A probability maximization model based on rough approximation and its application to the inventory problem

  • Authors:

  • Yu Shi;Liming Yao;Jiuping Xu

  • Affiliations:

  • Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, China;Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, China;Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, China

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2011

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Abstract

In the present paper, we concentrate on dealing with a class of multi-objective programming problems with random coefficients and present its application to the multi-item inventory problem. The P-model is proposed to obtain the maximum probability of the objective functions and rough approximation is applied to deal with the feasible set with random parameters. The fuzzy programming technique and genetic algorithm are then applied to solve the crisp programming problem. Finally, the application to Auchan's inventory system is given in order to show the efficiency of the proposed models and algorithms.