Genetic local search algorithm for a new bi-objective arc routing problem with profit collection and dispersion of vehicles
作者：
Highlights：
• The proposed problem has two objectives: profit collection and vehicles dispersion.
• Vehicles collect rewards for arcs traversed while traveling scattered in environment.
• We propose a Multi-objective Genetic Local Search method to find approximation sets.
• It uses specialized chromosome, genetic operators and local search strategy.
• Our MOGLS presents better approximation sets than a NSGA II implementation.
摘要
•The proposed problem has two objectives: profit collection and vehicles dispersion.•Vehicles collect rewards for arcs traversed while traveling scattered in environment.•We propose a Multi-objective Genetic Local Search method to find approximation sets.•It uses specialized chromosome, genetic operators and local search strategy.•Our MOGLS presents better approximation sets than a NSGA II implementation.
论文关键词：Bi-objective problem,Profit,Dispersion metric,Genetic algorithm,Local search,Synchronized routes
论文评审过程：Received 5 January 2017, Revised 21 September 2017, Accepted 22 September 2017, Available online 23 September 2017, Version of Record 2 October 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.050
