Pub Date : 2025-04-07DOI: 10.1016/j.cor.2025.107046
Baruch Mor , Xin-Na Geng
Gerstl et al (2019) studied the problem of minimizing the total late work (TLW) on an -machine proportionate flow shop. They solved the case where the total late work refers to the last operation of the job (i.e., the operation performed on the last machine of the flow shop). As the problem is known to be NP-hard, the authors proved two crucial properties of an optimal schedule and introduced a pseudo-polynomial dynamic programming (DP) algorithm. In this research, we revisit the same problem and present enhanced algorithms by the factor of , where is the number of jobs and is the number of machines. Furthermore, based on the improved algorithm, we extend the fundamental problem to consider optional job rejection. We focus on minimizing the TLW subject to an upper bound on the total rejection cost and introduce DP algorithms. Next, we address the problem of minimizing the TLW with generalized due dates, with an upper bound on the permitted rejection cost, and likewise introduce DP algorithms. We conducted an extensive numerical study to evaluate the efficiency of all DP algorithms.
{"title":"Improved algorithm for minimizing total late work on a proportionate flow shop and extensions to job rejection and generalized due dates","authors":"Baruch Mor , Xin-Na Geng","doi":"10.1016/j.cor.2025.107046","DOIUrl":"10.1016/j.cor.2025.107046","url":null,"abstract":"<div><div>Gerstl et al (2019) studied the problem of minimizing the total late work (TLW) on an <span><math><mi>m</mi></math></span>-machine proportionate flow shop. They solved the case where the total late work refers to the last operation of the job (i.e., the operation performed on the last machine of the flow shop). As the problem is known to be NP-hard, the authors proved two crucial properties of an optimal schedule and introduced a pseudo-polynomial dynamic programming (DP) algorithm. In this research, we revisit the same problem and present enhanced algorithms by the factor of <span><math><mrow><mo>(</mo><mi>n</mi><mo>+</mo><mi>m</mi><mo>)</mo></mrow></math></span>, where <span><math><mi>n</mi></math></span> is the number of jobs and <span><math><mi>m</mi></math></span> is the number of machines. Furthermore, based on the improved algorithm, we extend the fundamental problem to consider optional job rejection. We focus on minimizing the TLW subject to an upper bound on the total rejection cost and introduce DP algorithms. Next, we address the problem of minimizing the TLW with generalized due dates, with an upper bound on the permitted rejection cost, and likewise introduce DP algorithms. We conducted an extensive numerical study to evaluate the efficiency of all DP algorithms.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"181 ","pages":"Article 107046"},"PeriodicalIF":4.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808417","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 : 2025-04-02DOI: 10.1016/j.cor.2025.107057
Ramdhan Nugraha , Adriansyah Dwi Rendragraha , Soo Young Shin
This study explores the integration of electric ground vehicles (EGVs) and drones within the logistics sector to address environmental and operational challenges in transportation. By introducing a novel multi-cooperative EV routing problem with flexible drones (MCEVRPFD), the research leverages both technologies to enhance delivery efficiency and extend the operational range of EGVs. Additionally, the study highlights the potential of blockchain technology to ensure efficiency, security, and transparency in supply chain management, enhancing operational reliability and supporting sustainability. The findings suggest that the combined use of EGVs, drones, and blockchain can revolutionize logistics by offering more sustainable, efficient, and transparent solutions, thereby increasing consumer trust and satisfaction.
