“…Amit Kumar presented application of classical transportation methods to find the fuzzy optimal solution of fuzzy transportation problem and also find the optimal solution of transshipment [14,15]. Lee and Li proposed an idea for optimizing transportation problem with multiple objectives, and fuzzy approach to the MOTP [17,18]. Surapati [22] proposed goal programming approach for solving MOTP.…”
Section: Literature Reviewmentioning
confidence: 99%
“…To illustrate the efficiency of the proposed method, we consider the different size of interval in the following numerical example: x i3 = [17,21];…”
Section: Examplementioning
confidence: 99%
“…where [15,17]] [1, [12,13] Using Definition (3.4) we write the equivalent multi objective deterministic problem as:…”
Section: Examplementioning
confidence: 99%
“…However in reality these are important factors which greatly influence the decision process of transportation problems. Lee and Moore (1973) applied goal programming (GP) to find a solution of multi-objective transportation problem. Goal programming has widely applied to solve different problems which involve multiple objectives.…”
This paper presents a fuzzy goal programming approach for solving multi-objective transportation problem with interval cost. The fuzzy goal programming approach is used for achieving highest degree of each of the membership goals by minimizing negative deviational variables. Also a special type of hyperbolic membership function to each objective function describe fuzzy goal. A numerical example is given to illustrate the potential use of the proposed approach.
“…Amit Kumar presented application of classical transportation methods to find the fuzzy optimal solution of fuzzy transportation problem and also find the optimal solution of transshipment [14,15]. Lee and Li proposed an idea for optimizing transportation problem with multiple objectives, and fuzzy approach to the MOTP [17,18]. Surapati [22] proposed goal programming approach for solving MOTP.…”
Section: Literature Reviewmentioning
confidence: 99%
“…To illustrate the efficiency of the proposed method, we consider the different size of interval in the following numerical example: x i3 = [17,21];…”
Section: Examplementioning
confidence: 99%
“…where [15,17]] [1, [12,13] Using Definition (3.4) we write the equivalent multi objective deterministic problem as:…”
Section: Examplementioning
confidence: 99%
“…However in reality these are important factors which greatly influence the decision process of transportation problems. Lee and Moore (1973) applied goal programming (GP) to find a solution of multi-objective transportation problem. Goal programming has widely applied to solve different problems which involve multiple objectives.…”
This paper presents a fuzzy goal programming approach for solving multi-objective transportation problem with interval cost. The fuzzy goal programming approach is used for achieving highest degree of each of the membership goals by minimizing negative deviational variables. Also a special type of hyperbolic membership function to each objective function describe fuzzy goal. A numerical example is given to illustrate the potential use of the proposed approach.
“…Aneja and Nair (1979) presented a bicriteria transportation problem model. Lee and Moore (1973) studied the optimization of multiobjectives transportation problems. Diaz (1978Diaz ( , 1979 and Isermann (1979) proposed the procedures to generate all non-dominated solutions to the multiobjective linear transportation problem.…”
In the last 20 years many multi‐objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real‐world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi‐objective programming methods.
The present paper is intended to review the existing literature on multi‐objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi‐criteria framework. Some conclusions are drawn and directions for future research are suggested.
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