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The Cash in Transit (CIT) deals with the money distribution and picking up between depot(s), central bank, bank branches, Automated Teller Machines (ATMs), jewelry stores, and exchange offices, safely and quickly. It is critical for companies carrying out CIT activities to identify risks and their priorities. It is one of the problems to be resolved for CIT companies to determine the risks that may occur on the route for each vehicle that will carry money from a certain center to the demand points. It is important for decision makers how critical these routes are concerning risks. Within the scope of the study, determining the risks, the weights of these risks, and high-risk routes accordingly are discussed. The literature review related to the problem is first consulted. After the literature review, interviews are made with experts on their subject. The main criteria and sub-criteria to define the risks are determined. Then, these criteria are weighted by the Interval-Valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIF-AHP), and risk assessment is made to alternative routes based on these weighted risks. The routes are evaluated by Interval-Valued Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IVIF-TOPSIS) methodology, and thus, it is determined which route has the highest risk and which route has the lowest risk. In this way, it is stated on which route should be used for the vehicles to distribute money and which routes should be improved. [ABSTRACT FROM AUTHOR]

The increased focus of people on the quality of health care in recent years has led hospital owners to develop strategies and policies to improve medical services through the establishment of new hospitals. For hospitals to be competitive, the hospitalʼs location and proximity to potential patients are considered crucial factors in establishing new hospitals. In this context, evaluating and selecting the most suitable hospital location to establish a new hospital from the multicriteria decision-making (MCDM) perspective is a priority for the entrepreneurs or government to gain a competitive advantage. Therefore, this study aims to present a systematic literature review of the hospital location selection problem considering the applied methods and application areas. The preferred reporting items for systematic review and meta-analysis statement (PRISMA) are used as a reference framework. Initially, known electronic databases (Web of science, IEEEXplore, Scopus, Science direct, and Google Scholar) were searched up to the early 2021. A number of 47 articles are selected and analyzed under this systematic framework based on inclusion-exclusion points. State-of-the-art developments in adopting MCDM methods and their fuzzy extensions are summarized. All the articles have been examined in a systematic taxonomy to find answers to six research questions (trend, country of origin, outlet journal, MCDM methods used, MCDM environment and criteria type, and decision criteria used). Results show that (1) AHP and GIS-based MCDM models are the most contributing approaches to the solution of this problem, (2) location selection criteria are mostly cost, demand, environment, population, government, competition in the market, and distance to some important places, (3) the fuzzy structure is also preferred in addition to the MCDM structure depending on the crisp data type, and (4) the location selection criteria are mostly considered subjective. We pay attention to promising directions that can dominate future research in this field from a methodological or applicability perspective. This study shows the current views and opportunities for researchers and practitioners and acts as a guide to encourage more creative studies in this field. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) face high numbers of patient arrivals in comparison to other departments of hospitals because they provide non-stop service. Patient arrivals at these departments mostly do not appear in a steady state. Predicting existing uncertainty contributes to the future planning of these departments. Therefore, forecasting patient arrivals at emergency departments is crucial so as to make short and long term plans for physical capacity requirements, staffing, budgeting and arranging staff schedules. In this paper, variations in annual, monthly and daily ED arrivals are analyzed based on regression and neural network models with the aid of a collected data from a public hospital ED in Istanbul. The results show that ANNbased models have higher model accuracy values and lower values of absolute error in terms of forecasting the ED patient arrivals over the long and medium terms. The paper is also aimed to provide ED management and medical staff a useful guide for future planning of their emergency departments in the light of an accurate forecasting. [ABSTRACT FROM AUTHOR]

This paper aims to review what is known about the preparedness of hospital emergency departments (EDs) for a possible major earthquake expected in the near future in Istanbul and reveal the past experience and future plans from employees' viewpoints and, as well as from the related literature. For this purpose, potential effects of the earthquakes on ED operations, patient flow, and performance are discussed considering the statistical data of recent earthquakes in Turkey, a comprehensive report on the disaster preparedness of Istanbul and, one to one interviews with the earthquake experienced ED medical staff. In particular, issues on patient surge, triage and registration, communication, patient flow and transportation and, lack of enough resources and space to treat are presented as the main challenges. According to the viewpoints obtained from various sources, EDs in Istanbul are not ready to meet the needs caused by a major earthquake. It is proposed that operational research (OR) techniques can be useful to overcome these issues such as simulation modeling. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) have faced with high patient demand during peak hours in comparison to the other departments of hospitals because of their complexity and uncertainty. Therefore prolonged waiting times in EDs have caused the dissatisfaction on patients. Patient length of stay (LOS), also known as patient throughput time, is generally considered to be the length of time that passes from the patient's time of arrival at the ED until time of discharge or transfer to another department of the hospital. Starting from patient admissions to the EDs it becomes important have to be known the overall LOS in terms of right resource allocation and efficient utilization of the department. For this purpose this paper aims to forecast patient LOS using Artificial Neural Network (ANN) within the input factors that are predictive such as patient age, sex, mode of arrival, treatment unit, medical tests and inspection in the ED. The method can be used to provide insights to ED medical staff (doctors, nurses etc.) determining patient LOS. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a discrete-event simulation (DES) model of an emergency department (ED) unit in a regional university hospital in Turkey. In this paper emergency department operations of the hospital were modeled, analyzed and improved. The goal of the study is to reduce patient average length of stay (LOS) and to improve patient throughput and utilization of locations and human resources (doctors, nurses, receptionists). Some alternative scenarios in an attempt to determine optimal staff level were evaluated. These alternative approaches illustrate that vital improvement in LOS and throughput can be obtained by minor changes in shift hours and number of resources. Considering future changes in patient demand a scenario which reduces LOS and improves throughput is available in the paper. Significance: The Key Performance Indicators (KPIs) to determine and improve system performance in healthcare emergency departments consist of to reduce patient average length of stay (LOS), to improve patient throughput and resource utilization rates. Alternative scenarios and optimal staff levels are enhanced within the scope of this study. [ABSTRACT FROM AUTHOR]

