International Journal of Industrial Engineering & Production Research

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Abstract Purpose of this paper: The objectives of this paper are two folds: (1) utilizing hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach to evaluate the most suitable RFID-based systems decision, and (2) to highlight key risks and benefits of radio frequency identification technology in healthcare industry. Design/methodology/approach: Researcher explains the importance of selection criteria for evaluation of RFID-based system. It provides key elements on radio frequency identification, fuzzy hierarchical TOPSIS methodology and an algorithm that can be followed to solve the problem. A sample problem using the algorithm is solved and results are explained. Findings: The hierarchical TOPSIS model used in this article is able to grasp the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. The use of the hierarchical fuzzy TOPSIS methodology offers a number of benefits: (1) it is a systematic model and straight forward to work on (2) capable to capture the human's appraisal of ambiguity when management should deal with a complex multiple objective situations. The hierarchical fuzzy TOPSIS is in some way superior to the other Fuzzy multi criterion decision making techniques, such as fuzzy analytic hierarchy process (FAHP) and classical fuzzy TOPSIS methods. This is because while in the hierarchical structure no pair-wise comparisons among criteria, sub-criteria, and alternatives are necessary to be made, it is already being taken into consideration by the model. Hierarchical fuzzy. Practical implications (if applicable): What is original/value of paper: Due to the fact that a better management of health care system is related to the full understanding of the technologies implemented and the system under consideration, sufficient background on the radio frequency identification technology is provided and the RFID systems most likely management would face with and select one are provided for decisions to be made on them. Key Words: RFID Technology, RFID-based system selection, healthcare applications, hierarchical fuzzy TOPSIS.

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Abstract Purpose of this paper: This paper presents an analysis toward understanding the business value components that a health care organization can drive by adopting RFID technology into its system. This researcher proposes a framework for evaluating the business value of RFID technology. To do so, emphasis is put on delivering business value through refining business processes and expanding the business model. Author illustrates the concepts drawing on the experience of nine case studies already presented on the health care topics and service industries. Thereafter, a framework, as a set of propositions based on relevant literature, case studies from the field, and author's intuition, is formulated. The proposed propositions need to be validated through empirical evidence. To fully understand this topic some applications of radio frequency identification in health care management and other industries are briefly discussed and nine cases from health care industry are studied. Design/methodology/approach: Identifies key elements of radio frequency identification through the review of healthcare management literature and case studies. For this purpose, nine cases are reviewed from the health care industry and then key features of those cases are employed to determine four proposals for further studies. Findings: To make healthcare management systems functional and successfully operational we can use radio frequency identification solutions to reduce operating costs through decreasing the labor costs, including automation, improving tracking and tracing, and preventing the lost of materials under any circumstances. Practical implications (if applicable): What is original/value of paper: The emphasis is put on delivering business value through refining business processes and expanding the business model. A framework, as a set of propositions based on relevant literature, case studies from the field, and author's intuition is formulated and presented for further studies using true case studies.

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The eternal need for humans' blood as a critical commodity makes the healthcare systems attempt to provide efficient blood supply chains (BSCs) by which the requirements are satisfied at the maximum level. To have an efficient supply of blood, an appropriate planning for blood supply chain is a challenge which requires more attention. In this paper, we address a mixed integer linear programming model for blood supply chain network design (BSCND) with the need for making both strategic and tactical decisions throughout a multiple planning periods. A robust programming approach is devised to deal with inherent randomness in parameters data of the model. To illustrate the usefulness of the model as well as its solution approach, it is tested into a set of numerical examples, and the sensitivity analyses are conducted. Finally, we employ two criteria: the mean and standard deviation of constraint violations under a number of random realizations to evaluate the performance of both the proposed robust and deterministic models. The results imply the domination of robust approach over the deterministic one.

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Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Since the developed model of the problem is of an NP-hard type, tri-meta-heuristic algorithms are proposed to solve the problem. Initially, Pareto-based meta-heuristic algorithm called multi-objective simulated annealing (MOSA) is proposed in order to solve the problem. To validate the results obtained, two popular algorithms namely, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, Taguchi method has been utilized to fine tune the parameters of all algorithms. The computational results, obtained by implementing the algorithms on several problems of different sizes, demonstrate the reliable performances of the proposed methodology.

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Abstract
Background: In this paper healthcare condition of 31 countries that are the members of Organization for Economic and Co-operative Development (OECD) is measured by considering 14 indicators that are relevant to three main pillars of sustainable development.
Method: To estimate the efficiency scores, Principle Component Analysis-Data Envelopment Analysis PCA-DEA additive model in both forms of envelopment and multiplier is used to determine efficiency scores and also to define benchmarks and improvement plan for the inefficient countries. Then Decision Tree Analysis is also used to realize that which factors were the most influential ones to make a county an efficient Decision Making Unit (DMU).
Results: According to the PCA-DEA additive model, among 31 OECD countries, 16 countries have become inefficient, that USA have taken the lowest efficiency score, and among efficient countries Iceland could be considered as a paragon which has the highest frequency between the countries that are defined as the benchmarks. Decision tree analysis also show that exposure to PM2.5 is an influential factor on the efficiency status of countries.
Conclusion: This research gives an insight about the sustainable development and healthcare system and show the impressive effect of environmental and social factors like: exposure to PM2.5 and water quality, population insurance coverage, and AIDS on the healthcare efficiency of OECD countries

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Background: The continuous growth of healthcare and medicine costs as a strategic commodity requires tools to identify high cost populations and cost control. After the implementation of the healthcare Reform plan in Iran, a huge share of hospital funding has been spent on undesirable costs due to changes in the use of medicines and instruments.
Objective: The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.
Method: Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions information. Then using normalized risk function to identify abnormal and high cost cases based on the distance between the input data and the mean of the data. The data used are 15078 records, including information from patients' prescriptions from Shari'ati HIS in Tehran-Iran from 2012 to 2016.
Results: According to the obtained results, the proposed model has a positive Likehood ratio (LR+) of 6.35.
 

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This paper presents an exploratory study on the development of lean readiness index for Malaysian hospitals. A questionnaire survey were obtained from 118 public hospitals and lean readiness model was developed using structural equation modeling (SEM) and the relevant constructs were identified using confirmatory factor analysis. The Lean Readiness Index (LRI) is formulated and a ruler in associate with the LRI were proposed as to meet the objective of the study. The finding to emerge from this study is that only 10.1% of Malaysian public hospitals have ‘good’ readiness status. The study also revealed the overall LRI’s value is 0.617 and, the majority of the hospitals were categorize as having ‘fair’ and ‘weak’ readiness status. The result indicated that training had the strongest association towards lean readiness while communication is the least. This study had revealed the readiness level for lean implementation in Malaysian public hospitals and proposed the required foundation that need to be enhanced before implementing lean.