A stochastic biobjective MIP model for designing the network of biodiesel supply chains is presented. Ultimately intending to support the strategic decisions of stakeholders. The constraints included are: economies of scale, location of facilities, production capacity, raw material supply, product demand, bill of materials and mass balance.
The model aims to minimize, both, the total cost and environmental impact of five chain echelons
The solution procedure resorts to chance constraint and the ε-constraint method to solve the biobjective model.
Computational experiments allowed assessing the performance of the solution procedure. The CPU times for the solution of the instances of the problem show very good values.
By approaching the modeling of the biodiesel supply chain the current contribution can serve as the basis of future similar works and associated solution procedures, thus facilitating decision-making at different supply chain stages. The current approach can be improved through its modeling and/or through the development of solution procedures that allow its practical use in larger instances of the model. This can be overcome through permanent research lines that include the development of adequate acceleration methods, valid constraints, Benders decomposition, branch and cut, Lagrangian decomposition, Danzing-Wolf decomposition, or heuristics and meta-heuristics.
Supply Chain - Mathematical Programming
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