Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Fuzzy programming approach to multiobjective solid transportation problem
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
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
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
On the difference between traditional and deductive fuzzy logic
Fuzzy Sets and Systems
Flexible planning using fuzzy description logics: Theory and application
Applied Soft Computing
An entropy based solid transportation problem for general fuzzy costs and time with fuzzy equality
Mathematical and Computer Modelling: An International Journal
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In this paper, single and multi-objective transportation models are formulated with fuzzy relations under the fuzzy logic. In the single-objective model, objective is to minimize the transportation cost. In this case, the amount of quantities transported from an origin to a destination depends on the corresponding transportation cost and this relation is verbally expressed in an imprecise sense i.e., by the words 'low', 'medium', 'high'. For the multi-objective model, objectives are minimization of (i) total transportation cost and (ii) total time for transportation required for the system. Here, also the transported quantity from a source to a destination is determined on the basis of minimum total transportation cost as well as minimum transportation time. These relations are imprecise and stated by verbal words such as 'very high', 'high', 'medium', 'low' and 'very low'. Both single objective and multi-objective problems using Real coded Genetic Algorithms (GA and MOGA) are developed and used to solve the single level and bi-level logical relations respectively. The models are illustrated with numerical data and optimum results are presented.