Pub Date : 2022-06-01DOI: 10.1016/j.cor.2022.105904
Jone R. Hansen, K. Fagerholt, F. Meisel
{"title":"A MIP-based heuristic for a single trade routing and scheduling problem in roll-on roll-off shipping","authors":"Jone R. Hansen, K. Fagerholt, F. Meisel","doi":"10.1016/j.cor.2022.105904","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105904","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"88 1","pages":"105904"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85832100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.cor.2022.105916
Emil Karlsson, Elina Rönnberg
{"title":"Logic-based Benders decomposition with a partial assignment acceleration technique for avionics scheduling","authors":"Emil Karlsson, Elina Rönnberg","doi":"10.1016/j.cor.2022.105916","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105916","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"49 1","pages":"105916"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79775283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.cor.2022.105921
Mario Benini, P. Detti, Garazi Zabalo Manrique de Lara
{"title":"Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare","authors":"Mario Benini, P. Detti, Garazi Zabalo Manrique de Lara","doi":"10.1016/j.cor.2022.105921","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105921","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"8 1","pages":"105921"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72732997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1016/j.cor.2022.105912
Haishi Liu, Yuxuan Sun, Nan Pan, Yi Li, Yuqiang An, Dilin Pan
{"title":"Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks","authors":"Haishi Liu, Yuxuan Sun, Nan Pan, Yi Li, Yuqiang An, Dilin Pan","doi":"10.1016/j.cor.2022.105912","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105912","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"11 1","pages":"105912"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85131890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-14DOI: 10.48550/arXiv.2204.06908
Andreia P. Guerreiro, João Cortes, D. Vanderpooten, C. Bazgan, I. Lynce, Vasco M. Manquinho, J. Figueira
Recently, it has been shown that the enumeration of Minimal Correction Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean Optimization (MOBO) formulations. However, a major drawback of this approach is that most MCSs do not correspond to Pareto-optimal solutions. In fact, one can only know that a given MCS corresponds to a Pareto-optimal solution when all MCSs are enumerated. Moreover, if it is not possible to enumerate all MCSs, then there is no guarantee of the quality of the approximation of the Pareto frontier. This paper extends the state of the art for solving MOBO using MCSs. First, we show that it is possible to use MCS enumeration to solve MOBO problems such that each MCS necessarily corresponds to a Pareto-optimal solution. Additionally, we also propose two new algorithms that can find a (1 + {varepsilon})-approximation of the Pareto frontier using MCS enumeration. Experimental results in several benchmark sets show that the newly proposed algorithms allow finding better approximations of the Pareto frontier than state-of-the-art algorithms, and with guaranteed approximation ratios.
{"title":"Exact and approximate determination of the Pareto set using minimal correction subsets","authors":"Andreia P. Guerreiro, João Cortes, D. Vanderpooten, C. Bazgan, I. Lynce, Vasco M. Manquinho, J. Figueira","doi":"10.48550/arXiv.2204.06908","DOIUrl":"https://doi.org/10.48550/arXiv.2204.06908","url":null,"abstract":"Recently, it has been shown that the enumeration of Minimal Correction Subsets (MCS) of Boolean formulas allows solving Multi-Objective Boolean Optimization (MOBO) formulations. However, a major drawback of this approach is that most MCSs do not correspond to Pareto-optimal solutions. In fact, one can only know that a given MCS corresponds to a Pareto-optimal solution when all MCSs are enumerated. Moreover, if it is not possible to enumerate all MCSs, then there is no guarantee of the quality of the approximation of the Pareto frontier. This paper extends the state of the art for solving MOBO using MCSs. First, we show that it is possible to use MCS enumeration to solve MOBO problems such that each MCS necessarily corresponds to a Pareto-optimal solution. Additionally, we also propose two new algorithms that can find a (1 + {varepsilon})-approximation of the Pareto frontier using MCS enumeration. Experimental results in several benchmark sets show that the newly proposed algorithms allow finding better approximations of the Pareto frontier than state-of-the-art algorithms, and with guaranteed approximation ratios.","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"41 2 1","pages":"106153"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83556608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1016/j.cor.2022.105796
Amina El Yaagoubi, Mohamed Charhbili, J. Boukachour, A. Alaoui
{"title":"Multi-objective optimization of the 3D container stowage planning problem in a barge convoy system","authors":"Amina El Yaagoubi, Mohamed Charhbili, J. Boukachour, A. Alaoui","doi":"10.1016/j.cor.2022.105796","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105796","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"3 1","pages":"105796"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84845812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mines and their processing using interconnected facilities to generate a set of final products, while taking into account material supply (geological) uncertainty to manage the associated risk. Although simulated annealing has been shown to outperform comparing methods for solving the SSOMC, early performance might dominate recent performance in that a combination of the heuristics' performance is used to determine which perturbations to apply. This work proposes a data-driven framework for heuristic scheduling in a fully self-managed hyper-heuristic to solve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is a multi-neighborhood simulated annealing algorithm. The L2P selects the heuristic (perturbation) to be applied in a self-adaptive manner using reinforcement learning to efficiently explore which local search is best suited for a particular search point. Several state-of-the-art agents have been incorporated into L2P to better adapt the search and guide it towards better solutions. By learning from data describing the performance of the heuristics, a problem-specific ordering of heuristics that collectively finds better solutions faster is obtained. L2P is tested on several real-world mining complexes, with an emphasis on efficiency, robustness, and generalization capacity. Results show a reduction in the number of iterations by 30-50% and in the computational time by 30-45%.
{"title":"Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes","authors":"Yassine Yaakoubi, R. Dimitrakopoulos","doi":"10.2139/ssrn.4229477","DOIUrl":"https://doi.org/10.2139/ssrn.4229477","url":null,"abstract":"The simultaneous stochastic optimization of mining complexes (SSOMC) is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mines and their processing using interconnected facilities to generate a set of final products, while taking into account material supply (geological) uncertainty to manage the associated risk. Although simulated annealing has been shown to outperform comparing methods for solving the SSOMC, early performance might dominate recent performance in that a combination of the heuristics' performance is used to determine which perturbations to apply. This work proposes a data-driven framework for heuristic scheduling in a fully self-managed hyper-heuristic to solve the SSOMC. The proposed learn-to-perturb (L2P) hyper-heuristic is a multi-neighborhood simulated annealing algorithm. The L2P selects the heuristic (perturbation) to be applied in a self-adaptive manner using reinforcement learning to efficiently explore which local search is best suited for a particular search point. Several state-of-the-art agents have been incorporated into L2P to better adapt the search and guide it towards better solutions. By learning from data describing the performance of the heuristics, a problem-specific ordering of heuristics that collectively finds better solutions faster is obtained. L2P is tested on several real-world mining complexes, with an emphasis on efficiency, robustness, and generalization capacity. Results show a reduction in the number of iterations by 30-50% and in the computational time by 30-45%.","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"54 1","pages":"106349"},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74078013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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