Designing a manufacturing cell system by assigning workforce

Journal of Industrial Engineering and Management, Jan 2019

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce.Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms.Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation.Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.

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Designing a manufacturing cell system by assigning workforce

Journal of Industrial Engineering and Management JIEM 2013-0953 Designing a Manufacturing Cell System by Assigning Workforce Ashkan Ayough 0 Behrouz Khorshidvand 1 0 Shahid Beheshti University , Iran 1 Islamic Azad University , Iran Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells. cell production; group technology; particle swarm optimization; simulated annealing 1. Introduction Nowadays, in a global competitive environment, the companies tend to offer products at lower cost and higher quality for challenging with the competitors. In the recent decades, methods and production strategies have significantly changed, in comparison to the early half of the twentieth century. One of the changes that many companies have used is cellular manufacturing (CM). Group Technology (GT) is a well-known idea that includes both advantages of mass and small-scale production settings (Karthikeyan, Saravanan & Ganesh, 2012). GT is a philosophy of production that identifies the similar products and collects them in a certain group for reaching the benefits of their similarities in design and manufacturing. Components are gathered in clusters based on their designing, features and geometric shapes within product groups which as a result, the system performance improves significantly. CM is a GT application to help companies cope with global challenges. CM has been created to meet market demand due to the inefficiency of traditional production systems. Therefore, cellular manufacturing system (CMS) is a more efficient solution with higher quality than the other methods. CMS groups similar products into product groups and allocates the necessary machines to the production cells. The main objective of CM is identifying machine cells and product groups and assigning the product groups to machine cells in order to minimize the costs. In designing of a CM, four important decisions should be made: cell formation, group layout, group scheduling, and resource allocation (Rafiee, Rabbani, Rafiei & Rahimi-Vahed, 2011). In this research, a mathematical model is presented for designing a random cell production system which demands parameters and processing times are probable and have a normal distribution. The CMS deals with the grouping of products and allocation of them to the cells and the allocation of machines to cells, but another important factor in the CMS is workforce, which should be allocated to the cell. For this reason, the problem changes from a two-dimensional to three-dimensional state. This paper constructed as follows: literature review in section 2, methodology in section 3, a numerical example in section 4, comparison of algorithms in section 5, and conclusion in section 6. 2. Literature Review Aalaei and Davoudpour (2015) have studied a revised multi-choice goal programming for incorporating dynamic virtual cellular manufacturing into supply chain management. Nouri and Hong (2013) have researched on development of bacterial foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations. Karthikeyan et al. (2012) have provided a GT machine cell formation problem in scheduling for cellular manufacturing system using Meta-Heuristic method. Singh and Rajamani (2012) described about a cellular manufacturing system by taking into account designing, planning and controlling. Mahdavi, Aalaei, Paydar and Solimanpur (2012) have studied a new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Chang, Wu and Wu (2013) have provided an efficient approach to determine cell formation, cel (...truncated)


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Ashkan Ayough, Behrouz Khorshidvand. Designing a manufacturing cell system by assigning workforce, Journal of Industrial Engineering and Management, 2019, pp. 13-26, Volume 12, Issue 1, DOI: 10.3926/jiem.2547