This paper presents a novel integer nonlinear programming model for dynamic cellular manufacturing systems in
the presence of worker assignment. The proposed model incorporates several design features including operation time,
alternative workers, duplicate machines, removing idle machines from system or returning them to system, machine
capacity, hiring and firing of workers, production volume of parts, part movements between cells, cell reconfiguration and
production planning. A review of the literature reveals few attempts integrating these important design features during
cell formation, simultaneously. The objective is to minimize the total costs of inter-cellular material handling, holding and
backorder and manages machines and workers over a certain planning horizon.
This model is capable to determine the optimal cell configurations, worker assignment and process plan for each part
type at each period over the planning horizon. The nonlinear formulation of the proposed model was linearized using some
auxiliary variables. The performance of the model is illustrated by two numerical examples. CPU time required to reach the
optimal solution of the attempted examples show that obtaining an optimal solution in a reasonable time is computationally
intractable. Therefore, it is necessary to develop a heuristic or metaheuristic approach to solve the proposed model for large-
sized problems. Moreover, the originality of paper is as follows:
Considering the cubic space of machinepartworker in CFP.
Designing a comprehensive model for CFP and production planning.
Balancing machines and workers with respect to relocation and reconfiguration in multi-period production planning.
The proposed model is still open for incorporating other features in future researches. Some guidelines for future
researches can be outlined as follows.
Application of metaheuristic approach (Simulated Annealing, Genetic Algorithm, etc.) to solve the proposed model for
real-sized problems.
Incorporation of sequence data (sequence of operations) for CFP which provides additional information to the cell
designer.
Incorporating intra-cell layout of machines to exactly calculate inter-cell material handling cost.
This paper presents a novel integer nonlinear programming model for dynamic cellular manufacturing systems in
the presence of worker assignment. The proposed model incorporates several design features including operation time,
alternative workers, duplicate machines, removing idle machines from system or returning them to system, machine
capacity, hiring and firing of workers, production volume of parts, part movements between cells, cell reconfiguration and
production planning. A review of the literature reveals few attempts integrating these important design features during
cell formation, simultaneously. The objective is to minimize the total costs of inter-cellular material handling, holding and
backorder and manages machines and workers over a certain planning horizon.
This model is capable to determine the optimal cell configurations, worker assignment and process plan for each part
type at each period over the planning horizon. The nonlinear formulation of the proposed model was linearized using some
auxiliary variables. The performance of the model is illustrated by two numerical examples. CPU time required to reach the
optimal solution of the attempted examples show that obtaining an optimal solution in a reasonable time is computationally
intractable. Therefore, it is necessary to develop a heuristic or metaheuristic approach to solve the proposed model for large-
sized problems. Moreover, the originality of paper is as follows:
Considering the cubic space of machinepartworker in CFP.
Designing a comprehensive model for CFP and production planning.
Balancing machines and workers with respect to relocation and reconfiguration in multi-period production planning.
The proposed model is still open for incorporating other features in future researches. Some guidelines for future
researches can be outlined as follows.
Application of metaheuristic approach (Simulated Annealing, Genetic Algorithm, etc.) to solve the proposed model for
real-sized problems.
Incorporation of sequence data (sequence of operations) for CFP which provides additional information to the cell
designer.
Incorporating intra-cell layout of machines to exactly calculate inter-cell material handling cost.
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