{"title":"ACO and CP Working Together to Build a Flexible Tool for the VRP","authors":"N. ZakeriNejad, D. Riera, Daniel Guimarans","doi":"10.5220/0005668101220129","DOIUrl":null,"url":null,"abstract":"In this paper a flexible hybrid methodology, combining Ant Colony Optimisation (ACO) and Constraint Programming (CP), is presented for solving Vehicle Routing Problems (VRP). The stress of this methodology is on the word ‘flexible’: It gives reasonably good results to changing problems without high solution redesign efforts. Thus a different problem with a new set of constraints and objectives requires no changes to the search algorithm. The search part (driven by ACO) and the model of the problem (included in the CP part) are separated to take advantage of their best attributes. This separation makes the application of the framework to different problems much simpler. To assess the feasibility of our approach, we have used it to solve different instances of the VRP family. These instances are built by combining different sets of constraints. The results obtained are promising but show that the methodology needs deeper communication between ACO and CP to improve its performance.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Operations Research and Enterprise Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0005668101220129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper a flexible hybrid methodology, combining Ant Colony Optimisation (ACO) and Constraint Programming (CP), is presented for solving Vehicle Routing Problems (VRP). The stress of this methodology is on the word ‘flexible’: It gives reasonably good results to changing problems without high solution redesign efforts. Thus a different problem with a new set of constraints and objectives requires no changes to the search algorithm. The search part (driven by ACO) and the model of the problem (included in the CP part) are separated to take advantage of their best attributes. This separation makes the application of the framework to different problems much simpler. To assess the feasibility of our approach, we have used it to solve different instances of the VRP family. These instances are built by combining different sets of constraints. The results obtained are promising but show that the methodology needs deeper communication between ACO and CP to improve its performance.