Scientia Iranica
Volume:30 Issue: 5, Sep-Oct 2023

Home health care service has significant importance in modern societies. In most of the active institutions in this field, the traditional procedure is used for planning and managing health personnel and determining patient visit sequence. This procedure usually causes an increase in costs and reduces patients’ satisfaction. This paper, for the first time, groups the patients in a model according to the level of emergency and discriminating in their examination. Considering dependency and independence of patient visits to each other, assuming multi-depot and multi-period issues are attractive aspects of the proposed model. The model is solved with GAMS software for small scale and two variable neighborhood search algorithm and simulated annealing algorithm are used to solve large scale problems and their performances are compared. The results indicate minimizing total cost and also increasing patients` satisfaction by the proposed model.

Suppliers as one of the main sources of vulnerability may lead to disruption and risk in supply chains. Thus, resilient supplier selection can lead to an increase in the resilience of the supply process, especially in automotive supply chains. The goal of this study is to select a set of resilient suppliers and optimal demand allocation in an automotive supply chain under risk. For this purpose, a bi-objective two-stage stochastic programming model is presented. In contrast to previous mathematical models, our model includes a new objective function to consider the supplier’s delivery performance as one of the criteria of resilient supplier selection and also the k-means clustering method is used to cluster and decrease the number of disruption scenarios. In the proposed model, due to the uncertainty of demand, chance-constrained programming approach has been utilized. The augmented Ɛ-constraint method is implemented to solve the presented model. Finally, sensitivity analysis has been done to determine the effect of parameter changes on the final results. The results of the research indicate that contingency planning can reduce the effect of disruption risks. The findings also show that the strategy of the supply chain regionalization is important in reducing the effects of environmental disruption.

With the development of the economy and society, the scale of cities is increasing. At the same time, there are many subways being constructed in many cities. In the construction of subways, an appropriate scheme plays an important role in reducing cost and improving the satisfaction of the public. This paper attaches great importance to present a multi-criteria group decision-making (MCGDM) method to deal with selecting an appropriate construction scheme for subways. The process of selecting an appropriate construction scheme for subways is complex because it includes a great deal of fuzzy and uncertain information which can be presented by multi-valued neutrosophic numbers (MVNNs). In addition, in order to handle the interaction of inputs, an improved generalized multi-valued neutrosophic weighted Heronian mean (IGMVNWHM) operator is introduced. Subsequently, a new distance measure between two MVNNs is defined for deriving the objective criteria weights. Furthermore, considering the bounded rationality of decision-makers, we develop an improved multi-valued neutrosophic MULTIMOORA method based on prospect theory (IMVN-PT-MULTIMOORA). Finally, an application example of selecting an appropriate construction scheme for a subway and the influence of parameter are described. In addition, the proposed approach is compared with some existing methods to prove its validity and advantages.

It is clear that the location of the BTS antennas plays a very important role in the proper service and coverage of the mobile connection. Proper location of these antennas is a major challenge for operators in each country, as in addition to maximum network coverage, service costs must also be acceptable and competitive. This means that in busy areas, in order to provide better service, the number of antennas must be greater and closer to each other. In general, the location problem is a type of optimization problem that aims to select a subset of the set of candidate locations to create the facilities that provide the best service at the lowest cost. To solve such problems in a reasonable time, we can use meta-heuristic algorithms to find solutions that are very close to the optimal solution. Accordingly, this paper attempts to apply the genetic algorithm to find a suitable solution for finding BTS mobile antennas in north Kermanshah. A GA model is proposed that improves the location coordinates of the current BTS antennas extracted from the GIS system. Comparison of model results with the status of BTS active antennas in Kermanshah shows the performance of the model.

Oil waste, is one of the most important pollutants in the oil and gas industry. Since the wells' oil has significant saltwater, the effluent amount increases with increasing oil reservoir extraction. Separating the saltwater from the extracted oil before starting the refinery process plays an essential role in reducing the oil costs as well as the useful transfer capacity. This paper presents a new Chapman-Kolmogorov Equation-based (CKEB) method to evaluate a desalination system's availability with three-state equipment and weighted-k-out-of-n configuration. In this system, the equipment is repairable, and each repair facility can repair all equipment types of different sub-systems (pump stations). We consider all failures and repairs to have a constant rate (with Exponential distribution) and use the Chapman-Kolmogorov Equation to drive the system’s availability. Then we validate the presented method using a simulation technique. Finally, the elapsed time of both solving techniques is compared. The results show the superiority of the CKEB technique in terms of computational time. Compared with the simulation technique, the computational time ratio for the CKEB method is between 0.0002% to 0.0058% for the small-size problems, between 0.05% to 0.94% for the medium-size problems, and between 1.31% to 5.39% for the large-size problems.

The absence of systematic disparities in health utilization leads to achieving equity in health. However, equity in delivering healthcare services is always challenging because of financial and medical resource constraints. In this regard, a practical multi-objective mixed-integer linear programming model with priority-differentiated demand classes is presented for cost-effective inventory management of blood products considering health equity. The system deals with multiple substitutable products. There are elective and non-elective demands, which are categorized into three main classes based on medical urgencies. The health objectives are investigated to achieve a desirable health equity level in delivering healthcare services to patients. Moreover, the economic objective is pursued to minimize total costs incurred across managing the inventory without weakening the service level. An effective demand-oriented hybrid heuristic is proposed to issue and allocate the blood for demand satisfaction equitably. A goal programming approach is utilized to find the optimum solution. The applicability of the model is validated through a real case study. Finally, several sensitivity analyses are conducted to gain useful managerial insights. According to the results, the proposed model presents a proper solution by making a reasonable health-economic trade-off. Also, the results illustrate the beneficial improvement in patient care and promoting health equity.

The increasing value of facilities, on the one hand, and the complexity of the equipment used in them, on the other, have increased the importance of planning for the maintenance of facilities, especially for companies which their facilities are located in different locations. In this paper, a new hybrid model has been presented to optimize facility maintenance scheduling by a combination of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Monte Carlo Simulation for organizing facilities which are in different locations as well as determining the optimum number of crews with three different skills of mechanical, electrical and simple workers. The main contributions of this paper include: (a) optimizing the number of crew by different skills in the first stage. (b) evaluation of fitness value for each solution through the Monte Carlo Simulation Model. (c) scheduling by consideration different failure rates for different facilities in different locations. In order to evaluate the performance of the proposed model, the model has been compared with Golpira’s model, the results of which have shown that it is possible to reduce the cost by just over 39% and reduce MTBF by over half.