mixed integer program

After occurrence of some disturbances in power system that causes the sever imbalance between generation power and electrical load, the power system frequency begins to decrease. To prevent power system frequency instability and stop the frequency decay below the power system allowable frequency limitation, load shedding schemes should be utilized by applying under frequency load shedding relays. In this paper, power system behavior is modeled as a mathematical mixed integer programing (MIP), moreover the parameters that are required for UFLS relays setting are determined optimally in order to minimize the load shedding amount throughout power system in compliance with certain restrictions. The obtained results have been verified by Simulation studies.

Here, we consider single vendor-buyer model with multi-product and multi-customer and multi-facility location-production-distribution problem. It is assumed that the players of the supply chain are coordinated by sharing information. Vendor manufactures produce different products at different plants with limited capacities and then distribute the products to the consumers according to deterministic demands. A mixed integer linear fractional programming (MILFP) model is formulated and a solution approach for MILFP is discussed. Product distribution and allocation of different customers along with sensitivity of the key parameters and performance of the model are discussed through a numerical example. The results illustrate that profit achieved by the MILFP model is slightly higher than mixed integer programming (MIP) model. It is observed that increase in the opening cost decreases the profit obtained by both MILFP and MIP models. If the opening cost of a location decreases or increases, the demand and capacity of the location changes accordingly. The opening cost dramatically changes the demand rather than the capacity of the product. Finally, a conclusion is drawn in favor of the MILFP model as a relevant approach in a logistic model searching for the optimum solution.