Supplier Selection and Order Allocation Using a Stochastic Multi-Objective Programming Model and Genetic Algorithm

In this paper, we develop a supplier selection and order allocation multi-objective model in stochastic environment in which purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability density function. To do so, we use dependent chance programming (DCP) that maximises probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. After transforming the abovementioned stochastic multi-objective programming problem into a stochastic single objective problem using minimum deviation method, we apply a genetic algorithm to solve the later single objective problem. The employed genetic algorithm performs a simulation process in order to calculate the stochastic objective function as its fitness function. A stochastic analysis reveals that incorporation of stochasticity into the supplier selection and order allocation problem will be advantageous for a purchasing firm with respect to purchasing cost, percentage of delivered items with delay and percentage of rejected items. Furthermore, we explore the impact of stochastic parameters on the given solution via a sensitivity analysis exploiting coefficient of variation. The results show that as stochastic parameters have greater coefficients of variation, the value of objective function in the stochastic single objective programming problem is worsened.