Magsud Amiri; , Mohammad Amin Nayebi; Oveis Zarabadipour
Volume 12, Issue 33 , July 2015, , Pages 125-124
Abstract
In this paper we have developed inventory control models (r,Q) & (R,T) in multi-items environment by two objectives as minimizing costs (holding & shortage) and risk level under four constraints. These constraints include: available budge, service level, storage space & allowed shortage quantities. ...
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In this paper we have developed inventory control models (r,Q) & (R,T) in multi-items environment by two objectives as minimizing costs (holding & shortage) and risk level under four constraints. These constraints include: available budge, service level, storage space & allowed shortage quantities. Demand functions assumed normal in the study and extra demands also are backlogged. First we developed crisp models and then fuzzy stochastic models with fuzzy budge, allowed shortage quantities and shortage space which are fuzzy-stochastic parameters with normal distribution. All of fuzzy numbers are triangular typically. In this methodology we changed fuzzy-stochastic models to crisp multi objectives problem, by using difuzzification of fuzzy constraints and then solving by Fuzzy logic method. Finally we have tested an example to describe the model and methodology which is solved by LINGO package. .
Mohammad Amin Nayebi; Abbass Panahinia
Volume 9, Issue 22 , September 2011, , Pages 209-235
Abstract
In this paper we developed an inventory model in mixed imprecise and uncertain environment. Presented model is developed form of (r,Q) and is a multi-items model with two objectives as minimizing costs (holding & shortage) and risk level under constraints including available budgetary, the least ...
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In this paper we developed an inventory model in mixed imprecise and uncertain environment. Presented model is developed form of (r,Q) and is a multi-items model with two objectives as minimizing costs (holding & shortage) and risk level under constraints including available budgetary, the least service level, storage spaces & allowable quantities of shortage. Demand distribution functions are assumed to be exponential and extra demands are supposed in two situations as lost sales and backlogging. At first we develop crisp model then fuzzy stochastic model with fuzzy budgetary, allowable quantities of shortage and shortage spaces (i.e. stochastic with normal distribution function) parameter. All of fuzzy numbers are triangular type. In methodology of solution we change model to a crisp multi-objective by using difuzzification of fuzzy constraints and fuzzy chance-constrained programming methods, and then solve it by fuzzy logic method. Finally an illustrated example is taken and solved using LINGO package.
