A hybrid genetic-goal programming approach for improving group performance in cell formation problems. (18th January 2021)

• Record Type: Journal Article
• Authors: Chaudhuri, Barnali; Jana, R.K.; Sharma, Dinesh K.; Dan, P.K.
• Is Part Of: International journal of advanced operations management. Volume 12:Number 4(2020)
• Page Start: 377
• Page End: 395
• Publication Date: 2021-01-18
• Subjects: cellular manufacturing -- group efficacy -- hybrid genetic algorithm -- HGA -- goal programming; Business logistics -- Periodicals; Industrial management -- Periodicals; 658.5005
• Languages: English
• ISSNs: 1758-938X

Abstract

This paper proposes a hybrid genetic-goal programming approach to improve group performance in cell formation problems in manufacturing systems. The problem is formulated mathematically as a multi-objective programming problem. A proposed genetic algorithm (GA) is used to solve the problem. The chromosomes of the GA represent a combination of machines and parts. The proposed approach improves group performance by considering group efficacy as the performance measure. A software package corresponding to the proposed approach is developed in C# has a user-friendly GUI. Thirty problem instances of varying sizes prove the superiority of the approach in terms of group efficacy by avoiding duplicity in the allocation of parts into machines.