مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Information Journal Paper

Title

DISTRIBUTION NETWORK DESIGN UNDER DEMAND UNCERTAINTY USING GENETIC ALGORITHM AND MONTE CARLO SIMULATION APPROACH: A CASE STUDY IN PHARMACEUTICAL INDUSTRY

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  0-0

Abstract

 DISTRIBUTION NETWORK DESIGN as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the MONTE CARLO SIMULATION. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using GENETIC ALGORITHMS and 14% reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.

Cites

References

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APA: Copy

IZADI, ARMAN, & KIMIAGARI, ALI MOHAMMAD. (2014). DISTRIBUTION NETWORK DESIGN UNDER DEMAND UNCERTAINTY USING GENETIC ALGORITHM AND MONTE CARLO SIMULATION APPROACH: A CASE STUDY IN PHARMACEUTICAL INDUSTRY. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, 10(10), 0-0. SID. https://sid.ir/paper/309934/en

Vancouver: Copy

IZADI ARMAN, KIMIAGARI ALI MOHAMMAD. DISTRIBUTION NETWORK DESIGN UNDER DEMAND UNCERTAINTY USING GENETIC ALGORITHM AND MONTE CARLO SIMULATION APPROACH: A CASE STUDY IN PHARMACEUTICAL INDUSTRY. JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL[Internet]. 2014;10(10):0-0. Available from: https://sid.ir/paper/309934/en

IEEE: Copy

ARMAN IZADI, and ALI MOHAMMAD KIMIAGARI, “DISTRIBUTION NETWORK DESIGN UNDER DEMAND UNCERTAINTY USING GENETIC ALGORITHM AND MONTE CARLO SIMULATION APPROACH: A CASE STUDY IN PHARMACEUTICAL INDUSTRY,” JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL, vol. 10, no. 10, pp. 0–0, 2014, [Online]. Available: https://sid.ir/paper/309934/en

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