International Journal of Industrial Engineering & Production Research

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Implementation of the risk management concepts into construction practice may enhance the performance of project by taking appropriate response actions against identified risks. This research proposes a multi-criteria group decision making approach for the evaluation of different alternative response scenarios. To take into account the uncertainties inherent in evaluation process, fuzzy logic is integrated into the revaluation process. To evaluate alternative response scenarios, first the collective group weight of each criterion is calculated considering opinions of a group consisted of five experts. As each expert has its own ideas, attitudes, knowledge and personalities, different experts will give their preferences in different ways. Fuzzy preference relations are used to unify the opinions of different experts. After computation of collective weights, the best alternative response scenario is selected by the use of proposed fuzzy group decision making methodology which aggregates opinions of different experts. To evaluate the performance of the proposed methodology, it is implemented in a real project and the best alternative responses scenario is selected for one of the identified risks.

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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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An equitable risk allocation between contracting parties plays a vital role in enhancing the performance of the project. This research presents a new quantitative risk allocation approach by integrating fuzzy logic and bargaining game theory. Owing to the imprecise and uncertain nature of players’ payoffs at different risk allocation strategies, fuzzy logic is implemented to determine the value of players’ payoffs based on the experience and subjective judgment of experts involved in the project. Having determined the players' payoffs, bargaining game theory is then applied to find the equitable risk allocation between the client and contractor. Four different methods including symmetric Nash, non-symmetric Nash, non-symmetric Kalai–Smorodinsky and non-symmetric area monotonic are implemented to determine the equitable risk allocation. To evaluate the performance of the proposed model, it is implemented in a pipeline project and the quantitative risk allocation is performed for the inflation risk as one of the most significant identified risks.

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This research aims at presenting a consolidated model of data envelopment analysis (DEA) technique and value engineering to select the best manufacturing methods for gate valve covers and ranking the methods using TOPSIS.To do so, efficiency evaluation indices were selected based on the value engineering approach and different manufacturing methods were evaluated using DEA technique.Finally, effective methods were ranked based on TOPSIS. Accordingly, 48 different methods were identified for manufacturing the part. The DEA results showed that only 12 methods have complete efficiency. Meanwhile manufacturing method No. 32 (A216 WCB casting purchased from Chinese market as the raw material, machining by CNC+NC and drilling by radial drill) was ranked the first.Major limitations of the research include time limitations, place limitation, lack of access to the standards adaptability index in different machining and drilling methods, limitation on evaluating all parts of a product, limitation on a technique evaluating efficiency and ranking, and mere satisfying with superior indices in each factor of value engineering. Most previous studies only evaluated efficiency of manufacturing methods based on a single approach.By applying value engineering, which is in fact a combination of three approaches (including quality approach, functional, and cost approaches), the present research provided a far more comprehensive model to evaluate manufacturing methods in industrial.

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In today’s competitive business situation, performance evaluation of firms is an extremely important concern of all the people who are typically stakeholders of the business game. In case of holding companies, this is a more important issue since the parent firm must permanently control the situation of its subsidiaries in their sectors to make appropriate investment decisions. This paper develops a multicriteria decision making (MCDM) approach for evaluating performance of firms considering financial and productivity criteria.. We adopt Fuzzy Analytic Hierarchy Process (FAHP) method to determine the relative importance of evaluation criteria, taking the vagueness and imprecision of human judgments into consideration. Then, we employ the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) for ranking of firms. Afterward, this paper enjoys benefit of using Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the validity of the obtained ranking results. Our approach was applied to a holding company listed in Tehran Stock Exchange (TSE) as a real case. The analysis of ranking results revealed advantages of combining these MCDM methods.

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Data envelopment analysis (DEA) and balanced scorecard (BSC) are two well-known approaches for measuring performance of decision making units (DMUs). BSC is especially applied with quality measures, whereas, when the quantity measures are used to evaluate, DEA is more appropriate. In the real-world, DMUs usually have complex structures such as network structures. One of the well-known network structures is two-stage processes with intermediate measures. In this structure, there are two stages and each stage uses inputs to produce outputs separately where the first stage outputs are inputs for the second stage. This paper deals with integrated DEA and game theory approaches for evaluating two-stage processes. In addition, it is an extension of DEA model based on BSC perspectives. BSC is used to categorize the efficiency measures under two-stage process. Furthermore, we propose a two-stage DEA model with considering leader-follower structure and including multiple sub stages in the follower stage. To determine importance of each category of measures in a competitive environment, cooperative and non-cooperative game approaches are used. A case study for measuring performance of power plants in Iran is presented to show the abilities of the proposed approach.

