multi-objective possibilistic programming

In recent years, the need for blood and its products has increased due to various reasons including traffic accidents, natural disasters, spread of diseases such as cancer, etc. A very important issue regarding blood and blood products is their perishability. Excessive blood collection causes waste, while insufficient blood supply leads to shortages at demand points, which can result in irreparable costs. Given that the supply of blood from donors is not regular and continuous and the demand for blood products is not certain in the real world, appropriate planning for the collection and production of blood and its products has attracted the attention of many researchers. Among blood products, platelets are the most perishable blood product, with a lifespan of five days. In this paper, a mixed linear programming (MILP) model under supply and demand uncertainty is presented for the integrated design problem of platelet collection and production network in blood supply chain, which aims to minimize the total network costs and maximize the quality of platelets produced. The specific characteristics of this blood product, such as types of donors and two    different platelet production methods (whole blood and apheresis), are considered. In order to solve the model using a probabilistic programming approach, the initial non-deterministic model is converted into a deterministic model and the model is solved exactly by the CPLEX solver in GAMS. The efficiency of the model is demonstrated with a case study related to the blood network of Tehran.

Through a cross-docking strategy in logistics, goods are unloaded from inbound trucks and loaded onto outbound trucks with minimal storage. The management of equipment and manpower involved in unloading goods, moving them within the terminal, and reloading them on outgoing trucks is one of the most challenging aspects of cross-docking management. In this paper, a new bi-objective mixed-integer linear programming model is presented for scheduling incoming and outgoing trucks in a cross-docking terminal with flexible doors, where the distance between doors and the time for moving trucks inside the cross-dock are also taken into account. As a first objective, the proposed model attempts to minimize the total operation time, and as a second objective, it aims to manage the equipment and manpower required in the cross-docking terminal by minimizing the number of doors involved in unloading and loading operations. Considering the uncertainty of the parameters, triangular fuzzy numbers are used to deal with the uncertainty, and a hybrid solution approach is developed for solving multi-objective possibilistic programming problems. The proposed model and solving approach are used for scheduling incoming and outgoing trucks at a cross-docking terminal as part of a food and beverage-producing group, and the results are compared with two existing methods. The results show that the proposed method performs better compared to existing methods