جستجوی مقالات مرتبط با کلیدواژه « bi-objective optimization » در نشریات گروه « فنی و مهندسی »

Supply chain coordination is extensively used as an effective tool for firms to stay competitive and improve their profitability. However, this solution may fail if the financial problems of the firms are ignored in the operations. To solve this challenge, we have proposed a coordination scheme using internal financing for a three-level supply chain facing some financial constraints. This case is modeled as a bi-objective optimization problem that optimizes both system’s costs and service level. We have solved the model using εconstraint method. The results show that the proposed coordination scheme can provide full service level (100%) to the customer, while the noncoordinated system cannot reach more than 50% of the service level. Furthermore, the system’s operational and financial costs are reduced by coordination.

Cancer, one of the leading causes of death in the world, has been on the rise in recent years and is expected to continue in the coming years. Therefore, the importance of developing a cancer control program and the need to provide effective methods is very important. Given that radiation therapy is an effective way to treat cancer, studies on this method of treatment are important and significant. The main purpose of this paper is to provide a mathematical model for the latest method of radiotherapy called volumetric modulated arc therapy. In most studies in this field, due to the complexity of the model, the purpose of the problem is to maximize the dose received in the target area or to minimize the dose received in the area organs at risk. In this study, both objectives are considered together and a bi-objective model is presented. The results of the 8 studied instances show that the dose received in the target area is significantly higher than the dose received in the area around the tumor. In addition to increasing the dose received in the target area and decreasing the dose received in the cells around the target, the distribution of the dose in the voxels is very important, so the dose was examined with the coefficient of variation. The results show that the proposed model with a coefficient of variation of less than 10% has a conformal dose distribution in all tissues (cancerous tissue and healthy tissue).

The primary objective of a typical hierarchical facility location problem is to determine the location of facilities in a multi-level network in a way to serve the customers at the lowest level of hierarchy. Nowadays, hierarchical facility location models have been widely applied in public facility location problems. In most of such cases, a developed model may need to deal with the relocation of existing facilities along with the construction of new facilities. This further acknowledges the need to focus on solving relocation hierarchical facility location problem using innovative approaches such as dynamic time elements. A facility is an establishment providing services; its level is defined by the highest level of service it offers. Low level services can be supported by a relatively small population. Also, those facilities can be located relatively densely in space. High level services require a large supporting population; they can only be located sparsely in space. Successive inclusiveness means that facilities of each level offer the services available at all lower levels of facility as well as those that require at least that level of facility. Although, the systems of facilities usually exist as hierarchical systems, location problems have been mostly studied for single-level systems. Hierarchical systems have to decide about the locations of their interacting facilities within a multiple layer configuration. Systems with a hierarchical structure are common both in public and private sectors. In this paper, we present the hierarchical facility location-allocation with two layers, Because of demand congestion in service networks, an M/M/1/K queuing system is considered. We assume that the capacity of each facility is limited. Furthermore, servers of each level offer a different service and Users can go to the higher level server without a low-level server refers them to it. We formulate the problem as nonlinear integer-programming models and solve model with GAMS and Global Criteria's technique. The paper finally identifies the gaps for future modeling efforts.

Natural disasters cause to make a vast amount of relief items demand in affected areas. Reducing the waiting time of injured people for emergency supplies is one of the main issues in post-disaster emergency response. Blood is one of these items, which has a vital role in preserving affected people's life. Therefore, in post-disaster situation, creating a queue of injured people in order to receive blood services in hospitals is expected. Thus, designing a supply chain network that considers waiting time for blood supply while minimizing total cost is a challenging problem. In this paper, a bi-objective mixed integer nonlinear-programming model is proposed, which uses queuing theory to incorporate more realistic waiting time. This supply chain consists of five echelons: donors, blood collection facilities (permanently and temporary), blood center, demand points (hospitals), and injured people. Location-allocation, inventory level, blood shortage in some echelons, flow of blood in the network and waiting time are related decisions that are optimized in this model. With respect to variation in some parameters such as demand, a multi-period context is more effective to cope with these variations. In order to better manage blood collection, temporary blood facilities can move in a set of candidate points at the beginning of each period. Moreover, multiple allocations of donors to capacitated blood collection facilities and blood center are allowed by considering the covering radius of facilities. Finally, the performance of the proposed model is investigated by a practical numerical example. Moreover, several sensitively analyses are conducted. According to the model results, optimized allocation of injured people to hospitals and servers leads to reduction of queue length as well as waiting time. This improvement will be considerable when the intensity of disaster is high and a large number of injured people are transported to hospitals. Furthermore, the effectiveness of shortage cost and service time on objective functions is explored in the sensitivity analysis section.