A Genetic Algorithm Based Dynamic Berth Allocation Strategy for Container Terminals

Dong, Liang; Ding, Hanzhi

doi:10.1061/40996(330)542

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The algorithm can guarantee the determination of a feasible schedule for any given set of requests and can address the highly complex scheduling problem of an entire supply-chain for just-in-time production. Dong and Ding [ 35 ] introduced a dynamic berth allocation model for container terminal and proposed a GA-based heuristic method that improves the existing research on static berth allocation models. The GA-based approach explores detailed capabilities of a complex problem solution using these two encoding methods.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Modeling of Biological Intelligence for SCM System Optimization

Chen

Zheng

Cattani

et al. 2012

Computational and Mathematical Methods in Medicine

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This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Modeling of Biological Intelligence for SCM System Optimization

Chen

Zheng

Cattani

et al. 2012

Computational and Mathematical Methods in Medicine

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“…Similarly, immature convergence phenomenon of GEP is also due to the destroyed population diversity and the lost motive power of population evolution. To ensure global convergence of the algorithm, a feasible solution is to maintain the population diversity and avoid the effective genes [7] losing.…”

Section: Gep-pdsmentioning

confidence: 99%

Population Diversity Strategy in Gene Expression Programming

Zhang

Xiao

2011

AMR

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Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function finding, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.

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A Genetic Algorithm Based Dynamic Berth Allocation Strategy for Container Terminals

Cited by 2 publications

References 6 publications

Modeling of Biological Intelligence for SCM System Optimization

Modeling of Biological Intelligence for SCM System Optimization

Population Diversity Strategy in Gene Expression Programming

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