Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm

  • Authors:

  • P. C. Yang;H. M. Wee;S. Pai;Y. F. Tseng

  • Affiliations:

  • Department of Industrial Engineering and Management, St. John's University, Tamsui, Taipei 25135, Taiwan, ROC;Department of Industrial Engineering, Chung Yuan Christian University, Chungli 32023, Taiwan, ROC;Department of Marketing and Logistics, St. John's University, Tamsui, Taipei 25135, Taiwan, ROC;Department of Industrial Engineering and Management, St. John's University, Tamsui, Taipei 25135, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

This study presents a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Recently, much attention has been given to stochastic demand due to uncertainty in the real world. Conflicting objectives also exist between profit, service level and resource utilization. In this study, the relationship between the expected profit and the number of trials as well as between the expected profit and the combination of mutation and crossover rates are investigated to identify better parameter values to efficiently run the Genetic Algorithm. Pareto optimal solutions and return on investment are analyzed to provide decision makers with the alternative options of achieving the proper budget and service level. The results show that the optimal value for the return on investment and the expected profit are obtained with a certain budget and service level constraint.