Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2024

Abstract

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.

Keywords

E-waste collection, Vehicle routing problem, GRASP framework, Q-learning

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 24th European Conference on Evolutionary Computation in Combinatorial Optimisation (EVOSTAR 2024) : Aberystwyth Wales, UK, April 3-5

First Page

49

Last Page

64

ISBN

9783031577116

Publisher

Springer

City or Country

Aberystwyth, UK

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