Scientia Iranica
Volume:30 Issue: 1, Jan-Feb 2023

The rapid growth of the population has resulted in an increasing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title for problems that consists of the patient selection and sequencing of the surgeries at the operational level, setting their start times, and assigning the resources. Hospital managers usually encounter elective surgeries that can be delayed slightly and emergency surgeries whose arrivals are unexpected, and most of them need quick access to operating rooms. Reserving operating room capacity for handling incoming emergency surgeries is expensive. Moreover, emergency surgeries cannot afford long waiting times. This paper deals with the problem of surgery scheduling in the presence of emergency surgeries with a focus on balancing the efficient use of operating room capacity and responsiveness to emergency surgeries. We proposed a new algorithm for surgery scheduling with a specific operating room capacity planning and analyzed it through a simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite.

Interval-valued Pythagorean fuzzy sets (IVPFSs) as enhanced type of Pythagorean fuzzy sets (PFSs) improve the expression of the degrees of membership, non-membership and hesitancy in comparison with intuitionistic fuzzy sets (IFSs). In this paper, to use the advantages of IVPFSs a new group decision-making method is introduced based on linear assignment method (LAM). In this approach, subjective and objective weights of criteria are taken into account. Moreover, the method applies a new ranking method for IVPFSs. To avoid the shortcomings of first aggregation methods, the introduced decision-making approach involves a last aggregation approach. Finally, the method is used in a case study of sustainable project evaluation in order to depict the applicability of this method.

In this paper, we develop two acceptance sampling plans where the lifetimes of the product follow Weibull Exponential and Weibull Lomax distributions both belonging to the new truncated Weibull–X family of distributions based on run lengths of the conforming items. The model parameters are estimated by using the maximum likelihood method contrary to the existing plans where authors have been selecting arbitrary values of the parameters. The efficiency of the proposed plan is established by comparing it with the existing plans based on the average number of inspected items. A real example of failure rates of a piece of electronic equipment operating in a specific mode is presented to illustrate the proposed plans for industrial use.

Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.

Recent financial crises have strained the performance of different firms and it has challenged investors to invest in the stocks of these firms. Measuring the resilience of firms from a financial standpoint in terms of crises is an important indicator for investors. It is logical that investing in firms with higher historical financial resilience is more attractive for investors. In the literature the resilience is defined as the ability of anticipating, preparing, responding and adapting to incremental change and sudden disruptions in order to survive and prosper. In this paper, the concept of financial resilience has been studied from various dimensions and its quantification approaches are examined. The models developed in this paper are for calculating financial resilience in terms of key indicators, Value at Risk (VaR), and Conditional Value at Risk (CoVaR). Then, by comparing each of these methods, it has been tried to verify the methods by applying quantitative data of four bankrupt and four non-bankrupt firms listed on Tehran stock exchange (TSE) in recent years. The results show the proper performance of the proposed measure in expressing the concept of financial resilience in critical conditions.

In this article, a mathematical model is proposed for the joint planning of maintenance policies and inventory control in a deteriorating production system. A safety stock is maintained to meet the demand during the conduction of maintenance actions and to avoid shortages. The optimal planning of maintenance and inventory improves the productivity of the manufacturing system. In a deteriorating production system, the process has two operational states including in-control and out-of-control states as well as a non-operational state, or failure mode. The time for the transition among the states follows a general continuous distribution. The time duration of maintenance actions is also considered as a random variable. The purpose of this study is to optimize the safety stock level and the time to conduct maintenance actions so that the expected total cost per time unit can be minimized. To verify the efficiency of the model, some numerical examples are solved with a genetic algorithm, and validation is conducted for the solutions. Finally, sensitivity analyses are performed on the critical parameters.

Rice is a strategic commodity in the food chain for the people and governments. It is a fundamental food for many societies. Moreover, producing rice can provide a reliable source of revenue if proper supply chain management is coordinated by farmer countries. The rice supply chain includes diverse elements such as farms, rice mills, distribution centers, and markets. This study examines the important factors that play a significant role in the rice supply chain. A bi-objective mathematical model is formulated to minimize total costs as an economic goal and minimize soil erosion and its destruction due to the consumed water for rice cultivation as an environmental goal. To verify the proposed model, a case study of the rice supply chain with limited producer farms has been investigated. Moreover, some parameters such as annual precipitation in production areas along with other factors are presented under several scenarios. Furthermore, an extended goal programming approach and stochastic programming are utilized to solve the proposed model. Finally, the sensitivity analyses of the important parameters have been performed.