Comput. Oper. Res.最新文献_第4页

The Probabilistic Travelling Salesman Problem with Crowdsourcing 基于众包的概率旅行商问题

Comput. Oper. Res. Pub Date : 2022-02-01 DOI: 10.1016/j.cor.2022.105722

Alberto Santini, Ana Viana, Xenia Klimentova, João Pedro Pedroso

引用次数: 11

Reducing disease spread through optimization: Limiting mixture of the population is more important than limiting group sizes 通过优化减少疾病传播:限制种群的混合比限制群体规模更重要

Comput. Oper. Res. Pub Date : 2022-02-01 DOI: 10.1016/j.cor.2022.105718

N. Bagger, E. van der Hurk, Rowan Hoogervorst, David Pisinger

引用次数: 4

A survey of job shop scheduling problem: The types and models 作业车间调度问题综述:类型与模型

Comput. Oper. Res. Pub Date : 2022-01-01 DOI: 10.1016/j.cor.2022.105731

Hegen Xiong, Shuangyuan Shi, Danni Ren, Jinjin Hu

引用次数: 43

An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times 具有序列依赖设置时间的分布式置换流水车间调度问题的进化策略方法

Comput. Oper. Res. Pub Date : 2022-01-01 DOI: 10.1016/j.cor.2022.105733

Korhan Karabulut, Hande Öztop, Damla Kizilay, M. Tasgetiren, Levent Kandiller

引用次数: 13

The Sample Analysis Machine Scheduling Problem: Definition and comparison of exact solving approaches 样品分析机调度问题:精确求解方法的定义和比较

Comput. Oper. Res. Pub Date : 2022-01-01 DOI: 10.1016/j.cor.2022.105730

Miquel Bofill, Jordi Coll, G. Martín, Josep Suy, Mateu Villaret

引用次数: 3

Learn global and optimize local: A data-driven methodology for last-mile routing 学习全局和优化本地:最后一英里路由的数据驱动方法

Comput. Oper. Res. Pub Date : 2021-12-03 DOI: 10.2139/ssrn.4341533

Mayukh Ghosh, A. Kuiper, Roshan Mahes, Donato Maragno

{"title":"Learn global and optimize local: A data-driven methodology for last-mile routing","authors":"Mayukh Ghosh, A. Kuiper, Roshan Mahes, Donato Maragno","doi":"10.2139/ssrn.4341533","DOIUrl":"https://doi.org/10.2139/ssrn.4341533","url":null,"abstract":"In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone - a geographical area. First, on the global level, a zone sequence is established as a result of a minimization over a cost matrix which is a weighted combination of historical information and distances (travel times) between zones. Subsequently, within zones, sequences of stops are determined, such that, integrated with the predetermined zone sequence, a full solution is obtained. The methodology is particularly promising as it propels itself within the top-tier of submissions to the Last-Mile Routing Research Challenge, while it maintains an elegant decomposition that ensures a feasible implementation into practice. The concurrence between prescribed and realized routes underpins the adequateness of a hierarchical breakdown of the problem and the fact that drivers make a series of locally optimal decisions when navigating. Furthermore, experimenting with the balance between historical information and distance exposes that historic information is pivotal in deciding a starting zone of a route. The experiments also reveal that at the end of a route, historical information can best be discarded, making the time it takes to return to the station the primary concern.","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"323 1","pages":"106312"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80311516","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

A scatter search algorithm for time-dependent prize-collecting arc routing problems 时变集奖弧布线问题的散射搜索算法

Comput. Oper. Res. Pub Date : 2021-10-01 DOI: 10.1016/j.cor.2021.105392

V. Riahi, M. A. H. Newton, A. Sattar

引用次数: 2

Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands 具有高度可变客户基础和随机需求的车辆路径问题的离线近似动态规划

Comput. Oper. Res. Pub Date : 2021-09-21 DOI: 10.2139/ssrn.4251159

M. Dastpak, F. Errico, O. Jabali

{"title":"Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands","authors":"M. Dastpak, F. Errico, O. Jabali","doi":"10.2139/ssrn.4251159","DOIUrl":"https://doi.org/10.2139/ssrn.4251159","url":null,"abstract":"We study a stochastic variant of the vehicle routing problem arising in the context of domestic donor collection services. The problem we consider combines the following attributes. Customers requesting services are variable, in the sense that the customers are stochastic but are not restricted to a predefined set, as they may appear anywhere in a given service area. Furthermore, demand volumes are stochastic and observed upon visiting the customer. The objective is to maximize the expected served demands while meeting vehicle capacity and time restrictions. We call this problem the VRP with a highly Variable Customer basis and Stochastic Demands (VRP-VCSD). For this problem, we first propose a Markov Decision Process (MDP) formulation representing the classical centralized decision-making perspective where one decision-maker establishes the routes of all vehicles. While the resulting formulation turns out to be intractable, it provides us with the ground to develop a new MDP formulation, which we call partially decentralized. In this formulation, the action-space is decomposed by vehicle. However, the decentralization is incomplete as we enforce identical vehicle-specific policies while optimizing the collective reward. We propose several strategies to reduce the dimension of the state and action spaces associated with the partially decentralized formulation. These yield a considerably more tractable problem, which we solve via Reinforcement Learning. In particular, we develop a Q-learning algorithm called DecQN, featuring state-of-the-art acceleration techniques. We conduct a thorough computational analysis. Results show that DecQN considerably outperforms three benchmark policies. Moreover, we show that our approach can compete with specialized methods developed for the particular case of the VRP-VCSD, where customer locations and expected demands are known in advance.","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"18 11","pages":"106338"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91468356","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: 0

Integer programming approaches to the multiple team formation problem 多队形成问题的整数规划方法

Comput. Oper. Res. Pub Date : 2021-09-01 DOI: 10.1016/J.COR.2021.105354

Manoel B. Campêlo, Tatiane Fernandes Figueiredo

引用次数: 4

Scheduling with competing agents, total late work and job rejection 与竞争代理的日程安排，总迟到和工作拒绝

Comput. Oper. Res. Pub Date : 2021-09-01 DOI: 10.1016/J.COR.2021.105329

David Freud, G. Mosheiov

引用次数: 9