Pub Date : 2025-02-13DOI: 10.1016/j.ejor.2025.02.009
Xuan He, Quan-Ke Pan, Liang Gao, Janis S. Neufeld, Jatinder N.D. Gupta
The challenge of optimizing multiple objectives while considering job groups and partial due dates is prevalent in the flowshop group scheduling problem (FGSP). Despite its significance, the multi-objective FGSP with partial due dates (MFGSP) remains largely unaddressed in existing FGSP literature. In this paper, we bridge this gap by introducing a mixed integer linear programming model and an iterated greedy algorithm tailored for MFGSP with sequence-dependent group setup times, aimed at minimizing both makespan and total tardiness concurrently. Our proposed approach delves into the specific characteristics of times, acknowledging the inherent conflicts between objectives and the unique nature of each objective. We propose two novel local search operators: one inspired by the asymmetric traveling salesman problem and the other based on a domination criterion. These operators are seamlessly integrated into the iterated greedy algorithm framework, augmented with a cone-weighted scalar method as a fitness function and adaptive perturbation parameters. Extensive experimental evaluations demonstrate the efficacy and efficiency of our proposed algorithm, showcasing its capability to solve the MFGSP effectively. Through this research, we contribute a practical and versatile solution to a largely unexplored area in group scheduling optimization.
{"title":"Minimising Makespan and total tardiness for the flowshop group scheduling problem with sequence dependent setup times","authors":"Xuan He, Quan-Ke Pan, Liang Gao, Janis S. Neufeld, Jatinder N.D. Gupta","doi":"10.1016/j.ejor.2025.02.009","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.009","url":null,"abstract":"The challenge of optimizing multiple objectives while considering job groups and partial due dates is prevalent in the flowshop group scheduling problem (FGSP). Despite its significance, the multi-objective FGSP with partial due dates (MFGSP) remains largely unaddressed in existing FGSP literature. In this paper, we bridge this gap by introducing a mixed integer linear programming model and an iterated greedy algorithm tailored for MFGSP with sequence-dependent group setup times, aimed at minimizing both makespan and total tardiness concurrently. Our proposed approach delves into the specific characteristics of times, acknowledging the inherent conflicts between objectives and the unique nature of each objective. We propose two novel local search operators: one inspired by the asymmetric traveling salesman problem and the other based on a domination criterion. These operators are seamlessly integrated into the iterated greedy algorithm framework, augmented with a cone-weighted scalar method as a fitness function and adaptive perturbation parameters. Extensive experimental evaluations demonstrate the efficacy and efficiency of our proposed algorithm, showcasing its capability to solve the MFGSP effectively. Through this research, we contribute a practical and versatile solution to a largely unexplored area in group scheduling optimization.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"22 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.ejor.2025.02.005
Xin Li , Dongya Wu
High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including multivariate regression, system identification and matrix compressed sensing. Current studies mainly consider the idealized case that the covariate matrix is obtained without noise, while the more realistic scenario that the covariates may always be corrupted with noise or missing data has received little attention. We consider the general errors-in-variables matrix regression model and proposed a unified framework for low-rank estimation based on nonconvex spectral regularization. Then from the statistical aspect, recovery bounds for any stationary points are provided to achieve statistical consistency. From the computational aspect, the proximal gradient method is applied to solve the nonconvex optimization problem and is proved to converge to a small neighborhood of the global solution in polynomial time. Consequences for concrete models such as matrix compressed sensing models with additive noise and missing data are obtained via verifying corresponding regularity conditions. Finally, the performance of the proposed nonconvex estimation method is illustrated by numerical experiments on both synthetic and real neuroimaging data.
