Optimisation for job scheduling at automated container terminals using genetic algorithm

Skinner, B.T.; Yuan, Shenfang; Huang, Shoudong; Liu, Dikai; Cai, Binghuang; Dissanayake, Gamini; Lau, H. T.; Bott, Andrew; Pagac, D.

doi:10.1016/j.cie.2012.08.012

Cited by 51 publications

(15 citation statements)

References 25 publications

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“…GAs are global optimization techniques that avoid many of the shortcomings that exist in classical local search techniques on difficult search spaces [13]. GAs use operators such as reproduction, crossover and mutation as means of preserving beneficial information with the overall goal of finding a better solution to the problem.…”

Section: Ga-based Jamming Methodsmentioning

confidence: 99%

Triangle and GA Methods for UAVs Jamming

Zhang

Yang

2014

Mathematical Problems in Engineering

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We focus on how to jam UAVs network efficiently. The system model is described and the problem is formulated. Based on two properties and a theorem which helps to decide good location for a jammer, we present the Triangle method to find good locations for jammers. The Triangle method is easy to understand and has overall computational complexity ofON2. We also present a genetic algorithm- (GA-) based jamming method, which has computational complex ofOLMN2. New chromosome, mutation, and crossover operations are redefined for the GA method. The simulation shows that Triangle and GA methods perform better than Random method. If the ratio of jammers’ number to UAVs’ number is low (lower than 1/5 in this paper), GA method does better than Triangle method. Otherwise, Triangle method performs better.

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Section: Ga-based Jamming Methodsmentioning

confidence: 99%

Triangle and GA Methods for UAVs Jamming

Zhang

Yang

2014

Mathematical Problems in Engineering

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“…It is easy to see that the vertical transport (stacking cranes) is related to the horizontal transport and these transports have to be synchronized in order to achieve high-quality optimization of logistic problems within these transports. The authors in [27][28][29] have used the genetic algorithm for resolving specific problems within the horizontal and vertical transport. We assume that the introduction of the new nature inspired metaheuristics could also improve this optimization problem considering the performances of these metaheuristics.…”

Section: Transport Optimizationmentioning

confidence: 99%

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

2018

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Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.

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“…In order to adapt to uncertainties in the ACT, a new AGV dispatching method was presented (Angeloudis and Bell, 2010), where the uncertainty level and the running features of AGV were taken in to account, with their simulation results showing efficiency of the presented dispatching method. A model was developed to minimize the utilization of shuttle carriers (Skinner et al, 2013). The utilization of the shuttle carriers was designed in the objective function, and a genetic algorithm with double populations was presented to solve the problem.…”

Section: Previous Researchmentioning

confidence: 99%

A Double-Population Genetic Algorithm for ASC Loading Sequence Optimization in Automated Container Terminals

Fan

et al. 2015

Journal of Coastal Research

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Shu, F.;Mi, W.; Li, X.; Zhao, N.; Mi, C., and Yang, X., 2015. A double-population genetic algorithm for ASC loading sequence optimization in automated container terminals.An automated container terminal (ACT) emphasizes the autonomy of equipment scheduling. The scheduling of the automated stacking crane (ASC) as major equipment has an important impact on the yard operating efficiency. In the export process, ASC cooperated with the quay crane in loading operations. Although traditionally the loading sequence of ASC is generated directly in terms of the stowage plan, with the trend towards automated intelligent terminals, the ASC is becoming increasingly the content of dynamic decision. This paper presents a method of dynamic planning ASC loading sequence after the stowage plan. The method took reshuffling number and ASC movement frequency into account, considered the target vessel slots defined by the stowage plan as constraints, and established a multi-objective mathematical model. Taking into account the problem with NP-hard characteristics, this paper proposed a double-population genetic algorithm based on a general genetic algorithm, establishing a solution mechanism by leveraging genetic exchanges between populations to change the convergence speed. With the completion of a certain bay in the block, it online adjusted the next block collection, and eventually formed a solution of real-time loading sequence. Experiments demonstrated that the algorithm could dynamically obtain the near optimal solution of the problem, adapt the timeliness demand of ASC dynamic decision, and improve the intelligence of the equipment control system (ECS). ADDITIONAL INDEX WORDS:Automated stacking crane, double-population genetic algorithm, loading sequence, stowage plan, reshuffling.

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Optimisation for job scheduling at automated container terminals using genetic algorithm

Cited by 51 publications

References 25 publications

Triangle and GA Methods for UAVs Jamming

Triangle and GA Methods for UAVs Jamming

Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

A Double-Population Genetic Algorithm for ASC Loading Sequence Optimization in Automated Container Terminals

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