A Genetic Algorithm Approach for Production Capacity Planning Depends on Workers’ Expertise with Consideration of Learning and Forgetting
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Abstract
Workforce is one of the factors that should be considered in production planning. The special characteristics of workforce include learning and forgetting corresponding to experience and frequency of performing the production activity affecting capability of workers and total production capacity. The production planning and worker assignment of multiple products with time-varying demand are complicated for decision-making, especially for human planners. This research proposed the use of learning and forgetting rates to determine workforce capacity for production planning and work assignment. A suitable assignment by considering the worker’s expertise can reduce production time or increase production capacity when compared with the fixed assignment policy. A genetic algorithm was used to find the production plan with maximum profit. The parameters of genetic algorithm were tested in 4 models, i.e. C4M3, C4M7, C8M3, and C8M7. From the results showed that the proposed genetic algorithm approach had better performance than the fixed assignment policy. The suitable parameter of the genetic algorithm is C8M3 providing high performance in finding the best solution with less calculating time.
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