{"title":"Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression","authors":"Xin Li , Dongya Wu","doi":"10.1016/j.ejor.2025.02.005","DOIUrl":"10.1016/j.ejor.2025.02.005","url":null,"abstract":"<div><div>High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including multivariate regression, system identification and matrix compressed sensing. Current studies mainly consider the idealized case that the covariate matrix is obtained without noise, while the more realistic scenario that the covariates may always be corrupted with noise or missing data has received little attention. We consider the general errors-in-variables matrix regression model and proposed a unified framework for low-rank estimation based on nonconvex spectral regularization. Then from the statistical aspect, recovery bounds for any stationary points are provided to achieve statistical consistency. From the computational aspect, the proximal gradient method is applied to solve the nonconvex optimization problem and is proved to converge to a small neighborhood of the global solution in polynomial time. Consequences for concrete models such as matrix compressed sensing models with additive noise and missing data are obtained via verifying corresponding regularity conditions. Finally, the performance of the proposed nonconvex estimation method is illustrated by numerical experiments on both synthetic and real neuroimaging data.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"323 2","pages":"Pages 626-641"},"PeriodicalIF":6.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.ejor.2025.02.007
Waleed Najy, Claudia Archetti, Ali Diabat
This paper investigates a variant of an inventory-routing problem (IRP) that enforces two conditions on the structure of the solution: time-invariant routes, and a fixed, injective (i.e., one-to-one) assignment of routes to vehicles. The practical benefits of concurrent route invariance and driver assignments are numerous. Fixed routes reduce the solution space of the problem and improve its tractability; they simplify operations; and they increase the viability of newer delivery technologies like drones and autonomous vehicles. Consistency between driver and customer is linked to improved service, driver job satisfaction and delivery efficiency, and is also an important consideration in certain contexts like home healthcare. After formulating the problem as a mixed integer-linear program, we recast it as a set partitioning problem whose linear relaxation is solved via column generation. Due to the prohibitively expensive nature of the pricing problem that generates new columns, we present a novel column generation-based heuristic for it that relies on decoupling routing and inventory management decisions. We demonstrate the effectiveness of the proposed method via a numerical study.
{"title":"A column generation-based matheuristic for an inventory-routing problem with driver-route consistency","authors":"Waleed Najy, Claudia Archetti, Ali Diabat","doi":"10.1016/j.ejor.2025.02.007","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.007","url":null,"abstract":"This paper investigates a variant of an inventory-routing problem (IRP) that enforces two conditions on the structure of the solution: time-invariant routes, and a fixed, injective (i.e., one-to-one) assignment of routes to vehicles. The practical benefits of concurrent route invariance and driver assignments are numerous. Fixed routes reduce the solution space of the problem and improve its tractability; they simplify operations; and they increase the viability of newer delivery technologies like drones and autonomous vehicles. Consistency between driver and customer is linked to improved service, driver job satisfaction and delivery efficiency, and is also an important consideration in certain contexts like home healthcare. After formulating the problem as a mixed integer-linear program, we recast it as a set partitioning problem whose linear relaxation is solved via column generation. Due to the prohibitively expensive nature of the pricing problem that generates new columns, we present a novel column generation-based heuristic for it that relies on decoupling routing and inventory management decisions. We demonstrate the effectiveness of the proposed method via a numerical study.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"121 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 introduce a novel, profit-driven classification approach for churn prevention by framing the task of targeting customers for a retention campaign as a regret minimization problem within a predict-and-optimize framework. This is the first churn prevention model to utilize this approach. Our main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs, often resulting in significant information loss due to data aggregation. Our proposed model aligns with the principles of the predict-and-optimize framework and can be efficiently solved using stochastic gradient descent methods. Results from 13 churn prediction datasets, sourced from an investment company, underscore the effectiveness of our approach, which achieves the highest average performance in terms of profit compared to other well-established strategies.
{"title":"A predict-and-optimize approach to profit-driven churn prevention","authors":"Nuria Gómez-Vargas, Sebastián Maldonado, Carla Vairetti","doi":"10.1016/j.ejor.2025.02.008","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.008","url":null,"abstract":"In this paper, we introduce a novel, profit-driven classification approach for churn prevention by framing the task of targeting customers for a retention campaign as a regret minimization problem within a predict-and-optimize framework. This is the first churn prevention model to utilize this approach. Our main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs, often resulting in significant information loss due to data aggregation. Our proposed model aligns with the principles of the predict-and-optimize framework and can be efficiently solved using stochastic gradient descent methods. Results from 13 churn prediction datasets, sourced from an investment company, underscore the effectiveness of our approach, which achieves the highest average performance in terms of profit compared to other well-established strategies.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"129 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.ejor.2025.02.006
F. Carrabs, R. Cerulli, R. Mansini, D. Serra, C. Sorgente
This work addresses a variant of the maximum flow problem where specific pairs of arcs are not allowed to carry positive flow simultaneously. Such restrictions are known in the literature as negative disjunctive constraints or conflict constraints. The problem is known to be strongly NP-hard and several exact approaches have been proposed in the literature. In this paper, we present a heuristic algorithm for the problem, based on two different approaches: Carousel Greedy and Kernel Search. These two approaches are merged to obtain a fast and effective matheuristic, named Kernousel. In particular, the computational results reveal that exploiting the information gathered by the Carousel Greedy to build the set of most promising variables (the kernel set), makes the Kernel Search more effective. To validate the performance of the new hybrid method, we compare it with the two components running individually. Results are also evaluated against the best-known solutions available in the literature for the problem. The new hybrid method provides 15 new best-known values on benchmark instances.
