Incorporating risk into a supplier selection and order allocation problem in multi-item multi-supplier environment

This paper presents a multi- objective stochastic programming model for supplier selection problem with multiple items and multiple suppliers in a supply chain. The proposed model attempts to simultaneously minimize total purchasing and ordering costs, a number of defective units, and late delivered units ordered from suppliers. Chance constraint programming is also considered to model the risk associated with uncertain customer demand and supplier capacity. To solve the model a multi-objective harmony search algorithm(MOHS) and a non-dominated sorting genetic algorithm-II (NSGA- II) are proposed. The performance of solution procedures are compared in terms of performance metrics such as error ratio, generational distance, spacing metric, diversification metric, number of found solution and CPU time. The results of implementing the algorithms on some test problems show MOHS performs better than NSGA-II and authenticates its potential to solve the problem effectively