Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Dulebenets, Maxim A.; Moses, Ren; Özgüven, Eren Erman; Vanli, O. Arda

doi:10.1109/access.2017.2693030

Cited by 44 publications

(39 citation statements)

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“…Furthermore, the MCT operator must allocate the storage space in the marshaling yard for containers, delivered by each vessel. Generally, the storage space is allocated as close as possible to the berthing position, where a given vessel is originally planned to be assigned for service (the latter term is also known as "preferred berthing position" in the BSP literature [6,11]). Because of changes in the original berth schedule, a given vessel may be assigned for service at an alternative berthing position (e.g., to reduce the waiting time of that vessel in case if its "preferred berthing position" is occupied by another vessel at the moment).…”

Section: Mct Operations Descriptionmentioning

confidence: 99%

“…The developed SAEA algorithm will be terminated once the maximum allowable number of generations (g lim ) has been reached. The latter termination criterion has been widely used in the EAs, proposed in the BSP literature [7,8,[10][11][12].…”

Section: Termination Criterionmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…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].…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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

et al. 2018

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Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time.Algorithms 2018, 11, 100 2 of 35 of waterborne trade increased by 33.6% from 7.7 billion tons in 2006 to 10.3 billion tons in 2016 [5]. Moreover, the volumes of all major types of cargo, transported by vessels, have significantly increased over the last ten years. Specifically, containerized trade increased by 59.9%, while major bulk cargo, dry bulk cargo, and oil/gas increased by 74.9%, 10.8%, and 13.2%, respectively, from 2006 to 2016. The general consumption goods and high-value cargo are typically transported in containerized form. Containers are transferred among different continents by vessels, which are served at marine container terminals (MCTs). Rapid growth of waterborne trade requires MCT operators upgrading the existing terminal infrastructure and improving operations planning.MCT operations can be categorized in the following three types [1,6]: (1) seaside operations, which focus on service of arriving vessels (i.e., loading containers on vessels and unloading containers from vessels); (2) marshaling yard operations, which focus on temporary storage of containers in yard blocks of the MCT marshaling yard; and (3) landside operations, which focus on pick-up and delivery of containers by the inland transportation modes (generally, on-dock rail and/or drayage trucks). Efficient seaside operations are critical for MCT performance, as disruptions in the seaside operations may significantly delay service of the arriving v...

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Section: Mct Operations Descriptionmentioning

confidence: 99%

Section: Termination Criterionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2018

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A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

Prencipe

Marinelli

2020

Appl Intell

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Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.

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Priority-based multi-objective algorithms for green supply chain network design with disruption consideration

Masruroh

Rifai

Mulyani

et al. 2023

Prod. Eng. Res. Devel.

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Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Cited by 44 publications

References 29 publications

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

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

A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

Priority-based multi-objective algorithms for green supply chain network design with disruption consideration

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