Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm

Chawla, V.; Chanda, A.; Angra, S.

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 2 Issue 1 pp. 27-40 , 2018

Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm Pages 27-40 Download PDF

Authors: V. K. Chawla, Arindam Kumar Chanda, Surjit Angra

DOI: 10.5267/j.ijdns.2018.6.001

Keywords: Automatic guided vehicles, Flexible manufacturing system, Grey wolf optimization, Sustainable multi-objective scheduling

Abstract: The simultaneous scheduling decisions between production systems and material handling systems are highly significant for a substantial reduction in makespan and improvement in throughput of flexible manufacturing system resources. In the absence of appropriate scheduling of production resources, the optimum utilization of FMS resources is not harnessed which turns into wastage of resources. In the present study, investigations are carried out for the sustainable multi-objective scheduling of automatic guided vehicle and flexible manufacturing system by the application of a grey wolf optimization algorithm (GWO). Initially the Giffler and Thompson (GT) algorithm [Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations research, 8(4), 487-503.] along with four different priority hybrid dispatching rules (PHDRs) are applied for the development of the production center schedule thereafter the grey wolf optimization algorithm is applied for the yield of the sustainable multi-objective schedul-ing of automatic guided vehicles (AGVs) and the FMS together with an objective to minimize the total distance travel and number of backtracking of cruising automatic guided vehicle in the U type flexible manufacturing system facility. The applied methodology is evaluated by conducting computational experiments on a benchmark flexible manufacturing system configuration considered from the literature. The results obtained from the computational experiments clearly show that the proposed application of grey wolf optimization algorithm outperforms the other applied procedures in the literature.

How to cite this paper

 Chawla, V., Chanda, A & Angra, S. (2018). Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm.International Journal of Data and Network Science, 2(1), 27-40.

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