A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Abstract

Purpose The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost. Design/methodology/approach A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search. Findings Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space. Research limitations/implications This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured. Practical implications The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases. Originality/value The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

“…Both deterministic and adaptive parameter control strategies were found to be promising and outperformed the parameter tuning strategy. Specifically, the EAs with parameter control can more efficiently move along the search space, identify promising domains of the search space, and exploit the identified domains for superior solutions [10,12]. On the other hand, setting constant parameter values, which do not change throughout the algorithmic run (i.e., the parameter tuning strategy), typically limits the explorative and exploitative EA capabilities.…”

“…Section 6 describes the numerical experiments, which were conducted throughout this study to evaluate performance of the proposed solution The EAs, presented in the BSP literature, primarily rely on the parameter tuning strategy. In some of the recent BSP studies, the deterministic and adaptive parameter control strategies were implemented within the proposed EAs [10,12]. Both deterministic and adaptive parameter control strategies were found to be promising and outperformed the parameter tuning strategy.…”

“…A significant amount of previously conducted studies primarily focused on the berth scheduling problem (BSP), aiming to improve the seaside operations at MCTs [6]. In BSP, the MCT operator aims to assign arriving vessels for service at available MCT berthing positions and determine the service order of vessels at each berthing position.The BSP is a challenging decision problem, which can be reduced to the unrelated machine scheduling problem [6][7][8][9][10][11][12]. As underlined by Pinedo [13], the unrelated machine scheduling problem (therefore, the BSP as well) has NP-hard complexity.…”

“…For a detailed review of the BSP mathematical models and solution algorithms this study refers to the literature survey, conducted by Bierwirth and Meisel [6]. Many BSP studies applied Evolutionary Algorithms (EAs) and demonstrated their efficiency based on the numerical experiments [6][7][8][9][10][11][12][14][15][16][17][18][19]. EAs are biologically inspired metaheuristics, where the candidate solutions to the problem of interest are encoded in the chromosomes [20,21].…”

“…The BSP is a challenging decision problem, which can be reduced to the unrelated machine scheduling problem [6][7][8][9][10][11][12]. As underlined by Pinedo [13], the unrelated machine scheduling problem (therefore, the BSP as well) has NP-hard complexity.…”