Advertisement

An Inventory Control Model for Stone Industry under a Fuzzy Random Environment

  • Liming Yao
  • Jingjie Wang
  • Ling Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)

Abstract

This paper proposes a bilevel optimization model with fuzzy random coefficients to tackle an inventory control problem for the stone industry with the aim of minimizing the inventory cost and the cost of quarrying and processing and ensuring meet the requirements of order. On the upper level, the goal of inventory department is to minimize the cost and inventory space at the same time. On the lower level, the quarrying management’s aim is to minimize the cost of quarrying and processing and ensuring meet the requirements of order. A compromised point-based GA is proposed to solve the bi-level programming model with fuzzy random coefficients. Finally, a case study is presented to demonstrate the practicality and efficiency of the model.

Keywords

Inventory control Fuzzy random variable Chance constraint Genetic algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Al-Jabari M, Sawalha H (2002) Treating stone cutting waste by flocculation-sedimentation. In: Proceedings of the Sustainable Environmental Sanitation and Water Services Conference, 28th WEDC Conference, Calcutta, IndiaGoogle Scholar
  2. 2.
    Nasserdine K, Mimi Z, Bevan B et al (2009) Environmental management of the stone cutting industry. Journal of Environmental Management 90(1):466–470Google Scholar
  3. 3.
    Almeida N, Branco F, Santos J (2007) Recycling of stone slurry in industrial activities: Application to concrete mixtures. Building and Environment 42(2):810–819Google Scholar
  4. 4.
    Buffa E, Miller J (1979) Production-Inventory Systems: Planning and control. Richard D. Irwin Homewood, ILGoogle Scholar
  5. 5.
    Lewis CD (1981) Scientific inventory control. ButterworthsGoogle Scholar
  6. 6.
    Xu J, Liu Y (2008) Multi-objective decision making model under fuzzy random environment and its application to inventory problems. Information Sciences 178(14):2899–2914Google Scholar
  7. 7.
    Xu J, Zhou X (2011) Fuzzy-like multiple objeictive decision making. Springer Xu J, Yao L (2009) A class of multiobjective linear programming models with random rough coefficients. Mathematical and Computer Modelling 49(1-2):189–206Google Scholar
  8. 8.
    Xu J, Yao L (2009) A class of multiobjective linear programming models with random rough coefficients. Mathematical and Computer Modelling 49(1-2):189–206Google Scholar
  9. 9.
    Xu J, Yao L, Zhao X (2011) A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem. Fuzzy Optimization and Decision Making 10(3):255–285Google Scholar
  10. 10.
    Yao L, Xu J (2013) A class of expected value bi-level programming problems with random coefficients based on rough approximation and its application to a production-inventory system. Abstract and Applied Analysis (In press)Google Scholar
  11. 11.
    Yao L, Xu J (2012) A stone resource assignment model under the fuzzy environment. Mathematical Problems in Engineering doi: 10.1155/2012/265837
  12. 12.
    Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley- InterscienceGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengduPeople’s Republic of China
  2. 2.College of Architecture and EnvironmentSichuan UniversityChengduPeople’s Republic of China
  3. 3.Business SchoolSichuan UniversityChengduPeople’s Republic of China

Personalised recommendations