Pub Date : 2022-02-01DOI: 10.1016/j.cor.2022.105722
Alberto Santini, Ana Viana, Xenia Klimentova, João Pedro Pedroso
{"title":"The Probabilistic Travelling Salesman Problem with Crowdsourcing","authors":"Alberto Santini, Ana Viana, Xenia Klimentova, João Pedro Pedroso","doi":"10.1016/j.cor.2022.105722","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105722","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"18 1","pages":"105722"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88816958","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 : 2022-02-01DOI: 10.1016/j.cor.2022.105718
N. Bagger, E. van der Hurk, Rowan Hoogervorst, David Pisinger
{"title":"Reducing disease spread through optimization: Limiting mixture of the population is more important than limiting group sizes","authors":"N. Bagger, E. van der Hurk, Rowan Hoogervorst, David Pisinger","doi":"10.1016/j.cor.2022.105718","DOIUrl":"https://doi.org/10.1016/j.cor.2022.105718","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"72 1","pages":"105718"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80536138","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}
Mayukh Ghosh, A. Kuiper, Roshan Mahes, Donato Maragno
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1016/j.cor.2021.105392
V. Riahi, M. A. H. Newton, A. Sattar
{"title":"A scatter search algorithm for time-dependent prize-collecting arc routing problems","authors":"V. Riahi, M. A. H. Newton, A. Sattar","doi":"10.1016/j.cor.2021.105392","DOIUrl":"https://doi.org/10.1016/j.cor.2021.105392","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"44 1","pages":"105392"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86491765","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}
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.
{"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}
Pub Date : 2021-09-01DOI: 10.1016/J.COR.2021.105354
Manoel B. Campêlo, Tatiane Fernandes Figueiredo
{"title":"Integer programming approaches to the multiple team formation problem","authors":"Manoel B. Campêlo, Tatiane Fernandes Figueiredo","doi":"10.1016/J.COR.2021.105354","DOIUrl":"https://doi.org/10.1016/J.COR.2021.105354","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"8 1","pages":"105354"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74656219","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 : 2021-09-01DOI: 10.1016/J.COR.2021.105329
David Freud, G. Mosheiov
{"title":"Scheduling with competing agents, total late work and job rejection","authors":"David Freud, G. Mosheiov","doi":"10.1016/J.COR.2021.105329","DOIUrl":"https://doi.org/10.1016/J.COR.2021.105329","url":null,"abstract":"","PeriodicalId":10582,"journal":{"name":"Comput. Oper. Res.","volume":"54 1","pages":"105329"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73863070","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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