A linguistic multi-objective mixed integer programming model for multi-echelon supply chain network at bio-refinery

Abstract

This study uses a linguistic multi-objective mixed nonlinear programming with Z-numbers computation to design a multi-echelon supply chain network for bio-refinery. The method considers total transportation costs and capacities of all echelons, attempting to satisfy three different linguistic objective functions: minimising transportation costs, reducing greenhouse emission and maximising the customer service level while reducing the cost of collecting the raw materials. The main purpose of this model is to reduce the greenhouse emissions in transportation activities and maximise the profit of supply chain cycle based on the linguistic variables. To solve the proposed model we used PSO-GSA algorithm. This model to demonstrate the feasibility of the proposed model for the given problem as well as discussion on the model's advantages solved a numerical example. Solving the numerical example showed that the Z-numbers application may increase the cost instead provide better-balanced distribution to satisfy customers, which can compensate that added cost.