Q-Learning based framework for solving the stochastic E-waste collection problem

• Main Authors: NGUYEN, Dang Viet Anh, GUNAWAN, Aldy, MISIR, Mustafa, VANSTEENWEGEN, Pieter
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: E-waste collection; Vehicle routing problem; GRASP framework; Q-learning; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/9753; https://ink.library.smu.edu.sg/context/sis_research/article/10753/viewcontent/978_3_031_57712_3_4.pdf
• Institution: Singapore Management University

Electrical and Electronic Equipment (EEE) has evolved into a gateway for accessing technological innovations. However, EEE imposes substantial pressure on the environment due to the shortened life cycles. E-waste encompasses discarded EEE and its components which are no longer in use. This study focuses on the e-waste collection problem and models it as a Vehicle Routing Problem with a heterogeneous fleet and a multi-period planning problem with time windows as well as stochastic travel times. Two different Q-learning-based methods are designed to enhance the search procedure for finding solutions. The first method involves utilizing the state-action value to determine the order of multiple improvement operators within the GRASP framework. The second one involves a hyperheuristic that extracts a stochastic policy to select heuristic operators during the search. Computational experiments demonstrate that both methods perform competitively with state-of-the-art methods in newly-generated small-sized instances, while the performance gap widens as the size of the problem instances increases.