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Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. Regarding the fact that decision makers (DMs) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. This paper develops a nadir compromise programming (NCP) model for decision-making under uncertainty on the selection of suppliers within the framework of binary programming. Depending on the condition of uncertainty, three statuses are taken into consideration and a solution approach is proposed for each status. A pure deterministic NCP model is presented for solving the problem in white condition (certainty of data) and a solution approach resulted from combination of NCP and stochastic programming is introduced to solve the model in black (uncertainty of data) situation. The paper also proposes a NCP model under certainty and uncertainty for solving problem under grey (a combination of certainty and uncertainty of data) conditions. The proposed approaches are illustrated for a real problem in steel industry with multiple objectives. Also, a simulation approach has been designed in order to examine the results obtained and also verifies capabilities of the proposed model.

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Financial decision-making is the principal part of any decisions hence great efforts are done to improve the methods to assess and analyze the stock in financial markets as a part of the financial decision. This paper addresses the stock selection by discovering investor's utility function .Investors in the Stock Exchange consider diverse criteria to buy shares and bonds. Due to the criteria development in stock selection, understanding the investor's behavior by a consultant is a prominent issue. Recognizing an exclusive utility function according to the characteristics of the investors facilitates acquiring each share's value for the decision maker (DM) when it is required. In this study, UTASTAR method is used to estimate the marginal value function, using 3 appropriate criteria (risk, return, liquidity) and finally fit out the total utility function. It provides the opportunity to make a rational decision fit to investor's mentality and allowing their ranking, prioritization, selection or classification. The ranking of the options is as compatible as possible to the original one. The method is applied to an example from Iran Stock Exchange.

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The frequency of chronic diseases such as cardiovascular diseases has significantly increased in recent years. This study is a developmental research which is categorized as descriptive-survey in terms of data collection method. The aim of this study is to prioritize 22 districts of Tehran for the purpose of prevention from cardiovascular diseases. In the present study, after extraction of the effective factors on the prevalence of cardiovascular diseases from previous studies, the weight of each factor with their specific data for each 22 districts of Tehran (collected from relevant organizations) is obtained using two levels of Fuzzy Delphi method and one level of fuzzy best-worst method, for confirming or denying factors and weighting them based on the opinion of 25 cardiologists, respectively, and transferred to Arc GIS software for prioritizing 22 districts of Tehran.Using a combination of fuzzy best-worst method, which is one of the newest methods for making multi-criteria decision, and GIS, for weighting parameters and prioritizing 22 districts of Tehran, gives an acceptable worth to the present study.Our results-after classification, drawing, and combination of maps- indicated that the 8^th district (except a little part in the west) is the best district, and 16^th and 19^th districts (approximately whole district) are in the last priority for prevention of cardiovascular diseases. Other districts respectively placed in the second to 21^th places.

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Supply chain management is a set of used methods for the efficient integration of suppliers, manufacturers, warehouses, and sellers to response customer requirements to reduce system costs and to distribute products at the right place and right time. This study aims to identify and rank the supply chain damages using the analytic network process as a practical case in a fast moving consumer goods (FMCG-food industry) company. Firstly the supply chain damages are explored according to literature review. In the next step the most important damages are identified into four cluster of supply include
supply, production, distribution and support. Then, the weight of each identified damages based on its effects on other damages are calculated by using the analytic network process approach. According to results, the most important supply chain damages are logistics, distribution, competition and changing market tastes. The obtained results can provide practical discussion and solutions for similar companies to improve your market share and customer satisfaction.

 

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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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Regarding population growth and prompt development in developing countries, municipal solid waste management is always a great challenge for governments. Waste to energy conversion is an efficient approach with respect to overcoming not only the challenge of municipal solid waste management but also environmental challenges related to energy consumption like global warming and fossil fuel depletion. One of the substantial problems throughout the implementation of waste to energy approach is process selection. The selected process should be technically feasible and should have a high level of compliance with environmental standards. Owing to an inevitable significance of process selection, this paper focuses on defining the best process by relying on multi-criteria decision-making tools and network analytic process. Considering the effective parameters such as cost, efficiency in material diversity, productivity rate, energy consumption, pollutant emissions, toxic substances, and process time, the result indicates that the physico-chemical process is superior process for pretreatment of material.

