Fuzzy multiple objective programming and compromise programming with Pareto optimum
Fuzzy Sets and Systems
Fuzzy programming approach to multiobjective solid transportation problem
Fuzzy Sets and Systems
An additive fuzzy programming model for multiobjective transportation problem
Fuzzy Sets and Systems
A concept of the optimal solution of the transportation problem with fuzzy cost coefficients
Fuzzy Sets and Systems
Uncertain solid transportation problems
Fuzzy Sets and Systems
A fuzzy approach to the multiobjective transportation problem
Computers and Operations Research
A multi-objective transportation problem under fuzziness
Fuzzy Sets and Systems
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
Trapezoidal approximations of fuzzy numbers---revisited
Fuzzy Sets and Systems
Transportation policies for single and multi-objective transportation problem using fuzzy logic
Mathematical and Computer Modelling: An International Journal
Fixed charge transportation problem with type-2 fuzzy variables
Information Sciences: an International Journal
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In this paper, a multi-objective, solid transportation problem (STP) with imprecise unit costs and route-wise travel-times (general fuzzy numbers) is considered. The sources' availabilities, destinations' demands and capacities of conveyances are also represented by different types of fuzzy numbers like general, trapezoidal and triangular numbers. The transportation problem (T. P.) has been formulated with and without entropy function defined by Shannon's measure of entropy. Multi-objective problems are formed with different criteria and reduced to single objective optimization problems using Zimmermann's approach and possibility measure of the fuzzy equality. For the entropy based model, the multi-objective problem is converted to a single objective problem using weighted average of the objectives. Generalized Reduced Gradient (GRG) method is used to find the optimal solutions for a set of given numerical data. The models under two types of formulation are numerically solved and compared. The results of the models with and without entropy are also obtained and compared.