Simulation of production and transportation planning with uncertainty and risk

  • Authors:

  • Kuentai Chen;Hung-Chun Chen;Z. H. Che

  • Affiliations:

  • Department of Industrial Engineering and Management, Ming Chi University of Technology, Taishan, Taiwan;Department of Industrial Engineering and Management, Ming Chi University of Technology, Taishan, Taiwan;Department of Industrial Engineering and Management, Ming Chi University of Technology, Taishan and National Taipei University of Tech., Taiwan

  • Venue:
  • WSEAS Transactions on Computers
  • Year:
  • 2008

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Abstract

Inevitabl in the practical supply chain planning, uncertainties, including unsure demand and various risks such as machine failure and transportation loss, are fundamental issues for all members of the supply chain. In this research, a mathematic model of supply chain with risk and uncertain demand are established and solved. The inherent complexity of such an integer programming model leads to the solving difficulty in speedily finding exact and integer optimal solutions. Therefore, a quick and decent answer becomes essential to pace up with the competitive business world, even it is usually only an approximate estimate. Four types of model are discussed in this study, including certain demand without risk, certain demand with risk, uncertain demand without risk, and uncertain demand with risk. After model verification and validation, computer simulations are performed with three selecting policies, namely "low cost first", "random", and "minimum cost path". The results are analyzed and compared, in which the "minimum cost path" is the better policy for node selection according to simulation runs. A general linear programming solver called LINDO was used to find the optimal solutions but took days as the problem size increases, while simulation model obtains an acceptable solution in minutes. For small size problems, numerical examples show that the Mean Absolute Percentage Error (MAPE) between integer simulation solution and mathematical non-integer solution falls into the range of 3.69% to 7.34%, which demonstrates the feasibility and advantage of using simulation for supply chain planning.