International Journal of Industrial Engineering & Production Research

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Abstract: The shipments of hazardous materials (HAZMATs) induce various risks to the road network. Today, one of the major considerations of transportation system managers is HAZMATs shipments, due to the increasing demand of these goods (because it is more used in industry, agriculture, medicine, etc.), and the rising number of incidents that are associated to hazardous materials. This paper presents a tool for HAZMATs transportation authorities and planners that would reduce the risk of the road network by identifying safe and economic routes for HM transshipment. Using the proposed linear integer programming model, the HM management system could determine an optimal assignment for all origin–destination pairs for various hazardous materials in a transportation network and so reduce the vulnerability due to HAZMATs releases such as population and environmental vulnerability. The model is implemented and evaluated for the hazardous materials routing within Fars, Yazd, Isfahan, and Chaharmaha-o-Bakhtiyari provinces of Iran. The branch-and-bound algorithm is applied to solve the model using the Lingo software package.

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  Selecting an effective project plan is a significant area in the project management. The present paper introduces a technique to identify the project plan efficient frontier for assessing the alternative project plans and selecting the best plan. The efficient frontier includes two criteria: the project cost and the project time. Besides, the paper presents a scheme to incorporate Directed Acyclic Graph (DAG) into the project risk analysis.

This scheme is used to estimate the expected impacts of the occurrence of the project risks on the project cost and the project time. Also, a theoretical model is defined to provide integration between project risk analysis and overall project planning using the breakdown structures. We believe that applying the proposed technique helps the company’s managers in most effective manner dealing with his complicated project plan assessment and selection problems. The application of the technique was implemented in the companies in construction industry in which represented a considerable cost and time improvements.

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  In order to have better insight of project characteristics, different kinds of fuzzy analysis for project networks have been recently proposed, most of which consider activities duration as the main and only source of imprecision and vagueness, but as it is usually experienced in real projects, the structure of the network is also subject to changes. In this paper we consider three types of imprecision namely activity duration, activity existence and precedence relation existence which make our general fuzzy project network. Subsequently, a corrected forward recursion is proposed for analysis of this network. Since the convexity and normalization of traditional fuzzy numbers are not satisfied, some corrected algebraic operations are also presented. Employing the proposed method for a real project reveals that our method results in more applicable and realistic times for activities and project makespan in comparison to

Classic fuzzy PERT.

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Fuzzy Logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. However, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the accident and the undesired results of it.

In this paper, fuzzy approach is used for risk evaluating and forecasting, in accidents caused by working with vehicles such as lift truck. Basically, by using fuzzy rules in forecasting various accident scenarios, considering all input variables of research problem, the uncertainty space in the research subject is reduced to the possible minimum state and a better capability of accident forecasting is created in comparison to the classic two-valued situations. This new approach helps the senior managers make decisions in risk and accident management with stronger scientific support and more reliably.

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Design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. This paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. First, an efficient complex mixed-integer linear programming (MILP) model by considering some real-world assumptions is developed for the integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then, the stochastic counterpart of the proposed MILP model is used to measure the conditional value at risk (CVaR) criterion, as a risk measure, that can control the risk level of the proposed model. The computational results show the power of the proposed stochastic model with CVaR criteria in handling data uncertainty and controlling risk levels.

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Abstract In the dynamic and competitive market, managers seek to find effective strategies for new products development. Since There has not been a thorough research in this field, this study is based on a review on the risks exist in the NPD process and an analysis of risks through FMEA approach to prioritize the existent risks and a modeling behavior of the NPD process and main risks using system dynamics. First, we present new product development concepts and definition. We then based our study on a literature review on the NPD risks and then provide an FMEA approach to define risks priority. Using the obtained main risks, we model the NPD process risks applying system dynamics to analyze the system and the risks effect on. A safety clothing manufacturer is considered as a case study.

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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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Allocation of construction risks between clients and their contractors has a significant impact on the total construction costs. This paper presents a system dynamics (SD)-based approach for quantitative risk allocation. Using the proposed SD based approach, all the factors affecting the risk allocation process are modeled. The contractor’s defensive strategies against the one-sided risk allocation are simulated using governing feedback loops. The full-impact of different risk allocation strategies may efficiently be modeled, simulated and quantified in terms of time and cost by the proposed object-oriented simulation methodology. The project cost is simulated at different percentages of risk allocation and the optimum percentage of risk allocation is determined as a point in which the project cost is minimized. To evaluate the performance of the proposed method, it has been implemented in a pipe-line project. The optimal risk allocation strategy is determined for the inflation risk as one of the most important identified risks.

