Management Science
Coping with the loss of pooling synergy in cellular manufacturing systems
Management Science
Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Dynamic cellular manufacturing system (DCMS)
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Computers and Operations Research
An ant colony optimization algorithm for scheduling virtual cellular manufacturing systems
International Journal of Computer Integrated Manufacturing
Computers and Operations Research
A genetic algorithm with the heuristic procedure to solve the multi-line layout problem
Computers and Industrial Engineering
Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes
Computers and Operations Research
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We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.