Intelligent bulk cargo terminal scheduling based on a novel chaotic-optimal thermodynamic evolutionary algorithm

Abstract

This paper presents a chaotic optimal thermodynamic evolutionary algorithm (COTEA) designed to address the integrated scheduling problems of berth allocation, ship unloader scheduling, and yard allocation at bulk cargo terminals. Our proposed COTEA introduces a thermal transition crossover method that effectively circumvents local optima in the scheduling solution process. Additionally, the method innovatively combines a good point set with chaotic dynamics within an integrated initialization framework, thereby cultivating a robust and exploratory initial population for the optimization algorithm. To further enhance the selection process, our paper proposes a refined parental selection protocol that employs a quantified hypervolume contribution metric to discern superior candidate solutions. Postevolution, our algorithm employs a Cauchy inverse cumulative distribution-based neighborhood search to effectively explore and enhance the solution spaces, significantly accelerating the convergence speed during the scheduling solution process. The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. The effectiveness of our COTEA is demonstrated through extensive numerical simulations.