Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
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
Evolution program for bicriteria transportation problem
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
A concept of the optimal solution of the transportation problem with fuzzy cost coefficients
Fuzzy Sets and Systems
Fuzzy integer transportation problem
Fuzzy Sets and Systems
Bicriteria transportation problem by hybrid genetic algorithm
Proceedings of the 23rd international conference on on Computers and industrial engineering
A fuzzy approach to the multiobjective transportation problem
Computers and Operations Research
A multi-objective transportation problem under fuzziness
Fuzzy Sets and Systems
Fuzzy goal programming for multiobjective transportation problems
Journal of Applied Mathematics and Computing
Computers and Industrial Engineering
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In this paper, a fuzzy bi-criteria transportation problem is studied. Here, the model concentrates on two criteria: total delivery time and total profit of transportation. The delivery times on links are fuzzy intervals with increasing linear membership functions, whereas the total delivery time on the network is a fuzzy interval with a decreasing linear membership function. On the other hand, the transporting profits on links are fuzzy intervals with decreasing linear membership functions and the total profit of transportation is a fuzzy number with an increasing linear membership function. Supplies and demands are deterministic numbers. A nonlinear programming model considers the problem using the max-min criterion suggested by Bellman and Zadeh. We show that the problem can be simplified into two bi-level programming problems, which are solved very conveniently. A proposed efficient algorithm based on parametric linear programming solves the bi-level problems. To explain the algorithm two illustrative examples are provided, systematically.