{"title":"Hybridizing Carousel Greedy and Kernel Search: A new approach for the maximum flow problem with conflict constraints","authors":"F. Carrabs, R. Cerulli, R. Mansini, D. Serra, C. Sorgente","doi":"10.1016/j.ejor.2025.02.006","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.006","url":null,"abstract":"This work addresses a variant of the maximum flow problem where specific pairs of arcs are not allowed to carry positive flow simultaneously. Such restrictions are known in the literature as <ce:italic>negative disjunctive constraints</ce:italic> or <ce:italic>conflict constraints</ce:italic>. The problem is known to be strongly NP-hard and several exact approaches have been proposed in the literature. In this paper, we present a heuristic algorithm for the problem, based on two different approaches: Carousel Greedy and Kernel Search. These two approaches are merged to obtain a fast and effective matheuristic, named Kernousel. In particular, the computational results reveal that exploiting the information gathered by the Carousel Greedy to build the set of most promising variables (the <ce:italic>kernel set</ce:italic>), makes the Kernel Search more effective. To validate the performance of the new hybrid method, we compare it with the two components running individually. Results are also evaluated against the best-known solutions available in the literature for the problem. The new hybrid method provides 15 new best-known values on benchmark instances.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"85 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.ejor.2025.02.003
Victor Gonzalez, Patrick Jaillet
Natural disasters are recurring emergencies that can result in numerous deaths and injuries. When a natural disaster occurs, rescue teams can be sent to help affected survivors, but deploying them efficiently is a challenge. Rescuers not knowing where affected survivors are located poses a significant challenge in delivering aid. With the development of new technologies, there are new possibilities to reduce this uncertainty, alleviating this challenge. One can first send out automated drones to locate affected survivors and then send rescue teams to their locations. We develop a model for the search process and construct mathematical methods to construct efficient search routes. We utilize a divide and conquer technique to determine the routes that are most likely to yield an efficient search. We combine this with our mathematical methods to construct efficient search routes in real-time and a method to update these routes in real-time as drones gather information.
{"title":"Multi-drone rescue search in a large network","authors":"Victor Gonzalez, Patrick Jaillet","doi":"10.1016/j.ejor.2025.02.003","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.003","url":null,"abstract":"Natural disasters are recurring emergencies that can result in numerous deaths and injuries. When a natural disaster occurs, rescue teams can be sent to help affected survivors, but deploying them efficiently is a challenge. Rescuers not knowing where affected survivors are located poses a significant challenge in delivering aid. With the development of new technologies, there are new possibilities to reduce this uncertainty, alleviating this challenge. One can first send out automated drones to locate affected survivors and then send rescue teams to their locations. We develop a model for the search process and construct mathematical methods to construct efficient search routes. We utilize a divide and conquer technique to determine the routes that are most likely to yield an efficient search. We combine this with our mathematical methods to construct efficient search routes in real-time and a method to update these routes in real-time as drones gather information.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"21 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.ejor.2025.02.001
Stefan Voigt, Markus Frank, Heinrich Kuhn
E-commerce retailers are challenged to maintain cost-efficiency and customer satisfaction while pursuing sustainability, especially in the last mile. In response, retailers are offering a range of delivery speeds, including same-day and instant options. Faster deliveries, while trending, often increase costs and emissions due to limited planning time and reduced consolidation opportunities in the last mile. In contrast, this paper proposes the inclusion of a slower delivery option, termed some-day. Slowing down the delivery process allows for greater shipment consolidation, achieving cost savings and environmental goals simultaneously. We introduce the dynamic and stochastic some-day delivery problem, which accounts for a latest delivery day, customer time windows, and capacity limitations within a multi-period planning framework. Our solution approach is based on addressing auxiliary prize-collecting vehicle routing problems with time windows (PCVRPTW) on a daily basis, where the prize reflects the benefit of promptly serving the customer. We develop a hybrid adaptive large neighborhood search with granular insertion operators, outperforming existing metaheuristics for PCVRPTWs. Our numerical study shows significant cost savings with only small increases in delivery times compared to an earliest policy.
