Presenting a Multi Objective Model for Supplier Selection in Order to Reduce Green House Gas Emission under Uncertion Demand

Abstract

Recently, much attention has been given to Stochastic demand due to uncertainty in the real world. In the literature, decision-making models and suppliers' selection do not often consider inventory management as part of shopping problems. On the other hand, the environmental sustainability of a supply chain depends on the shopping strategy of the supply chain members. The supplier selection plays an important role in the green chain. In this paper, a multi-objective nonlinear integer programming model for selecting a set of suppliers considering Stochastic demand is proposed. while the cost of purchasing including the total cost, holding and stock out costs, rejected units, units that have been delivered sooner, and total green house gas emissions are minimized, while the obtained total score from the supplier assessment process is maximized. It is assumed that the purchaser provides multiple products from the number of predetermined supplier to a Stochastic demand and the uniform probability distribution function. The product price depends on the order quantity for each product line that is intended. Multi-objective models using known methods, such as Lpmetric have become an objective function and then using genetic algorithms and simulated annealing meta-heuristic is solved.