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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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Abstract Project management in construction industry, in many cases, is imperfect with respect to the integration of Occupational Health and Safety (OHS) risks. This imperfection exhibits itself as complications affecting the riskiness of industrial procedures and is illustrated usually by poor awareness of OHS within project teams. Difficulties on OHS regularly came about in the construction industry. The integration of OHS risk is not systematic in construction areas in spite of progressing laws and management systems. As project safety and risk evaluation in construction industry is an important issue, thus, the way on doing evaluation and liability of estimation is necessary. In this paper, we propose a new systematic approach based on Latin Hypercube Sampling (LHS) for integrating occupational health and safety into project risk evaluation. This approach tries to identify and evaluate reinforcement effects in a systematic approach for integrating OHS risks into project risk assessment. Furthermore, the proposed method allows evaluating and comparing OHS risks before and after the mitigation plan. A case study is used to prove the workability, credibility of the risk evaluation approach and uncomplicated integration of OHS risks at a construction project. This approach enables continual revaluation of criteria over the direction of the project or when new information is obtained. This model enables the decision makers such as project managers to integrate OHS risks toward schedule plan and compare them before and after the mitigation plan. The mentioned model is found to be useful for predicting OHS risks in construction industries and thus avoiding accidents over the path of the project.

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Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theory is basic tool for multivariate modeling, which is defined by using marginal and dependencies between variables joint distribution function. In addition, Copulas are able to explain and describe of complex multiple dependencies structures such as non-linear dependence. Therefore, in this study, by combining symmetric and asymmetric GARCH model for modeling the marginal distribution of variables and Copula functions for modeling financial data and also use of DCC model to determine the dynamic correlation structure between assets, try to estimate the Value at Risk of investment portfolio consists of five active index In Tehran Stock Exchange. The results demonstrate excellence of GJR-GARCH(1,1) with the distribution of t-student for marginal distribution. t-Copula model, estimates the Value at Risk model less than the Gaussian Copula in all cases.

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Today, many companies after achieving improvements in manufacturing operations are focused on the improvement of distribution systems and have long been a strong tendency to optimize the distribution network in order to reduce logistics costs that the debate has become challenging. Improve the flow of materials, an activity considered essential to increase customer satisfaction. In this study, we benefit cross docking method for effective control of cargo flow to reduce inventory and improve customer satisfaction. Also every supply chain is faced with risks that threaten its ability to work effectively. Many of these risks are not in control but can cause great disruption and costs for the supply chain process. In this study we are looking for a model to collect and deliver the demands for the limited capacity vehicle in terms of disruption risk finally presented a compromised planning process. In fact, we propose a framework which can consider all the problems on the crisis situation for decision-making in these conditions, by preparing a mathematical model and software gams for the following situation in a case study. In the first step, the results presented in mode of a two-level planning then the problem expressed in form of a multi-objective optimization model and the results was explained.

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This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.

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Innovation & Prosperity Fund (IPfund) in Iran as a governmental organization aims to develop new technology-based firms (NTBF) by its available resources through financing these firms. The innovative projects which refer to IPfund for financing are in a stage which can receive both fixed rate facilities and partnership in the projects, i.e. profit loss sharing (PLS). Since this fund must protect its initial and real value of its capital against inflation rate, therefore, this study aims to examine the suitable financing methods with considering risk. For this purpose we study on risk assessment models to see how to use risk adjusted net present value for knowledge based projects. On this basis, the NPV of a project has been analyzed by taking into account the risk variables (sales revenue and the cost of fixed investment) and using Monte Carlo simulation. The results indicate that in most cases for a project, the risk adjusted NPV in partnership scenario is more than the other scenario. In addition to, partnership in projects which demand for industrial production facilities is preferable for the IPfund than projects calling for working capital.

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Nowadays, the CNC machining industry uses FMEA approach to improve performance, reduce component failure, and downtime of the machines. FMEA method is one of the most useful approach for the maintenance scheduling and consequently improvement of the reliability. This paper presents an approach to prioritize and assessment the failures of electrical and control components of CNC lathe machine. In this method, the electrical and control components were analyzed independently for every failure mode according to RPN. The results showed that the conventional method by means of a weighted average, generates different RPN values ​​for the subsystems subjected to the study. The best result for Fuzzy FMEA obtained for the 10-scale and centroid defuzzification method. The Fuzzy FMEA sensitivity analysis showed that the subsystem risk level is dependent on O, S, and D indices, respectively. The result of the risk clustering showed that the failure modes can be clustered into three risk groups and a similar maintenance policy can be adopted for all failure modes placed in a cluster. Also, The prioritization of risks could also help the maintenance team to choose corrective actions consciously. In conclusion, the Fuzzy FMEA method was found to be suitably adopted in the CNC machining industry. Finally, this method helped to increase the level of confidence on CNC lathe machine.

