A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

Erfan Babaee Tirkolaee, Ali Asghar Rahmani Hosseinabadi, Mehdi Soltani, Arun Kumar Sangaiah and Jin Wang
Additional contact information
Erfan Babaee Tirkolaee: Department of Industrial Engineering, Mazandaran University of Science and Technology, 47166-85635 Babol, Iran
Ali Asghar Rahmani Hosseinabadi: Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, 46351-43358 Amol, Iran
Mehdi Soltani: Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, 34185-1416 Qazvin, Iran
Arun Kumar Sangaiah: School of Computing Science and Engineering, Vellore Institute of Technology (VIT), 632014 Vellore, India
Jin Wang: School of Computer & Communication Engineering, Changsha University of Science & Technology, 410004 Changsha, China

Sustainability, 2018, vol. 10, issue 5, 1-21

Abstract: Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy.

Keywords: green capacitated arc routing; hybrid genetic algorithm; greenhouse gases; sensitivity analysis; multiple trips (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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