{"title":"Integrated electric ground vehicle and drone with blockchain-driven approach for routing delivery","authors":"Ramdhan Nugraha , Adriansyah Dwi Rendragraha , Soo Young Shin","doi":"10.1016/j.cor.2025.107057","DOIUrl":"10.1016/j.cor.2025.107057","url":null,"abstract":"<div><div>This study explores the integration of electric ground vehicles (EGVs) and drones within the logistics sector to address environmental and operational challenges in transportation. By introducing a novel multi-cooperative EV routing problem with flexible drones (MCEVRPFD), the research leverages both technologies to enhance delivery efficiency and extend the operational range of EGVs. Additionally, the study highlights the potential of blockchain technology to ensure efficiency, security, and transparency in supply chain management, enhancing operational reliability and supporting sustainability. The findings suggest that the combined use of EGVs, drones, and blockchain can revolutionize logistics by offering more sustainable, efficient, and transparent solutions, thereby increasing consumer trust and satisfaction.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107057"},"PeriodicalIF":4.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767727","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 : 2025-04-01DOI: 10.1016/j.cor.2025.107061
Naiqi Liu, Wansheng Tang, Yanfei Lan
In this study, we address robust contract design problem, where the principal is ambiguous about effort-contingent multi-output probability distribution of the agent. To model this type of distributionally robust contract design problem, the Wasserstein ambiguity set is employed to characterize the ambiguous multi-output probability distribution. Two decision criteria are adopted by the principal to evaluate the designed robust contract under partial distribution information, the first is the worst-case expected criterion, and the second is the worst-case risk criterion. Furthermore, the concept of distributionally robust incentive compatibility is defined with respect to a pair of ambiguity sets. In virtue of two decision criteria, this paper develops a new reward-risk distributionally robust contract design model as well as its extension models based on different risk measures and globalized distributionally robust incentive compatibility condition. The distributionally robust counterpart and globalized distributionally robust counterpart problems of the developed distributionally robust contract design models are linear programming or mixed-integer linear programming models. According to the structural characteristic of the resulting mixed-integer linear programming model, a new tailored Benders decomposition algorithm is designed. At the end of this paper, an inventory decision with backorder problem is addressed and some numerical experiments are performed to demonstrate the influences of three risk measures on robust optimal contracts. The computational results demonstrate that the developed distributionally robust contract design models can facilitate the principal to make the informed contract decisions.
{"title":"Developing reward-risk aversion distributionally robust contract design models under ambiguous output probabilities","authors":"Naiqi Liu, Wansheng Tang, Yanfei Lan","doi":"10.1016/j.cor.2025.107061","DOIUrl":"10.1016/j.cor.2025.107061","url":null,"abstract":"<div><div>In this study, we address robust contract design problem, where the principal is ambiguous about effort-contingent multi-output probability distribution of the agent. To model this type of distributionally robust contract design problem, the Wasserstein ambiguity set is employed to characterize the ambiguous multi-output probability distribution. Two decision criteria are adopted by the principal to evaluate the designed robust contract under partial distribution information, the first is the worst-case expected criterion, and the second is the worst-case risk criterion. Furthermore, the concept of distributionally robust incentive compatibility is defined with respect to a pair of ambiguity sets. In virtue of two decision criteria, this paper develops a new reward-risk distributionally robust contract design model as well as its extension models based on different risk measures and globalized distributionally robust incentive compatibility condition. The distributionally robust counterpart and globalized distributionally robust counterpart problems of the developed distributionally robust contract design models are linear programming or mixed-integer linear programming models. According to the structural characteristic of the resulting mixed-integer linear programming model, a new tailored Benders decomposition algorithm is designed. At the end of this paper, an inventory decision with backorder problem is addressed and some numerical experiments are performed to demonstrate the influences of three risk measures on robust optimal contracts. The computational results demonstrate that the developed distributionally robust contract design models can facilitate the principal to make the informed contract decisions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107061"},"PeriodicalIF":4.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759494","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 : 2025-03-29DOI: 10.1016/j.cor.2025.107075
Roberto Bargetto , Maria Elena Bruni , Guido Perboli
In the nowadays common two-tier logistic systems for delivering products in urban areas, the last leg of the distribution chain, the so-called last mile, is by far the most problematic. The modeling of the last-mile delivery problem as a variable cost and size bin packing problem with time-dependent costs emerged recently as the best solution for planning the delivery operations. No exact solution has been yet proposed for efficiently solving this variant of the bin packing problem. In this paper, we present a new problem formulation and devise an exact branch-and-bound method for effective and efficient problem resolution. Upon the resulting tailored solution approach, we devise a procedure to speed up the problem resolution further. Numerical results collected on large-sized instances reveal the dramatic reduction of the computation time obtained with our solution approach, which turns out to be up to ten times faster than the commercial solver. The improvement in solving large-sized instances with a relatively easy-to-implement approach is remarkably relevant for real-world applications.
