent travel speed. The quality delay cost of the perishable commodity also varies with the departure time, namely the time-dependent quality delay cost. The cost of this problem consists of two components: one is related to the total travel time, and the other is related to the quality delay cost of the perishable commodity. To solve TDPDPI, this paper develops an arc-based (mixed integer programming, MIP) model solved by CPLEX and a route-based (set-partitioning formulation, SPF) model. To address the SPF model, this paper proposes an exact branch-price-and-cut (BPC) algorithm. A specialized bidirectional labeling algorithm is developed to address the pricing problem. Additionally, subset row cuts (SRC) are employed as valid inequalities to enhance the quality of the lower bound in the SPF model. Extensive computational experiments are conducted to evaluate the efficacy of the proposed BPC algorithm. The results demonstrate that the BPC algorithm effectively solves the problem with 50 requests. Finally, this study conducts a sensitivity analysis of the key constraints and parameters of the model, providing valuable managerial insights.
{"title":"An exact branch-price-and-cut algorithm for the time-dependent cold chain pickup and delivery problem with incompatibility constraints","authors":"Hongyuan Luo , Tao Ma , Zhendong Li","doi":"10.1016/j.cor.2025.107007","DOIUrl":"10.1016/j.cor.2025.107007","url":null,"abstract":"<div><div>This paper addresses a cold chain transportation problem derived from a real-life situation, namely a time-dependent pickup and delivery problem with commodity incompatibility constraints (TDPDPI). In TDPDPI, the travel speed of these vehicles varies with the departure time, that is, the time-dependent travel speed. The quality delay cost of the perishable commodity also varies with the departure time, namely the time-dependent quality delay cost. The cost of this problem consists of two components: one is related to the total travel time, and the other is related to the quality delay cost of the perishable commodity. To solve TDPDPI, this paper develops an arc-based (mixed integer programming, MIP) model solved by CPLEX and a route-based (set-partitioning formulation, SPF) model. To address the SPF model, this paper proposes an exact branch-price-and-cut (BPC) algorithm. A specialized bidirectional labeling algorithm is developed to address the pricing problem. Additionally, subset row cuts (SRC) are employed as valid inequalities to enhance the quality of the lower bound in the SPF model. Extensive computational experiments are conducted to evaluate the efficacy of the proposed BPC algorithm. The results demonstrate that the BPC algorithm effectively solves the problem with 50 requests. Finally, this study conducts a sensitivity analysis of the key constraints and parameters of the model, providing valuable managerial insights.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107007"},"PeriodicalIF":4.1,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.cor.2025.107003
Marta Baldomero-Naranjo , Jörg Kalcsics , Antonio M. Rodríguez-Chía
We tackle the downgrading maximal covering location problem within a network. In this problem, two actors with conflicting objectives are involved: (a) The location planner aims to determine the location of facilities to maximize the covered demand while anticipating that an attacker will attempt to reduce coverage by increasing the length of some edges (downgrade); (b) The attacker seeks to maximize the demand initially covered by the facilities but left uncovered after the downgrade. The attacker can increase the length of certain edges within a specified budget.
We introduce a mixed-integer linear bilevel program to formulate the problem, followed by a preprocessing phase and a matheuristic algorithm designed to address it. Additionally, computational results are presented to illustrate the potential and limitations of the proposed algorithm.
{"title":"Edge downgrades in the maximal covering location problem","authors":"Marta Baldomero-Naranjo , Jörg Kalcsics , Antonio M. Rodríguez-Chía","doi":"10.1016/j.cor.2025.107003","DOIUrl":"10.1016/j.cor.2025.107003","url":null,"abstract":"<div><div>We tackle the downgrading maximal covering location problem within a network. In this problem, two actors with conflicting objectives are involved: (a) The location planner aims to determine the location of facilities to maximize the covered demand while anticipating that an attacker will attempt to reduce coverage by increasing the length of some edges (downgrade); (b) The attacker seeks to maximize the demand initially covered by the facilities but left uncovered after the downgrade. The attacker can increase the length of certain edges within a specified budget.</div><div>We introduce a mixed-integer linear bilevel program to formulate the problem, followed by a preprocessing phase and a matheuristic algorithm designed to address it. Additionally, computational results are presented to illustrate the potential and limitations of the proposed algorithm.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107003"},"PeriodicalIF":4.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.cor.2025.106996
Xia Zhang, Shuang Zeng
The explosion of e-commerce has led to a continued increase in CO2 emissions in logistics. In this article, we explore the role of collaboration between drones and trucks in logistics in mitigating environmental pollution. We propose a mathematical model of drone-assisted truck service that considers customers’ simultaneous pickup and delivery needs within a specific time window. We name it the drone-assisted truck simultaneous pickup and delivery problem considering time windows (DASPDPTW), the objective is to minimize the total CO2 emissions. The energy consumption of the drone is calculated based on a load-based energy model. We design an improved adaptive large neighborhood search (IALNS) algorithm to solve large instances, which contains some special operators for the DASPDPTW. Numerical experiments have verified the effectiveness of IALNS, and drone-assisted trucks can effectively alleviate environmental pollution. Logistics companies can consider incorporating drones into delivery systems to promote supply chain sustainability.
