Group scheduling on two cells with intercell movement
Computers and Operations Research
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Computers and Industrial Engineering
Two-machine flowshop group scheduling problem
Computers and Operations Research
A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
Total flow time minimization in a flowshop sequence-dependent group scheduling problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach
Expert Systems with Applications: An International Journal
Scheduling groups of jobs in the two-machine flow shop
Mathematical and Computer Modelling: An International Journal
Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes
Computers and Operations Research
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This paper addresses the problem of scheduling parts in job shop cellular manufacturing systems by considering exceptional parts that need to visit machines in different cells and reentrant parts which need to visit some machines more than once in non-consecutive manner. Initially, an integer linear programming (ILP) model is presented for the problem to minimize the makespan, which considers intercellular moves and non-consecutive multiple processing of parts on a machine. Due to the complexity of the model, a simulated annealing (SA) based solution approach is developed to solve the problem. To increase the efficiency of the search algorithm, a neighborhood structure based on the concept of blocks is applied. Subsequently, the efficiency of the ILP model and the performance of the proposed SA are assessed over a set of problem instances taken from the literature. The proposed ILP model was coded in Lingo 8.0 and the solution obtained by the proposed SA was compared to the optimal values. The computational results demonstrate that the proposed ILP model and SA algorithm are effective and efficient for this problem.