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Today due to the globalization and competitive conditions of the market, decisions are generally made in group and in accordance with different attributes. In addition, all of the information is associated with uncertainty. In such situation, the emergence of inconsistency and facing with the contradictions will be obvious. Having regarded this fact, the development and application of tools that adequately address the uncertainty in decision making process and also be appropriate for group decision making is an important area of multi-criteria decision making (MCDM). Therefore, in this paper, firstly we developed the traditional best-worst method (BWM) and proposed an interval-valued intuitionistic fuzzy best-worst method (IVIFBWM), then introduced a novel approach for fuzzy multi-attribute group decision making based on the proposed method. Finally, in order to demonstrate how the introduced approach can be applied in practice, it is implemented in an Iranian investment company and the experimental results are examined. From the experimental results, we can extract that not only the introduced approach is simple in calculation but also it is convenient in implementation especially in interval-valued intuitionistic fuzzy environments.

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Due to the intensity of competition and economical condition in different countries, a group of manufacturers tried to add new products in their product portfolios in order to gain superiority against their competitors. However, the strategy and the manner of adding the products to the portfolio is one of the biggest challenges in the manufacturing process. As a result, researchers have used a variety of methods to evaluate the alternatives, such as ranking, mathematical optimization and multi criteria decision making. Hybrid methods using multi criteria decision making have gained popularity in recent years. This article uses a novel hybrid strategy using multi criteria decision making in order to find the best alternative. It is concluded that the ‘making’ alternative is superior to joint venturing and buying alternatives using the net outranking flow index.

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Preventive maintenance (PM) of machines has the critical role in a factory or enterprise. It decreases number of failures, increases reliability, as well as minimizes costs of production systems.  The managers’ duty of maintenance section is to prioritize machines and then, implement PM programs for them. Since machines have the different measures with respect to the maintenance costs, reliability, mean time between failures (MTBF), availability of spare parts, etc., the machines evaluation problem can be considered as a multiple criteria decision-making (MCDM) problem. Accordingly, the MCDM techniques can be applied to solve them. The aim of this paper is to extend the ELECTRE III (eLimination et choix traduisant la realite´– elimination and choice translation reality) method to interval type-2 fuzzy sets (IT2FSs) using curved (such as Gaussian) membership functions (MFs). The extended ELECTRE III methodology is then utilized to a maintenance group MCDM (GMCDM) matrix including the quantitative and qualitative criteria. In the proposed approach, the criteria weights, the assessment of alternatives with respect to criteria, and the thresholds are stated with Gaussian interval type-2 fuzzy sets (GIT2FSs). In order to show the effectiveness and applicability of the proposed approach, a case study and an illustrative example are exhibited using real decision-making problems. Due to the high correlation coefficients between our method and the others, as well as the results obtained by the proposed method, it can be taken into account as a valid and reliable approach to prioritize machines for PM.

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This study seeks to introduce the influential factors in controlling and dealing with uncertainty in intermittent demand. Hybrid forecasting and Grey Theory, due to their potential in facing complex nature, insufficient data, have been used simultaneously. Different modeling, unbiased weighting results have been used in estimating the safety stock(SS) by both theoretical and experimental methods. In other words, this work deals with the less studied feature of various modeling errors and their effect on SS determination and recommends its use to address the uncertainty of intermittent demand as a criterion for introducing a superior model in the field of inventory.

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  analysis of economic development of the Black Sea region. The article tests the authors' hypothesis about the adequacy of the indicators defined for evaluation through the proposed number of relative indicators, which in the complex will characterize the achievements of the region in ensuring the economic stability of the regional system, quality of transformation processes and indirectly the conditions created by public authorities for economic development. There is confirmed the dependence of the use of the proposed methodological approaches and the constructed comparative profile of the regions of the region, which can be useful for identifying the strengths and weaknesses of the region, outlining key issues and developing regional development plans and programs. It is determined that the largest vector length in the Mykolaivska region, which indicates that in the region on a number of economic indicators achieved higher results than in other regions of the Black Sea region and on average in other regions of Ukraine during the study period.
 

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