Title: Machine cell formation for dynamic part population considering part operation trade-off and worker assignment using simulated annealing-based genetic algorithm

Authors: Kamal Deep

Addresses: Department of Mechanical Engineering, Guru Jambeshwar University of Science and Technology, Hissar – 125 001 (HR), India

Abstract: In this study, an integrated mathematical model for the cell formation problem is proposed considering the dynamic production environment. The proposed model yields, manufacturing cells, part families and worker's assignment simultaneously by allowing a cubic search space of 'machine-part-worker' in the CMS. The resources are aggregated into manufacturing cells based on the optimal process route among the user specified multiple routes. The model interprets flexibility in the processing of subsets of a part operation sequence in the different production mode (internal production/subcontracting part operation). It is a tangible advantage during unavailability of worker and unexpected machine break down occurring in the real world. The proposed cell formation problem has been solved by using a simulated annealing-based genetic algorithm (SAGA). The algorithm imparts synergy effect to improve intensification, diversification in the cubic search space and increases the possibility of achieving near-optimum solutions. To evaluate the computational performance of the proposed approach the algorithm is tested on a number of randomly generated instances. The results substantiate the efficiency of the proposed approach by minimising overall cost. [Received: 17 August 2018; Accepted: 28 July 2019]

Keywords: dynamic cellular manufacturing systems; worker assignment; multiple process route; system reconfiguration; part operation trade-off; subcontracting part operation; simulated annealing-based genetic algorithm.

DOI: 10.1504/EJIE.2020.105720

European Journal of Industrial Engineering, 2020 Vol.14 No.2, pp.189 - 216

Received: 17 Aug 2018
Accepted: 28 Jul 2019

Published online: 27 Feb 2020 *

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