Abstract
This paper presents a new mathematical model to solve cell formation problem in cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic. The objective function maximizes the number of operations of each part with more arrival rate within one cell. Because a queue behind each machine; queuing theory is used to formulate the model. To solve the model, two metaheurstic algorithms such as modified particle swarm optimization and genetic algorithm are proposed. For the generation of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Both metaheurstic algorithms are compared against global solutions obtained from Lingo software’s branch and bound (B&B). Also, a statistical method will be used for comparison of solutions of two metaheurstic algorithms. The results of numerical examples indicate that considering the machine breakdown has significant effect on block structures of machine-part matrixes.
Highlights
The concept of group technology (GT) has emerged to reduce setups, batch sizes, and travel distances
This paper presents a new mathematical model to solve cell formation problem in cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic
For the generation of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Both metaheurstic algorithms are compared against global solutions obtained from Lingo software’s branch and bound (B&B)
Summary
The concept of group technology (GT) has emerged to reduce setups, batch sizes, and travel distances. A review of previous studies about static stochastic cell formation problem is presented in four parts such as processing time, the mix product, the demand, and the reliability, respectively. The main purpose of their study is to minimize the total costs of inter-cell and intra-cell movements in both machine and cell layout problems in CM system simultaneously They considered part demands as an independent variable with the normal probability distribution. The generalized CF problem follows selecting the best process plan for each part and assigning machines to the cells In this model, it has been assumed that the number of breakdowns for each machine follows a Poisson distribution with a known failure rate. The presented model forms manufacturing cells considering three stochastic parameters including the processing time, the time between two successive arrival parts to the cell, and the Decision variables. Identification of the particle in whole swarm with the best success so far, and assignment of its fitness value to gbest and its location to ~pg
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
