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M. S JabalAmeli, B. Bankian Tabrizi, M. Moshref Javadi ,
Volume 21, Issue 4 (IJIEPR 2010)

  The problem of locating distribution centers (DCs) is one of the most important issues in design of supply chain. In previous researches on this problem, each DC could supply products for all of the customers. But in many real word problems, DCs can only supply products for customers who are in a certain distance from the facility, coverage radius. Thus, in this paper a multi-objective integer linear programming (MOILP) model is proposed to locate DCs in a two-echelon distribution system. In this problem, customers who are in the coverage radius of the DCs can be supplied. Moreover, we suppose that the coverage radius of each DC can be controlled by decision maker and it is a function of the amount of money invested on the DC. Finally, a random generated problem is used to verify the model and the computational results are presented .

M. Ameli, A. Mirzazadeh, M. Shirazi,
Volume 24, Issue 1 (IJIEPR 2013)

It was suggested in 2004 by some researchers that it might be possible to improve production systems performance by applying the first and second laws of thermodynamics to reduce system entropy. Then these laws were used to modify the economic order quantity (EOQ) model to derive an equivalent entropic order quantity (EnOQ). Moreover the political instability or uncertainty of a country (as well as the whole world) leads to a much more unstable situation in the present world economy. Thus, changes in inflation take place, and it is needed to consider uncertain inflation rate. In this paper we extend the EnoQ model by considering deteriorating items with imperfect quality and price dependent demand. We also assume fuzzy inflation and discount rates.‌ A mathematical model is developed to determine the number of cycles that maximizes the present value of total revenue in a finite planning horizon. The fuzzified model for inflation and discount rate is formulated and solved by two methods: signed distance and fuzzy numbers ranking. Numerical examples are presented and results are discussed. Results show that the number of cycles decreases in fuzzy inflationary conditions. They also illustrate that defuzzification method results in more cycles than fuzzy method.

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