Optimization of logistic distribution centers process planning and scheduling

Karasek, Jan; Bürget, Radim; Uher, Václav; Dutta, Malay Kishore; Kumar, Yogesh

doi:10.1109/ic3.2013.6612217

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…It is a most simple operator which purpose is to change the path of the fork-lift truck during the commodity transportation through the logistic warehouse. This operator was described in [2].…”

Section: A Path Mutation Operatormentioning

confidence: 99%

“…In other words, the work-plan should be processed from the closest jobs to more distant jobs. This operator was described in [2].…”

Section: B Job Order Mutation Operatormentioning

confidence: 99%

“…This paper is related to previous research, were the Genetic Programming (GP) algorithm driven by the Context-free Grammar was developed for the logistic warehouse work-flow optimization [1]. The main contribution of this paper is to introduce three new genetic operators, which is the Swap Job Mutation (SJ), the Swap Work-Plan Mutation (SW), and the Split Job Mutation (SP) and compare their performance to already introduced genetic operators, such as Path Mutation (PA) and Job Order Mutation (JO) [2]. The comparison of particular genetic operators was performed on defined sets of benchmarking scenarios presented in [3].…”

Section: Introductionmentioning

confidence: 99%

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Genetic programming operators for work-flow optimization in logistic distribution centers

Karasek

Bürget

Povoda

et al. 2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

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This paper describes a set of new operators for grammar guided genetic programming algorithm used for workflow optimization in logistic distribution centers. The paper is focused on the description of design of five genetic operators, results of particular operators in logistic work-flow optimization process and comparison of performance of particular operators. The main contribution of this paper is to show the automated problem solving techniques in form of genetic operators applied in genetic programming algorithm which is able to optimize a given problem from the area of logistic warehousing.

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Section: A Path Mutation Operatormentioning

confidence: 99%

“…In other words, the work-plan should be processed from the closest jobs to more distant jobs. This operator was described in [2].…”

Section: B Job Order Mutation Operatormentioning

confidence: 99%