Uncertain programming model for multi-item solid transportation problem
This work addresses transportation logistics under uncertainty, but it is incremental as it applies existing methods to a specific problem variant.
The paper tackled the multi-item solid transportation problem with uncertain parameters by converting it into deterministic models using uncertainty theory and solving them with optimization tools, demonstrating performance through a numerical example.
In this paper, an uncertain Multi-objective Multi-item Solid Transportation Problem (MMSTP) based on uncertainty theory is presented. In the model, transportation costs, supplies, demands and conveyances parameters are taken to be uncertain parameters. There are restrictions on some items and conveyances of the model. Therefore, some particular items cannot be transported by some exceptional conveyances. Using the advantage of uncertainty theory, the MMSTP is first converted into an equivalent deterministic MMSTP. By applying convex combination method and minimizing distance function method, the deterministic MMSTP is reduced into single objective programming problems. Thus, both single objective programming problems are solved using Maple 18.02 optimization toolbox. Finally, a numerical example is given to illustrate the performance of the models.