A Study and Performance Evaluation of Evolutionary Optimization Techniques for Multi-objective Master Production Scheduling Problems
Ключевые слова:
master production scheduling, multi-objective optimization, differential evolution, teaching-learning-based optimizationАннотация
Master production schedule (MPS) can effectively and efficiently synchronize the operations in any organization. MPS, which is posed as one of the multi-objective parameter optimization problems, is a plan that determines optimal values of products to be produced. For many engineering optimization problems, more competitive and optimal solutions can be obtained by using Heuristic evolutionary optimization algorithms. Among these, two main algorithms considered here are the differential evolution (DE) whose results are not greatly affected by parameters and teaching-learning-based optimization (TLBO), the recent algorithm which does not require any algorithm-specific parameters. This work presents the development and use of DE and TLBO to MPS problems. The results available for the existing algorithm are compared with those obtained from the proposed evolutionary algorithms. The research demonstrates that use of TLBO yields the most optimal solution for MPS problems with a minimum computational time.Опубликован
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