Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty

• We investigate the design of a network supply chain considering uncertainty on demands and two performance measures.
• A two-stage stochastic bi-objective programming model is developed to get optimal Pareto fronts.
• We develop an efficient solution method based on metaheuristics to solve the bi-objective stochastic problem.
• We confirm the convenience of including in the model the randomness in the demand parameter.

We consider the design of a two echelon production distribution network with multiple manufacturing plants, distribution centers (DC’s) and a set of candidate warehouses. One of the main contributions of the study is to extend the existing literature by incorporating the demand uncertainty of DC’s within the warehouse location and transportation mode allocation decisions, as well as providing a network design satisfying the both economical and service quality objectives of the decision maker within two echelon supply network setting. In order to take into account the effects of the uncertainty we apply an scenario-based approach and a two-stage stochastic problem is formulated in order to minimize total cost and total service time, simultaneously. Another important contribution is the development of a solution procedure for this bi-objective stochastic problem by applying tabu search within the framework of Multi-objective Adaptive Memory Programming. Results are compared with the optimal Pareto fronts obtained for small instances using the ∊∊-constraint method and standard branch and bound techniques. Numerical results demonstrate the computational effectiveness of the algorithm proposed. Finally, we include some results that confirm the convenience of including the randomness in the demand parameter.