{"title":"Last mile delivery routing problem with some-day option","authors":"Stefan Voigt, Markus Frank, Heinrich Kuhn","doi":"10.1016/j.ejor.2025.02.001","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.001","url":null,"abstract":"E-commerce retailers are challenged to maintain cost-efficiency and customer satisfaction while pursuing sustainability, especially in the last mile. In response, retailers are offering a range of delivery speeds, including same-day and instant options. Faster deliveries, while trending, often increase costs and emissions due to limited planning time and reduced consolidation opportunities in the last mile. In contrast, this paper proposes the inclusion of a slower delivery option, termed some-day. Slowing down the delivery process allows for greater shipment consolidation, achieving cost savings and environmental goals simultaneously. We introduce the dynamic and stochastic some-day delivery problem, which accounts for a latest delivery day, customer time windows, and capacity limitations within a multi-period planning framework. Our solution approach is based on addressing auxiliary prize-collecting vehicle routing problems with time windows (PCVRPTW) on a daily basis, where the prize reflects the benefit of promptly serving the customer. We develop a hybrid adaptive large neighborhood search with granular insertion operators, outperforming existing metaheuristics for PCVRPTWs. Our numerical study shows significant cost savings with only small increases in delivery times compared to an earliest policy.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.ejor.2025.01.023
Guanxiang Yun, Qipeng P. Zheng, Lihui Bai, Eduardo L. Pasiliao
This paper studies residential users’ electricity consumption in response to dynamic pricing in smart grid, from the behavioral economics angle. Particularly, this research employs a novel approach, i.e., the boundedly rational user decision (BRUD) modeling framework, which assumes users will accept an electricity consumption schedule whose total utility is within an “acceptable bound” of his/her maximum utility. Subsequently, we study the optimistic and pessimistic/robust pricing models via bi-level optimization, where the utility company minimizes the total system cost while considering the best and worst case under BRUD, respectively. In order to solve the pricing models efficiently, we present two methods, i.e., the penalty method and the Lagrangian-dual method, both with the help of cutting planes. Computational results show that the proposed dynamic pricing schemes are effective in decreasing the total system cost. Furthermore, the Lagrangian-dual-based method outperforms the penalty method due to the exploitation of the special convexity structure of the problem. Finally, when compared to solutions without considering the irrationality of user behavior, solutions from the BRUD models are more efficient in reducing total system cost. Furthermore, the additional benefits gained from considering irrationality tend to grow when user’s monetary value of convenience increases.
