Robust capacity planning under uncertainty
Management Science
Principles on the benefits of manufacturing process flexibility
Management Science
Simulation-based methodology for machine cell design
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Capacity Optimization Planning System (Caps)
Interfaces
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This paper presents a method for allocating production capacity among flexible and dedicated machines based on uncertain demand forecasts of products in a production portfolio. Given multiple scenarios of future demands with the associated probabilities, the method provides alternative capacity allocations by quantifying the expected values of the product quality and cost. The product quality is estimated as the total performance variations from the nominal design for each product in a portfolio. The production cost is estimated as the total annual equivalent of investment and operation costs for each production period. A multi-objective genetic algorithm is utilized to compute the Pareto-optimal capacity allocations that quantify the tradeoffs between the expected product quality and cost. Case studies on an automotive valvetrain production are presented, where, under the demand forecasts with low uncertainty, the allocation of flexible machines is encouraged only at production steps critical to quality and cost.