Abstract: In this study, an integrated supply chain (SC) design model is developed and a SC network design case is examined for a reputable multinational company in alcohol free beverage sector. Here, a three echelon SC network is considered under demand uncertainty and the proposed integrated neuro-fuzzy and mixed integer linear programming (MILP) approach is applied to this network to realize the design effectively. Matlab 7.0 is used for neuro-fuzzy demand forecasting and, the MILP model is solved using Lingo 10.0. Then Matlab 7.0 is used for artificial neural network (ANN) simulation to supply a comparative study and to show the applicability and efficiency of ANN simulation for this type of problem. By evaluating the output data, the SC network for this case is designed, and the optimal product flow between the factories, warehouses and distributors are calculated. Also it is proved that the ANN simulation can be used instead of analytical computations because of ensuring a simplified representation for this method and time saving. [Copyright &y& Elsevier]

Abstract: In supply chain management process, the firm select best supplier takes the competitive advantage to other companies. Thus, supplier selection is an important issue and with the multiple criteria decision-making approach, the supplier selection problem includes both tangible and intangible factors. This paper is aimed to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient. Finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. The integrated model is illustrated by an example in a textile firm. [Copyright &y& Elsevier]

Abstract: In this paper, for effective multi-echelon supply chains under stochastic and fuzzy environments, an inventory management framework and deterministic/stochastic-neuro-fuzzy cost models within the context of this framework are structured. Then, a numerical application in a three-echelon tree-structure chain is presented to show the applicability and performance of proposed framework. It can be said that, by our framework, efficient forecast data is ensured, realistic cost titles are considered in proposed models, and also the minimum total supply chain cost values under demand, lead time and expediting cost pattern changes are presented and examined in detail. [Copyright &y& Elsevier]

Abstract: Multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) are used to model economic analysis of risky projects and are presented in this paper. Analytical models of risky projects are investigated and neural network function approximation results are compared. A general, problem independent ANNs are developed for the normalized input values for risky projects. The expected cost value and variance are the outputs of the ANNs. The simulation results of RBF and MLP with respect to a mathematical model are shown and concluded. [Copyright &y& Elsevier]

Introduction: Underground mining is considered one of the most hazardous industries and is often associated with serious work-related fatalities; this paper addresses job-related hazards and associated risks. Method: A risk assessment approach is proposed (Pythagorean fuzzy environment) and a case study is carried out in an underground copper and zinc mine. Results: Results of the study demonstrate that hazards can be categorized into different risk levels via compromised solutions of the fuzzy approach. Conclusion: The study provides a theoretical contribution by suggesting a Pythagorean fuzzy numbers-based VlseKriterijumska Optimizacija I Kompromisno Resenje (PFVIKOR) approach. Moreover, it contributes to improving overall safety levels of underground mining by considering and advising on the potential hazards of risk management. Practical applications: The proposed approach will improve the existing safety risk assessment mechanism in underground copper and zinc mining. Unlabelled Image • We propose a risk assessment approach in Pythagorean fuzzy environment. • L-matrix and VIKOR methods are used in the proposed approach. • A case study is carried out for mining hazards and risk. • A sensitivity and comparison analysis is provided for validation. • A risk evaluation including suggested preventive action plans are given. [ABSTRACT FROM AUTHOR]

Occupational Health and Safety (OHS) is a basis to reduce occupational accidents in an acceptable level, and it covers employee health, safety, and welfare in the workplaces. Hospitals as the largest employer group in healthcare industry in Turkey face major hazards categorized as chemical, biological, physical, ergonomic, and psychosocial risks. Although Turkey demonstrates rapid economic growth, OHS practices have not been fully put into practice, and necessary attention has not been paid to the health industry. For this reason, this paper aims to assess risks for health staff, contribute for planning of health services, and enhance regulations. A case study was carried out in a leading hospital in Turkey using a two-stage fuzzy multi-criteria approach, which provides more consistency in decision-making process and gives an appropriate final rank of hazard types. Fuzzy Analytic Hierarchy Process (FAHP) is used in weighing five risk parameters, which are severity, occurrence, undetectability, sensitivity to maintenance non-execution, and sensitivity to personal protective equipment (PPE) non-utilization. The fuzzy VIKOR (FVIKOR) approach is then applied for prioritization of hazard types in each department of the hospital. On conclusion of the hazard control hierarchy, measures are overtaken for the hazards, and areas open for improvement are presented. [ABSTRACT FROM PUBLISHER]

Multi criteria decision making (MCDM) is a discipline of operations research which has widely studied by researchers and practitioners. It deals with evaluating and ranking alternatives from the best to the worst under conflicting criteria with respect to decision maker(s) preferences. Since, many real-world systems include uncertainty and vagueness in information, MCDM uses fuzzy sets. In recent years, as an extension of the traditional fuzzy sets concept, type-2 fuzzy sets are preferred to have the capability of handling more uncertainty, and hence, to produce more accurate and robust results, MCDM approaches based on interval type-2 fuzzy sets (IT2FSs) have been published in various subjects. This paper reviews 82 different papers using various MCDM approaches based on IT2FSs which are classified into 35 categories. All papers with respect to single and hybrid approaches are discussed, pointing out their real applications or empirical results and limitations. Furthermore, the papers are statistically analyzed to show new trends within the context of IT2FSs. This systematic and comprehensive review study provides an insight for researchers on interval type-2 fuzzy MCDM in terms of showing current state and potential areas to be focused in the future. [ABSTRACT FROM AUTHOR]