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

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 th...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 10; no. 6; pp. 7435 - 7450
Main Authors: Liu, Shida, Liu, Qingsheng, Wang, Li, Chen, Xianlong
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2024
Springer Nature B.V
Springer
Subjects:
Berth allocation
Bulk cargo
Bulk terminal
Chaotic-optimal thermodynamic evolutionary algorithm
Complexity
Computational Intelligence
Crane schedule
Data Structures and Information Theory
Engineering
Evolutionary algorithms
Genetic algorithms
Multiple objective analysis
Optimization
Original Article
Scheduling
Ship terminals
Solution space
Stockyard allocation
Thermodynamics
Bulk terminal
Crane schedule
Stockyard allocation
Chaotic-optimal thermodynamic evolutionary algorithm
Berth allocation
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary: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.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01452-w