A Diffused Memetic Optimizer for reactive berth allocation and scheduling at marine container terminals in response to disruptions

•A Diffused Memetic Optimizer (DMO) is developed for reactive berth allocation and scheduling.•The algorithm addresses the issue of limited interactions within the diffusion grid.•DMO deploys tailored hybridization techniques to facilitate the search within the diffusion grid.•DMO showcases its supe...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 80; p. 101334
Main Author: Dulebenets, Maxim A.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2023
Subjects:
Berth allocation and scheduling
Diffused algorithms
Disruptions
Exact optimization
Hybridization
Migration
Berth allocation and scheduling
Diffused algorithms
Hybridization
Migration
Disruptions
Exact optimization
ISSN:2210-6502
Online Access:Get full text
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Summary:•A Diffused Memetic Optimizer (DMO) is developed for reactive berth allocation and scheduling.•The algorithm addresses the issue of limited interactions within the diffusion grid.•DMO deploys tailored hybridization techniques to facilitate the search within the diffusion grid.•DMO showcases its superiority against the exact optimization method and well-known state-of-the-art metaheuristics.•Some important managerial insights are derived that could assist with the real-time berth schedule recovery. The economic development of numerous countries is defined by maritime supply chains to a great extent. Substantial volumes of containerized cargoes delivered by ships are handled at marine container terminals. However, these terminals often face different types of disruptive events, and it is critical to provide an effective response to unexpected disruptions. This study proposes a novel optimization model for reactive berth allocation and scheduling at marine terminals that explicitly captures various recovery strategies along with the handling resources available at the terminal. Due to the computational complexity of the model, a novel Diffused Memetic Optimizer (DMO) is developed. The proposed DMO algorithm addresses the issue of limited interactions between the individuals located on the opposite sides of the diffusion grid, which is viewed as a common limitation of diffused algorithms. Furthermore, the proposed DMO deploys problem-specific tailored hybridization techniques inspired by exact optimization to facilitate the search for good-quality solutions within the diffusion grid. The computational experiments showcase the competitive DMO performance against the exact mixed-integer non-linear programming method (BARON) and some of the well-known state-of-the-art metaheuristics. Furthermore, it is discovered that slow diffusion alone by means of imposing the diffusion grid is not sufficient for the successful performance. The periodic migration between the designated areas of the diffusion grid and the application of problem-specific hybridization techniques are essential for explorative capabilities of the developed DMO algorithm. Last but not least, important managerial insights are revealed using the proposed methodology, which can assist with the berth schedule recovery.
ISSN:2210-6502
DOI:10.1016/j.swevo.2023.101334