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Neural Computing and Applications

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01-08-2016 | Original Article

Research on group search optimizers for a reconfigurable flow shop sequencing problem

Author: Hadi Mokhtari

Published in: Neural Computing and Applications | Issue 6/2016

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Abstract

A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.

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Title: Research on group search optimizers for a reconfigurable flow shop sequencing problem
Author: Hadi Mokhtari
Publication date: 01-08-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 6/2016
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1963-3

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