Article Preview
Top1. Introduction
The blood distribution problem is an important issue for optimizing performance of the healthcare systems. While some improvements are conducted for technological aspect of alternatives for blood products, the necessity required for donor blood and its related products will always be studied. Blood cannot be a normal product. The distribution of blood products is not a common plan because the demand for blood commodities is uncertain. Balancing between producing and demand is an important framework. Blood commodities are also perishable, which make difficulties for planning and managing. Backorders of blood donors can lead to high costs for people since they can lead to intensification mortality degrees. Instead, outdates products are not usually useable either, because blood donors must respect a determined time among two donations, according to the kind of donation. These show why we focused on this problem to develop a new optimization model in this field (Beliën and Forcé, 2012).
Undoubtedly, a good quality in locating facilities has considerable effects on economic aspects of facilities, providing good service and customers’ satisfaction. Therefore, analysis of location-allocation problems is interesting for researchers. The decision about locating facilities is recognized as a critical element of strategic planning of companies. Optimal locating of facilities is very important in industry and in the health system, simultaneously.
In industrial section, poor locating and unbalanced use of facilities may increase costs and reduce service quality to customers. The compact form of establishment of facilities increases inventory costs and the sparse establishment of facilities has a negative effect on customer service. Even if the optimal numbers of facilities are used, the scattered pattern of the establishment of facilities may reduce the availability of service and waste assets’ values. Poor decisions in the health facilities in locating have impacts beyond customer service and cost issues. Underutilization of the facility or establishment in inappropriate locations would increase health-related problems (Zanjirani Farahani et al., 2014).
Many efforts are done in some countries in order to establish an appropriate health system which can respond to all the needs of society. In addition, during a natural disaster, demand of blood products increases, so that supply of those products is considered as an important factor in that condition.
So, determining the most appropriate locations for blood donation centers facilitates the effectiveness of supply chain. Furthermore, possible change of parameters such as demand, costs of resettlement during the planning horizon are considered as main disadvantages of a static model. Because by changing some of the parameters, the solution is no longer optimal for the problem, so facilities need to be assigned to other locations and it takes time and costs.
Since certain assumptions lead to solutions that are far from optimal and feasible one in the real-world problems, it is assumed that total amount of donated blood for each area is not definite. Also, the amount of blood that each hospital needs cannot be conclusively predicted. The ratio between different expiration dates and blood depends on blood donations during different periods.
According to previous studies, in health products’ supply chain, there are no significant studies with application of fuzzy parameters up to now. Besides, so far it can be claimed that the study of the fuzzy logic associated with blood has not been used in locating a blood donation center. In this paper for the first time, a dynamic hierarchical location-allocation model with fuzzy conditions is offered to locate blood donation centers and assign blood donors to these centers. The possible limited lifespan of blood is considered as the most important model assumption using fuzzy theory. This research focuses on optimal locating of blood donation centers and optimized allocation of donors to these centers. These centers are categorized into two types: mobile and fixed centers.
In the rest of this paper, section 2 indicates a literature survey and gap of the previous studies. Section 3 shows the certain mathematical formulation. Section 4 denotes applying fuzzy approach and fuzzy mathematical formulation. Section 5 illustrates numerical results including real case study and sensitivity analysis. Finally, section 6 summarizes the paper and results.