{"title":"An ILP-based exact approach for solving the variable cost and size bin packing problem with time-dependent cost modeling the shared satellite-based last-mile delivery","authors":"Roberto Bargetto , Maria Elena Bruni , Guido Perboli","doi":"10.1016/j.cor.2025.107075","DOIUrl":"10.1016/j.cor.2025.107075","url":null,"abstract":"<div><div>In the nowadays common two-tier logistic systems for delivering products in urban areas, the last leg of the distribution chain, the so-called last mile, is by far the most problematic. The modeling of the last-mile delivery problem as a variable cost and size bin packing problem with time-dependent costs emerged recently as the best solution for planning the delivery operations. No exact solution has been yet proposed for efficiently solving this variant of the bin packing problem. In this paper, we present a new problem formulation and devise an exact branch-and-bound method for effective and efficient problem resolution. Upon the resulting tailored solution approach, we devise a procedure to speed up the problem resolution further. Numerical results collected on large-sized instances reveal the dramatic reduction of the computation time obtained with our solution approach, which turns out to be up to ten times faster than the commercial solver. The improvement in solving large-sized instances with a relatively easy-to-implement approach is remarkably relevant for real-world applications.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107075"},"PeriodicalIF":4.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777497","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 : 2025-03-29DOI: 10.1016/j.cor.2025.107063
Igor Eduardo Santos de Melo, Flavio Sanson Fogliatto
Resource allocation problems are a significant subset of optimization problems widely present in the healthcare industry. Notably, the Operating Room Scheduling Problem (ORSP) is prominent in literature due to its substantial cost and revenue implications. ORSP is structured into three hierarchical decision levels: strategic, tactical and operational, with higher-level decisions influencing subsequent ones. While most studies focus on single decision levels, there has been a growing interest in integrated models, as these offer a more holistic view and often result in better outcomes, despite their complexity of resolution. Although various literature reviews have explored different aspects of ORSP, few have focused on models integrating multiple decision levels. This study addresses that gap by systematically reviewing the literature focusing on contributions presenting mathematical programming models and optimization algorithms that simultaneously address multiple decision levels of the ORSP. Following the PRISMA 2020 protocol, the review spans works published until October 2024, totaling 46 articles. It outlines objectives, characteristics, solution approaches, and identifies gaps in literature. Additionally, a decision framework summarizing the identified integration levels is proposed.
{"title":"Integration of decision levels in operating room scheduling problems: Systematic review and proposition of a decision support framework","authors":"Igor Eduardo Santos de Melo, Flavio Sanson Fogliatto","doi":"10.1016/j.cor.2025.107063","DOIUrl":"10.1016/j.cor.2025.107063","url":null,"abstract":"<div><div>Resource allocation problems are a significant subset of optimization problems widely present in the healthcare industry. Notably, the Operating Room Scheduling Problem (ORSP) is prominent in literature due to its substantial cost and revenue implications. ORSP is structured into three hierarchical decision levels: strategic, tactical and operational, with higher-level decisions influencing subsequent ones. While most studies focus on single decision levels, there has been a growing interest in integrated models, as these offer a more holistic view and often result in better outcomes, despite their complexity of resolution. Although various literature reviews have explored different aspects of ORSP, few have focused on models integrating multiple decision levels. This study addresses that gap by systematically reviewing the literature focusing on contributions presenting mathematical programming models and optimization algorithms that simultaneously address multiple decision levels of the ORSP. Following the PRISMA 2020 protocol, the review spans works published until October 2024, totaling 46 articles. It outlines objectives, characteristics, solution approaches, and identifies gaps in literature. Additionally, a decision framework summarizing the identified integration levels is proposed.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107063"},"PeriodicalIF":4.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759496","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 : 2025-03-29DOI: 10.1016/j.cor.2025.107076
Liping Xu , Tao Zhou , Kai Li , Jianfu Chen , Han Zhang
The variability in the availability of network-shared manufacturing resources and the release times of orders pose challenges to the operational decision-making of industrial internet platforms. This paper addresses these characteristics by studying the identical parallel machine scheduling problem, aiming to minimize total weighted tardiness under constraints of arbitrary release times and multiple machine unavailability periods. To address this research problem, a decoding mechanism based on machine idle periods is first proposed, effectively solving the impact of machine unavailability periods on the scheduling scheme. Secondly, a multi-population cooperative evolutionary algorithm is designed in which the mechanisms of selection, crossover, mutation, and information exchange between populations are improved. The optimal scheduling properties of two jobs on the same machine and different machines are analyzed, resulting in the design of two local search mechanisms. Additionally, Q-learning is introduced to enhance the adaptability of algorithm parameters by dynamically adjusting them within the multi-population cooperative evolutionary algorithm, resulting in a Q-learning-driven multi-population cooperative evolutionary algorithm with local search (Q-MPCEA-LS). Finally, comparative experiments between the Q-MPCEA-LS algorithm and various metaheuristic algorithms are conducted. The experimental results show that, across all instances, the average relative error in the average value metric of the Q-MPCEA-LS algorithm is 40.0%, 0.1%, 44.2%, and 75.9% lower than that of Q-MPCEA-LS without local search, Q-MPCEA-LS without Q-learning-based dynamic parameter adjustment, the iterative hybrid metaheuristic algorithm, and the hybrid genetic immune algorithm, respectively. These results validate the effectiveness of the individual components and the overall effectiveness of the Q-MPCEA-LS algorithm.
