Self-learning differential evolution algorithm for scheduling of internal tasks in cross-docking

Abstract

A novel self-learning differential evolution (SLDE) algorithm for addressing large-scale internal tasks scheduling problems in cross-docking is proposed herein. The goal is to obtain an optimal schedule for working teams and transferring equipment for handling incoming containers at the inbound area and patient orders at the outbound area to minimise the total tardiness. The proposed SLDE aims to increase the search capability of its original differential evolution (DE). The key concept of SLDE is to allow a DE population to learn the capabilities of different search strategies and automatically adjust itself to potential search strategies. The performance of the proposed algorithms is evaluated on a set of generated data based on a real-case scenario of a medical product distribution centre; subsequently, the performance results are compared with results obtained from other metaheuristics. Numerical results demonstrate that the proposed SLDE outperforms other algorithms in terms of solution quality and convergence behaviour by providing superior solutions using fewer function evaluations.