Solving Multi-Objective Linear Fractional Stochastic Transportation Problems Involving Normal Distribution using Simulation-Based Genetic Algorithm
Adane Abebaw Gessesse1, Rajashree Mishra2, Mitali Madhumita Acharya3

1Adane Abebaw Gessesse*, Department of Mathematics, KIIT, Deemed to be University, Bhubneswar, India.
2Rajashree Mishra, Department of Mathematics, KIIT, Deemed to beUniversity, Bhubneswar, India.
3Mitali Madhumita Acharya, Department of Mathematics, KIIT,Deemed to be University, Bhubaneswa, India.
Manuscript received on October 29, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 9-17 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3054129219/2019©BEIES | DOI: 10.35940/ijeat.B3054.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: In real-life situations, we human beings faced with multi-objective problems that are conflicting and non-commensurable with each other. Especially, when goods are transported from source to locations with a goal to keep exact relationships between a few parameters, those parameters of such problems might also arise in the form of fractions which are linear in nature such as; actual transportation fee/total transportation cost, delivery fee/desired path, total return/total investment, etc. Due to the uncertainty of nature, such a relationship is not deterministic. Mathematically such kinds of mathematical problems are characterized as a multi-objective linear fractional stochastic transportation problem. However, it is difficult to handle such types of mathematical problems. It can’t be solved directly using mathematical programming approaches. In this paper, a solution procedure is proposed for the above problem using a stochastic Genetic Algorithm based simulation. The parameters in the constraint of the above problem follow a normal distribution. The probabilistic constraints are handled by stochastic simulation-based GA for the solution procedure of the proposed problem. The feasibility of probability constraints is checked by the stochastic programming through the Genetic Algorithm approach, without finding the equivalent deterministic model. The feasibility is maintained all-over the problem. The stochastic simulation-based Genetic Algorithm is considered to generate non-dominated solutions for the given problem. Then, a numerical case study is provided to illustrate the method.
Keywords: Genetic Algorithm, multi-objective programming, stochastic fractional programming, transportation problem.