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2015 | OriginalPaper | Buchkapitel

Particle Swarm Optimization with Multiple Learning Terms for Storage Location Assignment Problems Considering Three-Axis Traveling Distance

verfasst von : Warisa Wisittipanich, Pongsakorn Meesuk

Erschienen in: Toward Sustainable Operations of Supply Chain and Logistics Systems

Verlag: Springer International Publishing

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Abstract

This chapter presents an approach based on particle swarm optimization (PSO) for minimizing total traveling distance in warehouse storage location assignment problems (SLAP). The traveling distance in an order-picking process is considered with three-axis traveling distance: two horizontal axes and one vertical axis. A mathematical model is first presented, and LINGO optimization program is used to find optimal solutions for a set of generated problems. As the problem size increases, LINGO could not find solutions within reasonable time. Thus, particle swarm optimization (PSO) is applied to solve SLAP. The proposed algorithm employs multiple learning terms and utilizes the random key representation to generate a solution. The numerical experiments show that the proposed PSO is able to generate good solutions with relatively shorter computing time.

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Vorheriges Kapitel An Assessment of Customer Contentment for Ready-to-Drink Tea Flavor Notes Using Artificial Neural Networks

Nächstes Kapitel A Particle Swarm Optimization Approach for Solar Cell Industry Multi-stage Closed-Loop Supply Chain Model

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Titel: Particle Swarm Optimization with Multiple Learning Terms for Storage Location Assignment Problems Considering Three-Axis Traveling Distance
verfasst von: Warisa Wisittipanich
Pongsakorn Meesuk
Verlag: Springer International Publishing
Buch: Toward Sustainable Operations of Supply Chain and Logistics Systems
Print ISBN: 978-3-319-19005-1

Electronic ISBN: 978-3-319-19006-8

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-19006-8_30

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