Tackling the storage problem through genetic algorithms

doi:10.1007/s40436-014-0074-1

Advances in Manufacturing ›› 2014, Vol. 2 ›› Issue (3): 203-211.doi: 10.1007/s40436-014-0074-1

Tackling the storage problem through genetic algorithms

L. Chirici
Department of Computer Science, University of Pisa, Pisa, Italy
e-mail: lapochirici@gmail.com
K.-S. Wang
Department of Production and Quality Engineering, Norwegian
University of Science and Technology, Trondheim, Norway

出版日期:2014-09-25 发布日期:2014-09-25
通讯作者: L. Chirici Department of Computer Science, University of Pisa, Pisa, Italy e-mail: lapochirici@gmail.com
作者简介:L. Chirici Department of Computer Science, University of Pisa, Pisa, Italy e-mail: lapochirici@gmail.com K.-S. Wang Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Tackling the storage problem through genetic algorithms

L. Chirici
Department of Computer Science, University of Pisa, Pisa, Italy
e-mail: lapochirici@gmail.com
K.-S. Wang
Department of Production and Quality Engineering, Norwegian
University of Science and Technology, Trondheim, Norway

Online:2014-09-25 Published:2014-09-25
Contact: L. Chirici Department of Computer Science, University of Pisa, Pisa, Italy e-mail: lapochirici@gmail.com
About author:L. Chirici Department of Computer Science, University of Pisa, Pisa, Italy e-mail: lapochirici@gmail.com K.-S. Wang Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway

摘要/Abstract

摘要： The capability of a company to implement an automated warehouse in an optimized way might be nowadays a crucial leverage in order to gain competitive advantage to satisfy the demand. The order picking is a warehouse function that needs to deal with the retrieval of articles from their storage locations. Merging several single customer orders into one, a picking order can increase efficiency of warehouse operations. The aim of this paper is to define throughout the use of ad-hoc genetic algorithm (GA) how better a warehouse can be set up. The paper deals with order batching, which has a major effect on
efficiency of warehouse operations to avoid wastes of
resources in terms of processes and to control possibility of
unexpected costs in advance.

关键词: Genetic algorithms (GA) , Warehouse management , Order batching , Optimization

Abstract: The capability of a company to implement an automated warehouse in an optimized way might be nowadays a crucial leverage in order to gain competitive advantage to satisfy the demand. The order picking is a warehouse function that needs to deal with the retrieval of articles from their storage locations. Merging several single customer orders into one, a picking order can increase efficiency of warehouse operations. The aim of this paper is to define throughout the use of ad-hoc genetic algorithm (GA) how better a warehouse can be set up. The paper deals with order batching, which has a major effect on
efficiency of warehouse operations to avoid wastes of
resources in terms of processes and to control possibility of
unexpected costs in advance.

Key words: Genetic algorithms (GA) , Warehouse management , Order batching , Optimization

Lapo Chirici,Ke-Sheng Wang. Tackling the storage problem through genetic algorithms[J]. Advances in Manufacturing, 2014, 2(3): 203-211.

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Tackling the storage problem through genetic algorithms

Tackling the storage problem through genetic algorithms

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