{"title":"Q-learning-driven multi-population cooperative evolutionary algorithm with local search for scheduling of network-shared manufacturing resources","authors":"Liping Xu , Tao Zhou , Kai Li , Jianfu Chen , Han Zhang","doi":"10.1016/j.cor.2025.107076","DOIUrl":"10.1016/j.cor.2025.107076","url":null,"abstract":"<div><div>The variability in the availability of network-shared manufacturing resources and the release times of orders pose challenges to the operational decision-making of industrial internet platforms. This paper addresses these characteristics by studying the identical parallel machine scheduling problem, aiming to minimize total weighted tardiness under constraints of arbitrary release times and multiple machine unavailability periods. To address this research problem, a decoding mechanism based on machine idle periods is first proposed, effectively solving the impact of machine unavailability periods on the scheduling scheme. Secondly, a multi-population cooperative evolutionary algorithm is designed in which the mechanisms of selection, crossover, mutation, and information exchange between populations are improved. The optimal scheduling properties of two jobs on the same machine and different machines are analyzed, resulting in the design of two local search mechanisms. Additionally, Q-learning is introduced to enhance the adaptability of algorithm parameters by dynamically adjusting them within the multi-population cooperative evolutionary algorithm, resulting in a Q-learning-driven multi-population cooperative evolutionary algorithm with local search (Q-MPCEA-LS). Finally, comparative experiments between the Q-MPCEA-LS algorithm and various metaheuristic algorithms are conducted. The experimental results show that, across all instances, the average relative error in the average value metric of the Q-MPCEA-LS algorithm is 40.0%, 0.1%, 44.2%, and 75.9% lower than that of Q-MPCEA-LS without local search, Q-MPCEA-LS without Q-learning-based dynamic parameter adjustment, the iterative hybrid metaheuristic algorithm, and the hybrid genetic immune algorithm, respectively. These results validate the effectiveness of the individual components and the overall effectiveness of the Q-MPCEA-LS algorithm.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107076"},"PeriodicalIF":4.1,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739565","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 : 2025-03-28DOI: 10.1016/j.cor.2025.107078
Xiaomin Zhou, Rongrong Li, Zhihui Wu
In the digital transformation of the wooden-door manufacturing industry, material preparation planning and production scheduling directly influence the stability and effectiveness of the manufacturing system. Constructive problem-specific algorithms have been instrumental in solving real-world laminated door machining shop scheduling problem (LDMSSP). LDMSSP is a complex problem that combines a distributed permutation flow-shop scheduling problem and distributed hybrid flow-shop scheduling problem. An improved genetic algorithm fused with the strategies of the improved heuristic algorithm, the local search, variable neighborhood search with multiple critical paths, and the iterated greedy search (IGGA) was proposed for application in the material preparation planning and scheduling optimization to minimize the makespan. Comprehensive design of experiments and statistical analyses were conducted to determine appropriate algorithm parameters and verify the substantial improvement of the IGGA. Experiments conducted on various benchmark instances indicated that IGGA outperformed other metaheuristics in both the best relative deviation index and the average relative deviation index. In the end, the minimal makespan for a real-world case involving the production of 74 laminated doors was 1.1 h with a 17.91% reduction, which further demonstrated the effectiveness of the proposed model and algorithm in solving LDMSSP. It also provided a valuable reference for the rational arrangement of material preparation planning and machining scheduling sequences.
