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Paper Information

Journal:   JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL   JANUARY 2014 , Volume 10 , Number 10; Page(s) 0 To 0.
 
Paper: 

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

 
 
Author(s):  IZADI ARMAN, KIMIAGARI ALI MOHAMMAD*
 
* DEPARTMENT OF INDUSTRIAL ENGINEERING, AMIRKABIR UNIVERSITY OF TECHNOLOGY, HAFEZ STREET, NO 424, 15875-4413, TEHRAN, IRAN
 
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.

 
Keyword(s): DISTRIBUTION NETWORK DESIGN, FACILITY LOCATION, GENETIC ALGORITHMS, MONTE CARLO SIMULATION
 
 
References: 
 
Cite:
APA: Copy

IZADI, A., & KIMIAGARI, A. (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. https://www.sid.ir/en/journal/ViewPaper.aspx?id=343868



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. 2014 [cited 2021April16];10(10):0-0. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=343868



IEEE: Copy

IZADI, A., KIMIAGARI, A., 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, [online] 10(10), pp.0-0. Available at: <https://www.sid.ir/en/journal/ViewPaper.aspx?id=343868>.



 
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