Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

• The proposed approach generates robust machine cell by considering equivalent part demand, but part families are formed based on number of parts demanded in the different period segments of planning span.
• Optimal alternative process plan for each part at the start of planning horizons is worked out.
• The number of machines needed in different machines cells by considering inter-cell part tradeoff for each part type is worked out at the start of planning horizons.
• Parts mix to machine cell in different period segments of planning span is worked out without any machine relocation.
• Considers the machine capacity and maximal and minimal cell size.
• Optimal solution to the design of the cellular manufacturing system using genetic algorithms.

In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.