{"title":"Scheduling optimization for laminated door machining shop based on improved genetic algorithm","authors":"Xiaomin Zhou, Rongrong Li, Zhihui Wu","doi":"10.1016/j.cor.2025.107078","DOIUrl":"10.1016/j.cor.2025.107078","url":null,"abstract":"<div><div>In the digital transformation of the wooden-door manufacturing industry, material preparation planning and production scheduling directly influence the stability and effectiveness of the manufacturing system. Constructive problem-specific algorithms have been instrumental in solving real-world laminated door machining shop scheduling problem (LDMSSP). LDMSSP is a complex problem that combines a distributed permutation flow-shop scheduling problem and distributed hybrid flow-shop scheduling problem. An improved genetic algorithm fused with the strategies of the improved heuristic algorithm, the local search, variable neighborhood search with multiple critical paths, and the iterated greedy search (IGGA) was proposed for application in the material preparation planning and scheduling optimization to minimize the makespan. Comprehensive design of experiments and statistical analyses were conducted to determine appropriate algorithm parameters and verify the substantial improvement of the IGGA. Experiments conducted on various benchmark instances indicated that IGGA outperformed other metaheuristics in both the best relative deviation index and the average relative deviation index. In the end, the minimal makespan for a real-world case involving the production of 74 laminated doors was 1.1 h with a 17.91% reduction, which further demonstrated the effectiveness of the proposed model and algorithm in solving LDMSSP. It also provided a valuable reference for the rational arrangement of material preparation planning and machining scheduling sequences.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107078"},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759493","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}
The vessel pool alliance is a prominent cooperation model within the liner shipping industry, where a joint operator manages the collective shipping capacities of all alliance members. The primary challenge for the alliance manager is the efficient allocation of container slots among cargoes with stochastic demand. This study addresses this complex problem by formulating it as a stochastic linear programming model aimed at maximizing the alliance’s total freight revenue while simultaneously ensuring adequate revenue for each member operator, thereby maintaining long-term alliance stability. To solve this problem, we first employ an enhanced Depth-First Search algorithm to identify a set of feasible transportation paths for each cargo. Subsequently, we develop an efficient policy to determine the optimal slot allocation for each realized demand scenario. Numerical experiments using both synthetic and real-world data demonstrate that our proposed policy significantly outperforms the container slot exchange alliance and independent operation modes currently prevalent in practice. Our approach notably enhances revenues for both the alliance as a whole and individual member operators by optimizing the utilization of slot resources.
{"title":"Container slot allocation policy in vessel pool alliance under stochastic demand","authors":"Jinpeng Liang, Yuhang Zhou, Shuang Wang, Jianfeng Zheng","doi":"10.1016/j.cor.2025.107074","DOIUrl":"10.1016/j.cor.2025.107074","url":null,"abstract":"<div><div>The vessel pool alliance is a prominent cooperation model within the liner shipping industry, where a joint operator manages the collective shipping capacities of all alliance members. The primary challenge for the alliance manager is the efficient allocation of container slots among cargoes with stochastic demand. This study addresses this complex problem by formulating it as a stochastic linear programming model aimed at maximizing the alliance’s total freight revenue while simultaneously ensuring adequate revenue for each member operator, thereby maintaining long-term alliance stability. To solve this problem, we first employ an enhanced Depth-First Search algorithm to identify a set of feasible transportation paths for each cargo. Subsequently, we develop an efficient policy to determine the optimal slot allocation for each realized demand scenario. Numerical experiments using both synthetic and real-world data demonstrate that our proposed policy significantly outperforms the container slot exchange alliance and independent operation modes currently prevalent in practice. Our approach notably enhances revenues for both the alliance as a whole and individual member operators by optimizing the utilization of slot resources.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107074"},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746340","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 : 2025-03-27DOI: 10.1016/j.cor.2025.107073
Alan T. Murray
The importance of good locational decision making cannot be understated. In many cases it is quite literally a question of life and death, whether in the context of safety and security or associated with the viability of business activity. As a result, location modeling has become essential in system understanding, designing or extension in whatever way spatial choice is considered. Location modeling too is central in addressing sustainability, resilience, efficiency and effectiveness across a range of urban and environmental contexts. Over the past three decades geographic information systems, and more generally GIScience, has emerged as a critical complement to location modeling. This paper seeks to articulate and demonstrate how GIScience is now a central component of location modeling, one that fundamentally bridges geographic information systems and optimization in many ways. GIScience primitives are formally structured and specified in order to make linkages explicit in the context of location modeling. This is significant as GIScience helps to further establish locational theory and principles that form the basis of model extension as well as enables better solution approaches to be developed. Because of this, continued integration of location modeling and GIS is anticipated in the coming years and decades.
