Comput. Oper. Res.最新文献_第3页

A MIP-based heuristic for a single trade routing and scheduling problem in roll-on roll-off shipping 基于mip的滚装运输单贸易路线和调度问题的启发式算法

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105904

Jone R. Hansen, K. Fagerholt, F. Meisel

引用次数: 1

Logic-based Benders decomposition with a partial assignment acceleration technique for avionics scheduling 航电调度中基于逻辑的Benders分解与部分分配加速技术

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105916

Emil Karlsson, Elina Rönnberg

引用次数: 5

A fuzzy correlation based heuristic for Dual-mode integrated Location routing problem 基于模糊关联的双模集成位置路由启发式算法

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105923

Chang Lv, Chaoyong Zhang, Yaping Ren, Leilei Meng

引用次数: 3

Mathematical programming formulations and metaheuristics for biological sample transportation problems in healthcare 医疗保健中生物样本运输问题的数学规划公式和元启发式

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105921

Mario Benini, P. Detti, Garazi Zabalo Manrique de Lara

引用次数: 2

Target encirclement of moving ride-hailing vehicle under uncertain environment: A multi-vehicle mutual rescue model 不确定环境下移动网约车目标包围:一种多车互救模型

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105901

Dongyu Luo, Jiangfeng Wang, Wenqi Lu, Lei Chen, Zhijun Gao, Jiakuan Dong

引用次数: 1

Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks 城市突发疫情应急物资配送路径优化研究

Comput. Oper. Res. Pub Date : 2022-06-01 DOI: 10.1016/j.cor.2022.105912

Haishi Liu, Yuxuan Sun, Nan Pan, Yi Li, Yuqiang An, Dilin Pan

引用次数: 13

Exact and approximate determination of the Pareto set using minimal correction subsets 用最小校正子集精确近似地确定帕累托集

Comput. Oper. Res. Pub Date : 2022-04-14 DOI: 10.48550/arXiv.2204.06908

Andreia P. Guerreiro, João Cortes, D. Vanderpooten, C. Bazgan, I. Lynce, Vasco M. Manquinho, J. Figueira

{"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}

引用次数: 1

Comput. Oper. Res. Pub Date : 2022-04-01 DOI: 10.1016/j.cor.2022.105847

K. Hu, Yuxin Che, Zhenzhen Zhang

引用次数: 10

Multi-objective optimization of the 3D container stowage planning problem in a barge convoy system 驳船运输系统三维集装箱积载规划问题的多目标优化

Comput. Oper. Res. Pub Date : 2022-03-01 DOI: 10.1016/j.cor.2022.105796

Amina El Yaagoubi, Mohamed Charhbili, J. Boukachour, A. Alaoui

引用次数: 4

Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes 采矿复合体同步随机优化的学习调度启发式

Comput. Oper. Res. Pub Date : 2022-02-25 DOI: 10.2139/ssrn.4229477

Yassine Yaakoubi, R. Dimitrakopoulos

{"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}

引用次数: 2