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
Interval number and fuzzy number linear programmings
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
Strategic level three-stage production distribution planning with capacity expansion
Computers and Industrial Engineering
An evolutionary-based approach for solving a capacitated hub location problem
Applied Soft Computing
A class of rough multiple objective programming and its application to solid transportation problem
Information Sciences: an International Journal
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In this paper, discount in transportation cost on the basis of transportated amount is extended to a solid transportation problem. In a transportation model, the available discount is normally offered on items/criteria, etc., in the form AUD (all unit discount) or IQD (incremental quantity discount) or combination of these two. Here transportation model is considered with fixed charges and vechicle costs where AUD, IQD or combination of AUD and IQD on the price depending upon the amount is offered and varies on the choice of origin, destination and conveyance. To solve the problem, genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover and uniform mutation has been suitably developed and applied. To illustrate the models, numerical examples have been presented. Here, different types of constraints are introduced and the corresponding results are obtained. To have better customer service, the entropy function is considered and it is displayed by a numerical example. To exhibit the efficiency of GA, another method-weighted average method for multi-objective is presented, executed on a multi-objective problem and the results of these two methods are compared.