{"title":"Beyond location modeling and GIS: Integration and bridging","authors":"Alan T. Murray","doi":"10.1016/j.cor.2025.107073","DOIUrl":"10.1016/j.cor.2025.107073","url":null,"abstract":"<div><div>The importance of good locational decision making cannot be understated. In many cases it is quite literally a question of life and death, whether in the context of safety and security or associated with the viability of business activity. As a result, location modeling has become essential in system understanding, designing or extension in whatever way spatial choice is considered. Location modeling too is central in addressing sustainability, resilience, efficiency and effectiveness across a range of urban and environmental contexts. Over the past three decades geographic information systems, and more generally GIScience, has emerged as a critical complement to location modeling. This paper seeks to articulate and demonstrate how GIScience is now a central component of location modeling, one that fundamentally bridges geographic information systems and optimization in many ways. GIScience primitives are formally structured and specified in order to make linkages explicit in the context of location modeling. This is significant as GIScience helps to further establish locational theory and principles that form the basis of model extension as well as enables better solution approaches to be developed. Because of this, continued integration of location modeling and GIS is anticipated in the coming years and decades.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107073"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767725","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 : 2025-03-27DOI: 10.1016/j.cor.2025.107072
Josef Grus , Claire Hanen , Zdeněk Hanzálek
Periodic messages transfer data from sensors to actuators in cars, planes, and complex production machines. When considering a given routing, the unicast message starts at its source and goes over several dedicated resources to reach its destination. Such unicast message can be represented as a chain of point-to-point communications. Thus, the scheduling of the periodic chains is a principal problem in time-triggered Ethernet, like IEEE 802.1Qbv Time-Sensitive Networks. This paper studies a strongly NP-hard periodic scheduling problem with harmonic periods, task chains, and dedicated resources. We analyze the problem on several levels and provide proofs of complexity and approximation algorithms for several special cases. We describe a solution methodology to find a feasible schedule that minimizes the chains’ degeneracy related to start-to-end latency normalized in the number of periods. We use the local search with the first fit scheduling heuristic, which we warm-start with a constraint programming model. This notably improves the schedulability of instances with up to 100% utilization and thousands (and more) of tasks, with high-quality solutions found in minutes. An efficient constraint programming matheuristic significantly reduces the degeneracy of the found schedules even further. The method is evaluated on sets of industrial-, avionic-, and automotive-inspired instances.
{"title":"Periodic chains scheduling on dedicated resources - A crucial problem in time-sensitive networks","authors":"Josef Grus , Claire Hanen , Zdeněk Hanzálek","doi":"10.1016/j.cor.2025.107072","DOIUrl":"10.1016/j.cor.2025.107072","url":null,"abstract":"<div><div>Periodic messages transfer data from sensors to actuators in cars, planes, and complex production machines. When considering a given routing, the unicast message starts at its source and goes over several dedicated resources to reach its destination. Such unicast message can be represented as a chain of point-to-point communications. Thus, the scheduling of the periodic chains is a principal problem in time-triggered Ethernet, like IEEE 802.1Qbv Time-Sensitive Networks. This paper studies a strongly NP-hard periodic scheduling problem with harmonic periods, task chains, and dedicated resources. We analyze the problem on several levels and provide proofs of complexity and approximation algorithms for several special cases. We describe a solution methodology to find a feasible schedule that minimizes the chains’ degeneracy related to start-to-end latency normalized in the number of periods. We use the local search with the first fit scheduling heuristic, which we warm-start with a constraint programming model. This notably improves the schedulability of instances with up to 100% utilization and thousands (and more) of tasks, with high-quality solutions found in minutes. An efficient constraint programming matheuristic significantly reduces the degeneracy of the found schedules even further. The method is evaluated on sets of industrial-, avionic-, and automotive-inspired instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107072"},"PeriodicalIF":4.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746341","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}
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