Parseh Designers Transportation Research Institute Quarterly Journal of Transportation Engineering 2008-6598 11 4 2020 06 21 Solving green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods using genetic algorithm Solving green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods using genetic algorithm 937 969 87203 10.22119/jte.2020.87203 FA Adel Aazami Ph D. Candidate, Department of Industrial Engineering, Iran University of Science and Technology, Tehran Mohammad Saidi Mehrabad Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran Journal Article 2018 07 01 Vehicle routing problem (VRP) is about finding optimal routes for a fleet of vehicles so that they can meet the demands for a set of given customers by traveling through those paths. This problem is one of the most important and most applicable problems of transportation and logistics scope. In this paper, green vehicle routing and scheduling problem with a heterogeneous fleet, including reverse logistics in the form of collecting returned goods along with weighted earliness and tardiness costs considering multiple time windows, is studied to establish a trade-off between operational and environmental costs. In this regard, a mixed-integer non-linear programming (MINLP) model is proposed at the first stage; then its accuracy and correct functioning are evaluated by solving some examples. Demand is considered uncertain based on fuzzy numbers that robust possibilistic programming is employed regarding the other parameters uncertainty. Since this problem is categorized as an NP-hard problem, a genetic algorithm (GA) is suggested to find near-optimal solutions for large instances in a rational computational time. Eventually, the GA’s performance is evaluated compared to solving the mathematical model for small-sized problems. Analysis of the results considering two criteria, solutions quality and computational times, indicates the satisfactory operation of the proposed algorithm in a proper computational time.   Vehicle routing problem (VRP) is about finding optimal routes for a fleet of vehicles so that they can meet the demands for a set of given customers by traveling through those paths. This problem is one of the most important and most applicable problems of transportation and logistics scope. In this paper, green vehicle routing and scheduling problem with a heterogeneous fleet, including reverse logistics in the form of collecting returned goods along with weighted earliness and tardiness costs considering multiple time windows, is studied to establish a trade-off between operational and environmental costs. In this regard, a mixed-integer non-linear programming (MINLP) model is proposed at the first stage; then its accuracy and correct functioning are evaluated by solving some examples. Demand is considered uncertain based on fuzzy numbers that robust possibilistic programming is employed regarding the other parameters uncertainty. Since this problem is categorized as an NP-hard problem, a genetic algorithm (GA) is suggested to find near-optimal solutions for large instances in a rational computational time. Eventually, the GA’s performance is evaluated compared to solving the mathematical model for small-sized problems. Analysis of the results considering two criteria, solutions quality and computational times, indicates the satisfactory operation of the proposed algorithm in a proper computational time.   https://jte.sinaweb.net/article_87203_c0c0dbed43d262274738f44200b29155.pdf