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The article examines foreign and domestic experience in organizing financial monitoring systems, systematizes the requirements for its implementation in Ukraine. The basic legal norms, enshrined in the joint directives of the European Parliament and of the Council of the EU, and underlying the national financial monitoring systems of the EU countries and Ukraine have been also analyzed. Taking into account the fact that the risk-based approach is the main basis for the effective implementation of all FATF recommendations, the nature of the risk of money laundering / financing of terrorism and the criteria for their assessment have been investigated. A scheme of improving the process of financial monitoring in a bank has been developed, as well as a number of measures have been proposed to raise the level of adhering to the legislation in the field of anti-money laundering and counter-terrorist financing by the banking sector.

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The aim of the paper is to present a complex risk analysis of investing in agriculture Exchange Trade Funds (ETFs). The specific characteristics of agricultural investments should be taken into account as from the direct financial investments into agricultural ETFs, as for the general portfolio approach applying. To achieve the objectives of the work, the authors structured agriculture ETFs into 6 classes, which represent different types of ETFs. A special sample of 26 agricultural ETFs was formed. A complex risk analysis consisted of applying 5 different conceptual approaches to measuring investment risk. In particular, approaches based on measuring variability, applying the concept of Value-at-Risk are applied. The approach of estimating the shocks of changes in the profitability of the asset class in question is applied. The risk level in the aspect of sensitivity to changes in stock returns, bonds and the uncertainty index EPU is investigated. Built portfolios with minimal risk. Obtained results can be applied for investment decisions

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Sustainability is now increasingly recognized as an effective strategy to deal with the current challenges of global supply chains. Supply chains of the lead and zinc industries are most important. Because these two industries not only are among the high-risk in different countries, including Iran, but also can affect economic, social, and environmental sustainability. On the other hand, identifying and assessing the critical risks of supply chains have been less addressed in recent studies. This study aimed to identify and assess critical risks of sustainable supply chains (SSCs) in the Iranian lead and zinc industry. This study was a mixed-method (qualitative and quantitative) descriptive survey. Based on the literature, 24 risk factors that affect supply chain sustainability were identified, out of which 20 critical risk factors were confirmed in two steps by reviewing experts’ comments and the data obtained from in-depth interviews and questionnaires. The validity of questionnaires is verified based on the opinions of a group of 5 experts in the first step and another group of 17 experts and professionals of the lead and zinc industry in the second. The Cronbach’s alpha coefficient of the questionnaires was calculated to be 0.837, indicating the reliability of the questionnaires. The risk factors were analyzed using the Risk Priority Number (RPN), fuzzy DEMATEL, and risk matrices. Based on the results, “lack of technological/knowledge sustainability”, “price and cost fluctuations”, “inflation and exchange rates” and “environmental pollution” were the most important risk factors in the supply chain of the Iranian lead and zinc industry.

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Today, one of the most important concerns of production units is the evaluation, analysis and risk management in the production process. In this research, based on the fuzzy control approach, a scientific and logical method for evaluating, analyzing and managing risk in the production process is presented. Based on the proposed method of this research, after identifying the risks in the production process of products, according to the three criteria of failure severity, probability of failure and detectability, as well as using the best - worst method, evaluation and determining the importance of these risks, is done. Then, with the fuzzy rules, fuzzy inference system is designed. The final result is the classification and prioritization of identified risks. Finally, the proposed research model for an applied sample is used and its final results are analyzed.

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The study investigates the impact of work environment on organizational performance in Ethiopiachr('39')s Arjo Dedessa Sugar Factory (ADSF) and Finchaa Sugar Factory (FSF), as well as the relationship between physical work environment, work-related risk and injuries, psychological work environment, and social work environment. The total number of people employed in two industries is 867 and 2824. To examine the work-related environment condition, a stratified random sampling technique was used to select a sample of 266 and 338 employees. A 60% response rate has been achieved. Statistical software SPSS V 23.0 was used to analyze and determine the relationship between dependent and independent variables using Pearson correlation and linear regression analyses. The findings show that employees in ADSF have a moderate social work environment compared to those in FSF, but both organizationschr('39') physical work environments are the least conducive. Both ADSF and FSFchr('39')s physical work environments had statistically significant effects on their performance. The suggestion was made to improve the social environment in order to improve the employeeschr('39') psychological health.