Fairness-oriented multi-objective optimization of supply chain planning under uncertainties

Fair strategies in the supply chain planning are important to the sustainability development of supply chains. This work addresses the fairness-oriented multi-objective optimization of the production, distribution and capacity planning problem of multi-period, multi-product global supply chains under uncertainties, to optimize cost, responsiveness and customer service level, simultaneously. A fuzzy-based optimization model is established and transformed into a deterministic mixed integer linear programming (MILP) formulation, in which a new formulation method is developed to transform the variables with fuzzy parameters in the index into a definite form. In addition, to achieve sustainable development of the supply chain, Nash bargaining method is used for the proportionally fair strategies between the multiple objectives. Finally, a numerical case is used to demonstrate the applicability of the proposed model and solution approach. The computational results are shown to be Pareto-optimal. The fair solutions under different allowed degrees of uncertainty are compared through the Monte Carlo simulation. In addition, the obtained solutions are shown to be fairer than those from alternative solution method and capacity expansion strategy.