An Adaptive Large Neighborhood Search for the green mixed fleet vehicle routing problem with realistic energy consumption and partial recharges

• Main Authors: YU, Vincent F., JODIAWAN, Panca, GUNAWAN, Aldy
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Electric vehicle routing problem; Mixed fleet; Emission minimization; Adaptive large neighborhood search; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6038; https://ink.library.smu.edu.sg/context/sis_research/article/7041/viewcontent/AdaptiveLNS_2021_av.pdf
• Institution: Singapore Management University

This study addresses a variant of the Electric Vehicle Routing Problem with Mixed Fleet, named as the Green Mixed Fleet Vehicle Routing Problem with Realistic Energy Consumption and Partial Recharges. This problem contains three important characteristics — realistic energy consumption, partial recharging policy, and carbon emissions. An adaptive Large Neighborhood Search heuristic is developed for the problem. Experimental results show that the proposed ALNS finds optimal solutions for most small-scale benchmark instances in a significantly faster computational time compared to the performance of CPLEX solver. Moreover, it obtains high quality solutions for all medium- and large-scale instances under a reasonable computational time. We also perform numerical studies to analyze the potential carbon emission reduction resulted from the proposed model.