A two-step approach for the integrated multi item production/distribution planning problem under shipment consolidation considerations

To achieve optimal operational performance in a supply chain, production and distribution functions have to be integrated. Besides, significant cost savings in transportation and consequently in logistics can be obtained through shipment consolidation considerations by means of the better utilization of transportation vehicle capacities. In this perspective, this thesis study deals with the integrated production and distribution planning considering the shipment consolidation strategies. The production and distribution model considers production, inventory and transportation activities simultaneously for an environment that includes multiple production plants, multiple distribution centers, and multiple product items for multiple time periods. In the integrated model, heterogeneous fleet of vehicles is taken into account. At first, this model is formulated as a mixed integer programming model. However, it becomes harder to solve the model optimally in reasonable time. As a consequence, a two-step solution approach is proposed to solve this integrated model with reasonable computational effort. The developed two-step solution approach integrates mixed integer programming and genetic algorithms. The first step of the approach is still formulated as a mixed integer programming model and still considers production, inventory and transportation activities. However, the vehicle routing considerations are excluded from the mixed integer programming model and they are later considered in the succeeding step. In the second step, a genetic algorithm based approach is developed to solve the vehicle routing problem. Finally, the two-step solution approach is illustrated by two hypothetical case problems and the performance of the proposed approach is evaluated. In general, the developed two-step approach is an efficient solution methodology for integrated production and distribution planning problems with multiple production plants, multiple distribution centers, and multiple product items for multiple time periods and heterogeneous fleet of vehicles. Additionally, this approach can also be modified to satisfy the requirements of various production and distribution planning problems. Keywords: Supply chain management; Integrated production and distribution planning; Shipment consolidation; Vehicle routing; Mixed integer programming; Genetic algorithms