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A methodology for cost‐oriented assembly line balancing problems

A methodology for cost‐oriented assembly line balancing problems Purpose – The efficiency of assembly lines is a critical factor for the competitiveness of industries in the global market. The purpose of this paper is to present a line balancing methodology consisting of the combination of a heuristic model and an exact algorithm with intelligent task location or line zone constraints. The objective is to find a minimum cost solution in a feasible computational time with a realistic cost function considering short‐term operation costs, task‐related and workstation capital investment costs, and workstation paralleling. Design/methodology/approach – The methodology is evaluated using different problem sizes, zone sizes and cycle time scenarios. The quality of results is measured by the closeness to the global optimum and the computational time requirements. Findings – The proposed methodology is found to be highly effective with an average percentage difference of −0.63 percent from the optimum solution. In the experimentation, results are compared against the global optimum in 24 of the 36 scenarios tested. In 23 of the 24 (95.8 percent) results, the largest percentage difference is 0.55 percent. In eight of the 12 cases in which the global optimum is not found, the algorithm with zone constraints provided a better solution than the upper bound available when the simulation model pursuing the optimum is stopped. These unfinished runs are stopped after a minimum run time of 24 hours. Originality/value – The originality of the methodology is on the strategy used to consider workstation paralleling with task‐related capital investment costs. It is the only one with an exact algorithm considering task‐related capital investment costs in combination with workstation paralleling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Manufacturing Technology Management Emerald Publishing

A methodology for cost‐oriented assembly line balancing problems

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References (17)

Publisher
Emerald Publishing
Copyright
Copyright © 2009 Emerald Group Publishing Limited. All rights reserved.
ISSN
1741-038X
DOI
10.1108/17410380910997254
Publisher site
See Article on Publisher Site

Abstract

Purpose – The efficiency of assembly lines is a critical factor for the competitiveness of industries in the global market. The purpose of this paper is to present a line balancing methodology consisting of the combination of a heuristic model and an exact algorithm with intelligent task location or line zone constraints. The objective is to find a minimum cost solution in a feasible computational time with a realistic cost function considering short‐term operation costs, task‐related and workstation capital investment costs, and workstation paralleling. Design/methodology/approach – The methodology is evaluated using different problem sizes, zone sizes and cycle time scenarios. The quality of results is measured by the closeness to the global optimum and the computational time requirements. Findings – The proposed methodology is found to be highly effective with an average percentage difference of −0.63 percent from the optimum solution. In the experimentation, results are compared against the global optimum in 24 of the 36 scenarios tested. In 23 of the 24 (95.8 percent) results, the largest percentage difference is 0.55 percent. In eight of the 12 cases in which the global optimum is not found, the algorithm with zone constraints provided a better solution than the upper bound available when the simulation model pursuing the optimum is stopped. These unfinished runs are stopped after a minimum run time of 24 hours. Originality/value – The originality of the methodology is on the strategy used to consider workstation paralleling with task‐related capital investment costs. It is the only one with an exact algorithm considering task‐related capital investment costs in combination with workstation paralleling.

Journal

Journal of Manufacturing Technology Management – Emerald Publishing

Published: Oct 23, 2009

Keywords: Manufacturing systems; Line management; Optimization techniques; Process planning; Modelling

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