{"title":"Robust pricing for demand response under bounded rationality in residential electricity distribution","authors":"Guanxiang Yun, Qipeng P. Zheng, Lihui Bai, Eduardo L. Pasiliao","doi":"10.1016/j.ejor.2025.01.023","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.01.023","url":null,"abstract":"This paper studies residential users’ electricity consumption in response to dynamic pricing in smart grid, from the behavioral economics angle. Particularly, this research employs a novel approach, <ce:italic>i.e</ce:italic>., the boundedly rational user decision (BRUD) modeling framework, which assumes users will accept an electricity consumption schedule whose total utility is within an “acceptable bound” of his/her maximum utility. Subsequently, we study the optimistic and pessimistic/robust pricing models via bi-level optimization, where the utility company minimizes the total system cost while considering the best and worst case under BRUD, respectively. In order to solve the pricing models efficiently, we present two methods, <ce:italic>i.e</ce:italic>., the penalty method and the Lagrangian-dual method, both with the help of cutting planes. Computational results show that the proposed dynamic pricing schemes are effective in decreasing the total system cost. Furthermore, the Lagrangian-dual-based method outperforms the penalty method due to the exploitation of the special convexity structure of the problem. Finally, when compared to solutions without considering the irrationality of user behavior, solutions from the BRUD models are more efficient in reducing total system cost. Furthermore, the additional benefits gained from considering irrationality tend to grow when user’s monetary value of convenience increases.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"32 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1016/j.ejor.2025.02.002
Steffen Elting, Jan Fabian Ehmke, Margaretha Gansterer
To improve routing efficiency, transport service providers can enter a horizontal transport collaboration that uses a combinatorial auction to reallocate delivery orders. To find the optimal allocation, the carriers have to report bids for all possible combinations of available delivery orders. As this number grows exponentially with the number of orders to be reallocated, they are faced with an enormous computational challenge. To lift this burden, the auctioneer may offer only a limited set of order combinations. However, selecting this limited set is itself a stochastic combinatorial optimization problem known as the Bundle Selection Problem. In contrast to previous one-shot approaches to solve this problem, in this paper, a partial preference learning scheme is applied that iteratively queries carriers’ valuations, uses their responses to train preference models and then uses these fitted models to estimate valuations for new combinations of orders. This work investigates different ways to realize such a concept and analyzes their respective improvement in collaboration gains. The results indicate that the suggested algorithm can yield travel time savings of up to 20% higher than those achieved by a random benchmark and up to 10% higher than those of a literature benchmark if at least 40 query-response pairs are considered.
{"title":"Preference learning for efficient bundle selection in horizontal transport collaborations","authors":"Steffen Elting, Jan Fabian Ehmke, Margaretha Gansterer","doi":"10.1016/j.ejor.2025.02.002","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.02.002","url":null,"abstract":"To improve routing efficiency, transport service providers can enter a horizontal transport collaboration that uses a combinatorial auction to reallocate delivery orders. To find the optimal allocation, the carriers have to report bids for all possible combinations of available delivery orders. As this number grows exponentially with the number of orders to be reallocated, they are faced with an enormous computational challenge. To lift this burden, the auctioneer may offer only a limited set of order combinations. However, selecting this limited set is itself a stochastic combinatorial optimization problem known as the Bundle Selection Problem. In contrast to previous one-shot approaches to solve this problem, in this paper, a partial preference learning scheme is applied that iteratively queries carriers’ valuations, uses their responses to train preference models and then uses these fitted models to estimate valuations for new combinations of orders. This work investigates different ways to realize such a concept and analyzes their respective improvement in collaboration gains. The results indicate that the suggested algorithm can yield travel time savings of up to 20% higher than those achieved by a random benchmark and up to 10% higher than those of a literature benchmark if at least 40 query-response pairs are considered.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"31 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-08DOI: 10.1016/j.ejor.2025.01.044
Bahadır Pamuk, Temel Öncan, İ. Kuban Altınel
In this work we study an extension of the ordinary one-to-many shortest path problem that also considers additional disjunctive conflict relations between the arcs: an optimal shortest path tree is not allowed to include any conflicting arc pair. As is the case with many polynomially solvable combinatorial optimization problems, the addition of conflict relations makes the problem NP-hard. We propose a novel branch-and-bound algorithm, which benefits from the solution of the one-to-many shortest path relaxations, an efficient primal–dual reoptimization scheme and a fast infeasibility detection procedure for pruning the branch-and-bound tree. According to the extensive computational tests it is possible to say that the novel algorithm is very efficient.
{"title":"An efficient branch-and-bound algorithm for the one-to-many shortest path problem with additional disjunctive conflict constraints","authors":"Bahadır Pamuk, Temel Öncan, İ. Kuban Altınel","doi":"10.1016/j.ejor.2025.01.044","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.01.044","url":null,"abstract":"In this work we study an extension of the ordinary one-to-many shortest path problem that also considers additional disjunctive conflict relations between the arcs: an optimal shortest path tree is not allowed to include any conflicting arc pair. As is the case with many polynomially solvable combinatorial optimization problems, the addition of conflict relations makes the problem <mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mi mathvariant=\"script\">NP</mml:mi></mml:math>-hard. We propose a novel branch-and-bound algorithm, which benefits from the solution of the one-to-many shortest path relaxations, an efficient primal–dual reoptimization scheme and a fast infeasibility detection procedure for pruning the branch-and-bound tree. According to the extensive computational tests it is possible to say that the novel algorithm is very efficient.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"15 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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