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

Abstract

In transportation problem (TP), a decision-maker (DM) always wishes to optimise the given objectives by effectively transporting a given item from several sources to several destinations. The present paper explores the genetic algorithm (GA)-based hybrid approach to solve multi-objective stochastic transportation problem. By using exponential membership function, different shape parameters (SPs) and aspiration levels (ALs), higher degree of satisfaction for each objective function are obtained which provides more flexibility to the decision-maker (DM) for a better decision. In this approach, a multi-objective optimisation problem first converted into a single optimisation problem, then GA is applied with operator selection, crossover, mutation, etc. The logistic distribution is used here to convert the stochastic supply and demand into the real value. 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.