Multi Objective Production–Distribution Decision Making Model Under Fuzzy Random Environment

  • Muhammad Nazim
  • Muhammad Hashim
  • Abid Hussain Nadeem
  • Liming Yao
  • Jamil Ahmad
Conference paper

DOI: 10.1007/978-3-642-55182-6_52

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 280)
Cite this paper as:
Nazim M., Hashim M., Nadeem A.H., Yao L., Ahmad J. (2014) Multi Objective Production–Distribution Decision Making Model Under Fuzzy Random Environment. In: Xu J., Cruz-Machado V., Lev B., Nickel S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg

Abstract

Today the most important concern of the managers is to make their firms viable in the competitive trade world. Managers are looking effective tools for decision making in the complex business world. This paper addresses a hierarchical multi objective production-distribution planing problem under fuzzy random environment. A mathematical model is presented to describe the purpose problem. To deal the uncertain environment, the fuzzy random variables are first transformed into trapezoidal fuzzy numbers, and by using the expected value operation, the trapezoidal fuzzy numbers are subsequently defuzzified. For solving the multi-objective problem a weighted sum base genetic algorithm is applied. Finally, the result of a numerical example are presented to demonstrate the practical and efficiency of the optimized model.

Keywords

Multi-objective optimization Fuzzy lead-time Fuzzy inventory cost parameters Inventory planing Interactive fuzzy decision making method 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Muhammad Nazim
    • 1
  • Muhammad Hashim
    • 1
  • Abid Hussain Nadeem
    • 1
  • Liming Yao
    • 1
  • Jamil Ahmad
    • 1
  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengdu  People’s Republic of China

Personalised recommendations