Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

Mengke Li, Yongkui Shi () and Meiyan Li
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Mengke Li: College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yongkui Shi: College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Meiyan Li: College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2023, vol. 11, issue 7, 1-19

Abstract: In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Keywords: hybrid fleet; simultaneous delivery and pickup; real-time road conditions; electric vehicle; immune genetic algorithm; elitist strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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