Genetic algorithm-based solution of multi-objective stochastic transportation problem

2021 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Jayesh M. Dhodiya ◽  
Jaydeepkumar M. Sosa
Author(s):  
Jaydeepkumar M. Sosa ◽  
Jayesh M. Dhodiya

Optimizing problems in the modern era, the single objective optimization problems are insufficient to hold the full data of the problem. Therefore, multi-objective optimization problems come to the rescue. Similarly, in daily life problems, the parameters used in the optimization problem are not always fixed but there may be some uncertainty and it can characterize by fuzzy number. This work underlines the genetic algorithm (GA) based solution of fuzzy transportation problem with more than one objective. With a view to providing the multifaceted choices to decision-maker (DM), the exponential membership function is used with the decision-makers desired number of cases which consisted of shape parameter and aspiration level. Here, we consider the objective functions which are non-commensurable and conflict with each other. To interpret, evaluate and exhibit the usefulness of the proposed method, a numerical example is given.


2014 ◽  
Vol 1 (2) ◽  
pp. 212 ◽  
Author(s):  
Srikumar Acharya ◽  
Narmada Ranarahu ◽  
Jayanta Kumar Dash ◽  
Mitali Madhumita Acharya

2019 ◽  
Vol 29 (3) ◽  
pp. 337-358 ◽  
Author(s):  
Mitali Acharya ◽  
Adane Gessesse ◽  
Rajashree Mishra ◽  
Srikumar Acharya

In this paper, we considered a multi-objective stochastic transportation problem where the supply and demand parameters follow extreme value distribution having three-parameters. The proposed mathematical model for stochastic transportation problem cannot be solved directly by mathematical approaches. Therefore, we converted it to an equivalent deterministic multi-objective mathematical programming problem. For solving the deterministic multi-objective mathematical programming problem, we used an ?-constraint method. A case study is provided to illustrate the methodology.


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