The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 27-712
Full Name: Kuentai Chen
Position: Assistant Professor
Age: ON
Sex: Male
Address: 84 Gungjuan Rd., Taishan, Taipei Hsien 243,
Country: TAIWAN
Tel: +886-2-29089899 ext 4727
Tel prefix:
Fax:
E-mail address: kuentai@mail.mcut.edu.tw
Other E-mails: kch5207@hotmail.com
Title of the Paper: simulation of production and transportation planning with uncertainty and risk
Authors as they appear in the Paper: Kuentai Chen, Hung-chun Chen, Z.h. Che*
Email addresses of all the authors: kuentai@mail.mcut.edu.tw;zhche@ntut.edu.tw
Number of paper pages: 10
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.
Keywords: Supply Chain, Risk, Simulation, Integer Programming, Uncertainty
EXTENSION of the file: .pdf
Special (Invited) Session: Simulation of the Production and Distribution Planning with Risk
Organizer of the Session: 591-625
How Did you learn about congress: Ping-Teng Chang(ptchang@ie.thu.edu.tw);Ping-Feng Pai (paipf@ncnu.edu.tw)
IP ADDRESS: 210.240.249.10
