Objective:

A stochastic bi-objective Mixed Integer Problem (MIP) model of biodiesel supply chain networks is presented, ultimately intended to support strategic decisions of stakeholders.

Materials and Methods:

The bi-objective MIP model aims to minimize the total cost and environmental impact of five chain echelons, taking into consideration the following constraints: economies of scale, location of facilities, production capacity, raw material supply, product demand, bill of materials and mass balance. The solution procedure resorts to chance constraints, valid constraints and the ε-constraint method.

Results and Discussion:

The CPU times for the optimal solution of the problem instances show very good values. Computational experiments allowed assessing the performance of the solution procedure.

Conclusion:

The current approach to the modeling of the biodiesel supply chain may serve as the basis of future similar works and associated solution procedures, thus facilitating decision-making at different supply chain stages. The approach fosters the development of new solution approaches such as adequate acceleration; heuristics and meta-heuristics; branch and cut methods; and Lagrangian, Benders and Danzing-Wolfe decompositions. These new approaches are intended to allow comparisons in terms of computational performance level, optimality gap, CPU time and memory usage.

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