Presenting a Supplier Selection, Order Allocation, and Pricing Model in Multi-item, Single-Period, and Multi-Supplier Supply Chain Management with Surface Response Methodology and Genetic Algorithm Approach

Selecting the supplier, allocating the goods to the suppliers, and pricing the goods are the important challenges with which retailers are faced. The present study is aimed at providing a multi-item supplier selection, order allocation, and pricing model with stochastic demand and purchase from the suppliers who provide goods with all-unit discount.

The study has used quantitative modeling by presenting mathematical model. The demand functions are price-based with additive uncertainty. In this study, a mixed nonlinear integer single-objective model was developed. To this end, the response surface methodology was used to estimate the income function and the genetic algorithm was applied to solve the model. Further, the Taguchi method was utilized to set the parameter of the genetic algorithm. For verifying the proposed method, nine problems with different product quantities and levels of variance of stochastic variables of demand were solved. In addition, in order to evaluate the performance of the genetic algorithm, the results of the algorithm in solving problems with small dimensions were compared to the results in solving the model in Lingo software.

The results of the study indicated that the difference between the results of genetic algorithm and lingo is not significant. After solving the model for the examples using the proposed method, it was determined that increasing the variance of the random variable of demand results in decreasing the profit level.

Considering the supplier selection, order allocation, and pricing issues can help retailers to make better decisions. Furthermore, demand based on the price and various conditions of the discount are assumptions which make the model more practical. The results of solving the model for various examples indicated that increasing uncertainty in demand leads to a decrease in the amount of profit. Moreover, the genetic algorithm is considered as an appropriate alternative to solve a mixed nonlinear integer model of supplier selection, order allocation, and pricing.