{"title":"The drone-assisted simultaneous pickup and delivery problem with time windows","authors":"Xia Zhang, Shuang Zeng","doi":"10.1016/j.cor.2025.106996","DOIUrl":"10.1016/j.cor.2025.106996","url":null,"abstract":"<div><div>The explosion of e-commerce has led to a continued increase in CO<sub>2</sub> emissions in logistics. In this article, we explore the role of collaboration between drones and trucks in logistics in mitigating environmental pollution. We propose a mathematical model of drone-assisted truck service that considers customers’ simultaneous pickup and delivery needs within a specific time window. We name it the drone-assisted truck simultaneous pickup and delivery problem considering time windows (DASPDPTW), the objective is to minimize the total CO<sub>2</sub> emissions. The energy consumption of the drone is calculated based on a load-based energy model. We design an improved adaptive large neighborhood search (IALNS) algorithm to solve large instances, which contains some special operators for the DASPDPTW. Numerical experiments have verified the effectiveness of IALNS, and drone-assisted trucks can effectively alleviate environmental pollution. Logistics companies can consider incorporating drones into delivery systems to promote supply chain sustainability.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 106996"},"PeriodicalIF":4.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.cor.2025.107004
Robert Aboolian, Majid Karimi
This paper introduces the Benefit Maximization Network Design Problem (BNDP), an innovative framework aimed at optimizing public service systems. By integrating consumer choice models with a focus on minimizing consumer disutility, the BNDP strategically maximizes societal welfare by optimizing the net benefits derived from service utilization, carefully weighing the costs of service capacity against the value delivered to the public. An essential aspect of this study is the development of an -optimal solution approach, which effectively addresses the intricate optimization challenges inherent in public service system design, including nonlinear equilibrium conditions. This approach efficiently identifies the optimal number, locations, and capacities for service facilities, ensuring practical and effective solutions despite the model’s complexity. A practical application of the BNDP is demonstrated through a detailed case study on preventive healthcare in San Diego, showcasing the model’s ability to refine operational solutions and significantly enhance societal benefits. The results underscore the importance of prioritizing benefit maximization over simplistic capacity thresholds and participation maximization, leading to either higher levels of consumer participation while using optimal levels of resources or comparable levels of customer participation with significantly fewer resources and reasonable utilization rates. Furthermore, the paper explores the model’s applicability in the private sector to maximize corporate profits, highlighting its versatility. The study concludes by advocating for policymakers to prioritize the use of BNDP in establishing optimal budgets for public agencies rather than allocating resources based on exogenous capacities.
