A data-driven optimization model to response to COVID-19 pandemic: a case study.

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.

Effective machine learning, Meta-heuristic algorithms and multi-criteria decision making to minimizing human resource turnover.

Employee turnover is one of the most important issues in human resource management, which is a combination of soft and hard skills. This makes it difficult for managers to make decisions. In order to make better decisions, this article has been devoted to identifying factors affecting employee turnover using feature selection approaches such as Recursive Feature Elimination algorithm and Mutual Information and Meta-heuristic algorithms such as Gray Wolf Optimizer and Genetic Algorithm. The use of Multi-Criteria Decision-Making techniques is one of the other approaches used to identify the factors affecting the employee turnover in this article. Our expert has used the Best-Worst Method to evaluate each of these variables. In order to check the performance of each of the above methods and to identify the most significant factors on employee turnover, the results are used in some machine learning algorithms to check their accuracy in predicting the employee turnover. These three methods have been implemented on the human resources dataset of a company and the results show that the factors identified by the Mutual Information algorithm can show better results in predicting the employee turnover. Also, the results confirm that managers need a support tool to make decisions because the possibility of making mistakes in their decisions is high. This approach can be used as a decision support tool by managers and help managers and organizations to have a correct insight into the departure of their employees and adopt policies to retain and optimize their employees.

Multi-objective programming and Six Sigma approaches for a competitive pharmaceutical supply chain with the value chain and product lifecycle.

This study examines two pharmaceutical supply chains (PSCs) under the product life cycle and marketing strategies for the first time. Nash equilibrium between PSCs is based on marketing mix factors (i.e., price, the value provided by the value chain, availability, and promotion) at different periods of product life (i.e., introduction, growth, and maturity). Considering the previous step's outputs, environmental protection, and sustainable development, this study provides a multi-objective mixed-integer nonlinear programming model (MOMINLP) for the design of PSCs to minimize environmental pollution and maximize profit, consumer health level, and brand equity. At this stage of the network design, disruption issues in the manufacturer, distributor, and retailer are considered. Based on the value from the value chain in different periods of product life, different scenarios are considered. Optimizing the supply chain network design (SCND) under uncertainty through the reliability and Six Sigma concepts is examined. The proposed approach is validated with a real-case study in Iran. The results show that the brand equity, pollution created, and supply chain profits decrease with increasing optimization levels. However, the level of consumer health rises with increasing levels of optimization. Based on the obtained results, the total profit of the two supply chains at the optimization level 3σ is 3.6% more than the profit at the optimization level 6σ. The total environmental pollution of the two supply chains at the optimization level 3σ is 1.9% less than the environmental pollution at the optimization level 1.285σ. The total consumer health level of the two supply chains at the optimization level 3σ is 3.3% more than the consumer health level at the optimization level 1.285σ. The total brand equity of the two supply chains at the optimization level 3σ is 2.5% more than the brand equity at the optimization level 6σ. It seems that the optimization level 3σ for the two pharmaceutical supply chains is more appropriate than the other optimization levels.

A multi-objective mixed integer linear programming model proposed to optimize a supply chain network for microalgae-based biofuels and co-products: a case study in Iran.

Recently, with efficient methods of cultivating microalgae at hand, researchers and investors have paid considerable attention to the production of different environmentally friendly biofuel products. In this study, a two-stage deterministic model is proposed to design a microalgae-based biofuel and co-product supply chain network (MBCSCN). In the first stage, appropriate locations to cultivate microalgae are identified through the analytical hierarchy process (AHP). In the second stage, a deterministic mathematical mixed integer linear programming (MILP) model is developed for a period of 5 years based on economic and environmental impacts as two criteria. The economic objective function maximizes the overall profit, while the objective function of the environmental impacts seeks to minimize fossil fuel consumption throughout the supply chain. Then, a multi-objective MILP optimization problem is solved using the ε-constraint method. The proposed model is evaluated through a case study in Iran. It has helped to identify appropriate locations for the cultivation of microalgae and to specify the required quantity of feedstock, the species of microalgae, the required technology, and the transportation modes in each step of the supply chain.

Bi-objective approach for placing ground and air ambulance base and helipad locations in order to optimize EMS response.

Shortening the travel time of patient transfer has clinical implications for many conditions such as cardiac arrest, trauma, stroke and STEMI. As resources are often limited precise calculations are needed. In this paper we consider the location problem for both ground and aerial emergency medical services. Given the uncertainty of when patients are in need of prompt medical attention we consider these demand points to be uncertain. We consider various ways in which ground and helicopter ambulances can work together to make the whole process go faster. We develop a mathematical model that minimizes travel time and maximizes service level. We use a compromising programming method to solve this bi-objective mathematical model. For numerical experiments we apply our model to a case study in Lorestan, Iran, using geographical and population data, and the location of the actual hospital based in the capital of the province. Results show that low-accessibility locations are the main focus of the proposed problem and with mathematical modeling access to a hospital is vastly improved. We also found out that once the budget reaches a certain point which suffices for building certain ambulance bases more investments does not necessarily result in less travel time.

Integrated locating of helicopter stations and helipads for wounded transfer under demand location uncertainty.

Health emergency medical service (HEMS) plays an important role in reducing injuries by providing advanced medical care in the shortest time and reducing the transfer time to advanced treatment centers. In the regions without ground relief coverage, it would be faster to transfer emergency patients to the hospital by a helicopter. In this paper, an integer nonlinear programming model is presented for the integrated locating of helicopter stations and helipads by considering uncertainty in demand points. We assume three transfer modes: (1) direct transfer by an ambulance, (2) transfer by an ambulance to a helicopter station and then to the hospital by a helicopter, (3) transfer by an ambulance to a predetermined point and then to the hospital by a helicopter. We also assume that demands occur in a square-shaped area, in which each side follows a uniform distribution. It is also assumed that demands in an area decrease errors in the distances between each two cities. The purpose of this model is to minimize the transfer time from demand points to the hospital by considering different modes. The proposed model is examined in terms of validity and applicability in Lorestan Province and a sensitivity analysis is also conducted on the total allocated budget.