http://omreza.persiangig.com/Topsis2000.pdf

Extensions of the TOPSIS for group decision-making under fuzzy environment

A fuzzy approach for supplier evaluation and selection in supply chain management

Expert systems applications

A supplier selection decision inherently is a multi‐criterion problem. It is a decision of strategic importance to companies. The nature of this decision usually is complex and unstructured. Management science techniques might be helpful tools for these kinds of decision‐making problems. The aim of this paper is to use fuzzy analytic hierarchy process (AHP) to select the best supplier firm providing the most satisfaction for the criteria determined. The purchasing managers of a white good manufacturer established in Turkey were interviewed and the most important criteria taken into account by the managers while they were selecting their supplier firms were determined by a questionnaire. The fuzzy AHP was used to compare these supplier firms.

Multi‐criteria supplier selection using fuzzy AHP

An overview of the analytic hierarchy process and its applications

We present a new method of finding the fuzzy weights in fuzzy hierarchical analysis which is the direct fuzzification of the original method used by Saaty (1980) in the analytic hierarchy process. We test our new procedure in two cases where there are formulas for the crisp weights. An example is presented where there are five criteria and three alternatives.

Fuzzy hierarchical analysis

In this paper we are interested in generating good approximate solutions to single item, N-period, fuzzy inventory control problems. It is a fuzzy inventory control problem since some of the parameters (ordering cost, holding cost, penalty cost) can be fuzzy numbers. We consider three cases: (1) demand is known each period; (2) demand is unknown, and fuzzy each period; and (3) demand is fuzzy and backordering is allowed. We employ an evolutionary algorithm to search out good approximate solutions.

Solving fuzzy problems in operations research: inventory control

We are interested in generating good approximate solutions to single item, N-period, fuzzy inventory control problems. It is a fuzzy inventory control problem since some of the parameters (ordering cost, holding cost, penalty cost) can be fuzzy numbers. We consider two cases: (1) demand is known each period, and (2) demand is unknown, and fuzzy each period. We employ an evolutionary algorithm to search out good approximate solutions.

In this paper, we are interested in generating good approximate solutions to single-item, N-period, fuzzy inventory control problems, i.e. problems where some of the parameters (ordering cost, holding cost, penalty cost) can be fuzzy numbers. We consider three cases: (1) where the demand is known in each period; (2) where the demand is unknown and fuzzy in each period; and (3) where the demand is fuzzy and back-ordering is allowed. We employ an evolutionary algorithm to search out good approximate solutions.

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Fuzzy hierarchical analysis

Solving fuzzy problems in operations research: inventory control

Solving fuzzy problems in operations research: inventory control

Solving fuzzy problems in operations research: inventory control