{"title":"Benefit Maximizing Network Design in the public sector","authors":"Robert Aboolian, Majid Karimi","doi":"10.1016/j.cor.2025.107004","DOIUrl":"10.1016/j.cor.2025.107004","url":null,"abstract":"<div><div>This paper introduces the Benefit Maximization Network Design Problem (BNDP), an innovative framework aimed at optimizing public service systems. By integrating consumer choice models with a focus on minimizing consumer disutility, the BNDP strategically maximizes societal welfare by optimizing the net benefits derived from service utilization, carefully weighing the costs of service capacity against the value delivered to the public. An essential aspect of this study is the development of an <span><math><mi>ϵ</mi></math></span>-optimal solution approach, which effectively addresses the intricate optimization challenges inherent in public service system design, including nonlinear equilibrium conditions. This approach efficiently identifies the optimal number, locations, and capacities for service facilities, ensuring practical and effective solutions despite the model’s complexity. A practical application of the BNDP is demonstrated through a detailed case study on preventive healthcare in San Diego, showcasing the model’s ability to refine operational solutions and significantly enhance societal benefits. The results underscore the importance of prioritizing benefit maximization over simplistic capacity thresholds and participation maximization, leading to either higher levels of consumer participation while using optimal levels of resources or comparable levels of customer participation with significantly fewer resources and reasonable utilization rates. Furthermore, the paper explores the model’s applicability in the private sector to maximize corporate profits, highlighting its versatility. The study concludes by advocating for policymakers to prioritize the use of BNDP in establishing optimal budgets for public agencies rather than allocating resources based on exogenous capacities.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"178 ","pages":"Article 107004"},"PeriodicalIF":4.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-06DOI: 10.1016/j.cor.2025.107001
Ning Xue, Huan Jin, Tianxiang Cui
This paper provides a solution to the driver break scheduling problem that can be integrated into current solvers for the generalised pick-up and delivery problem (GPDP) with a negligible increase in development costs. The driver break scheduling problem affects truck drivers who are required to adhere to working time and/or driving time regulations that oblige them to take mandatory breaks. These regulations are in force in many countries across the European Union, Great Britain, Australia, New Zealand, and the United States. We propose a flexible solution strategy that combines routing with driver break scheduling, taking into account both Regulations (EC)561/2006 and 2002/15/EC within a single day of operation. We deliberately incorporate driver breaks into the routes generated, with the aim of minimising route duration. The method proposed in this paper is particularly beneficial for the solvers that have not initially implemented driver break rules but need to introduce this functionality to accommodate customers, given the critical nature of driver break rules for effective scheduling. This is a flexible approach because the method proposed here can be applied directly to any individual route and at any stage of the scheduling process. For instance, it can be used during the constructive heuristic or the improvement heuristic phases. It is also applicable at the column generation stage of an exact method after a route has been constructed. This method is particularly beneficial when a human scheduler wishes to make manual adjustments along a single route, such as adding or removing an order from the route. Experimental analyses, grounded in two benchmark instances, affirm the efficacy of the proposed method. Results indicate that the method yields a 6.1% decrease in distance, a 1.7% reduction in duty time, and a 1.1% decrease in the number of unassigned tasks when contrasted with the standard compliant approach. We also offer guidance on how to tailor this method for driver break scheduling in accordance with the regulatory standards of Australia and New Zealand.
{"title":"Routing and driver break scheduling with working and driving regulations: A flexible approach for various general pickup and delivery problem variants","authors":"Ning Xue, Huan Jin, Tianxiang Cui","doi":"10.1016/j.cor.2025.107001","DOIUrl":"10.1016/j.cor.2025.107001","url":null,"abstract":"<div><div>This paper provides a solution to the driver break scheduling problem that can be integrated into current solvers for the generalised pick-up and delivery problem (GPDP) with a negligible increase in development costs. The driver break scheduling problem affects truck drivers who are required to adhere to working time and/or driving time regulations that oblige them to take mandatory breaks. These regulations are in force in many countries across the European Union, Great Britain, Australia, New Zealand, and the United States. We propose a flexible solution strategy that combines routing with driver break scheduling, taking into account both Regulations (EC)561/2006 and 2002/15/EC within a single day of operation. We deliberately incorporate driver breaks into the routes generated, with the aim of minimising route duration. The method proposed in this paper is particularly beneficial for the solvers that have not initially implemented driver break rules but need to introduce this functionality to accommodate customers, given the critical nature of driver break rules for effective scheduling. This is a flexible approach because the method proposed here can be applied directly to any individual route and at any stage of the scheduling process. For instance, it can be used during the constructive heuristic or the improvement heuristic phases. It is also applicable at the column generation stage of an exact method after a route has been constructed. This method is particularly beneficial when a human scheduler wishes to make manual adjustments along a single route, such as adding or removing an order from the route. Experimental analyses, grounded in two benchmark instances, affirm the efficacy of the proposed method. Results indicate that the method yields a 6.1% decrease in distance, a 1.7% reduction in duty time, and a 1.1% decrease in the number of unassigned tasks when contrasted with the standard compliant approach. We also offer guidance on how to tailor this method for driver break scheduling in accordance with the regulatory standards of Australia and New Zealand.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"177 ","pages":"Article 107001"},"PeriodicalIF":4.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号