Journal of Industrial and Systems Engineering
Year:2019 | Volume:12 | Issue:2|Papers Count:15

This study determined effect of automobile seat backrest inclination on lattisimus dorsi muscle electromyographic (EMG) activity. Myoelectric activity was determined at two seat backrest angles (90 degrees and 100 degrees inclinations). Twenty-one (21) participants were engaged in the study. Electromygraphic activity at 90 degrees inclination had slope value of-0. 065 mV/minute while 100 degrees had slope value-0. 044 mV/minute. Myoelectric data indicated activity at 100 degrees was lesser than at 90 degrees for latissimus dorsi muscles. Results of the experiment were significant (P=0. 01). Myoelectric results indicated that activity of the latissimus dorsi muscles decreased with increase in backrest angle.

Since knowledge is currently considering as one of the most critical resources in organizations, knowledge management has an essential role in organizational success. Recently knowledge transfer has become a fast growing, innovative and essential research theme in the management domain. This paper proposes a comprehensive framework to have effective knowledge transfer in projects, especially transactional projects. We firstly explore, verify, and map out the key factors affecting knowledge transfer in organizations within the last decade. Secondly, a meta-synthesis approach is conducted by adopting “ Wash and Downe’ s” seven-step method to determine the relevant, vital factors. We identify thirty-nine effective factors classified into four categories named individual, organizational, technological, and transnational factors. The first three types of factors are effective for knowledge transfer in any projects; however, transnational factors are involved primarily in transnational projects. A breakdown structure of these factors is presented as a coherent framework. Lastly, the paper concludes with a discussion of emerging issues, new research directions, and the practical implications of knowledge transfer research.

This paper proposes a unique mixed integer programming model to solve non-identical parallel machines (NIPM) with sequence-dependent set-up times and human resiliency engineering. The presented mathematical model is formulated to consider human factors including Learning, Teamwork and Awareness. Moreover, processing time of jobs are assumed to be non-deterministic and dependent to their start time which leads to more precision and reality. The applicability of the proposed approach is demonstrated in a real world car accessories industrial unit. A hybrid metaheuristic method based on Genetic algorithm and simulated annealing is proposed to solve the problem. Parameter tuning is applied for adjustment of metaheuristic algorithm parameters. The superiority of the proposed hybrid metaheuristic method is evaluated by comparing the obtained results to GAMS, and two other hybrid metaheuristics. Moreover, it is shown that the hybrid approach provides better solutions than other hybrid approaches under uncertainty. This is the first study that presents a new hybrid approach for optimization of the stated problem by considering human resiliency.

Risk ranking of Horizontal Directional Drilling (HDD) for gas and oil wells is a key criterion in the project feasibility, pricing and for introducing a risk management strategy that aims to reduce the number of failures in the installation phase and its negative consequences. HDD is currently widely used in drilling wells in Iran, but research in the area of identification and risks ranking of these projects have not been done so far. Therefore, this research will identify and ranks the HDD risks in the field of Gachsaran as the case study, by helping literature review and drilling experts and using statistical techniques and Multi-Criteria Decision Making (MCDM) methods. The method of the network analysis process is a powerful tool in deciding uncertain topics for ranking the risks. The offered approach allows decision-makers to involve in the ranking process and use linguistic assessment in the ranking of HDD risks.

E-banking has grown dramatically with the development of ICT industry and banks offer their services to customers from different channels. Nowadays, considering the great economic benefits of electronic banking systems, the need to pay attention to the expansion of electronic banking is increasingly felt in terms of reducing costs and increasing the bank's profitability. The purpose of this study is to identify the factors that encourage customers to accept e-banking across the country using the statistics and information retrieved from the Central Bank and the data mining techniques. For this purpose, initially, the K-Means clustering algorithm was appliyed and the provinces of Iran is separated into 3 clusters. In addition, the transactions related to each year were clustered separately, and the formed clusters were compared with each other. In the next step the hidden patterns of the E-payment instrument transactions were detected using the CART algorithm. According to the results obtained from decision tree rules, indices of social-economic and Information and Communication Technology development and business boom were the most effective factors in increasing the usage of electronic payment methods.

Increasing the use of fossil fuels is with severe environmental and economic problems, bringing more attention to alternative fuels. The compressed natural gas (CNG), as an alternative fuel, offers many more benefits than gasoline or diesel fuel such as cost-effectiveness, lower pollution, better performance, and lower maintenance costs. Gas stations location and the number of gas stations are the pivotal factors, influencing the less using of CNG in comparison with other fuels. In this regard, this paper unveils a two-step phase method to locate the CNG stations in the two-way highways. In the first phase, an optimized Data Envelopment Analysis (DEA) model is deployed to determine the best candidate location for gas fuel stations. Concerning the selected candidate locations, the second stage devises a multi-objective flow refueling location model with the aim of maximizing the traffic flow of the vehicles in the twoway highways and reducing the cost of constructing fuel stations. Notably, fuel tanks capacity is considered to be hemmed in by uncertainty. The introduced method is evaluated and verified via investigating a three-part of Persian Gulf Highway. The results corroborate the effectiveness and usefulness of the model and can help researchers to set up their refueling location problems efficiently.

Reduction of complex product systems (CoPS) manufacturing costs are the main factors of sustainability and survival of the manufacturers. Choosing proper CoPS suppliers can dramatically reduce these costs and increases competitive capability for manufacturers. This is due to the fact that in the complex industries, the costs of raw materials for the production processes or the purchase of components includes a substantial part of the product costs. In this regard, in this paper, a tailored data envelopment analysis (DEA) model is deployed to assess and select the supplier of CoPS, helping to deduct these costs as well as eventuate in productivity of the products. In the proposed model, various suppliers of CoPS are evaluated based on a set of economic, technical, and geographic criteria. The suppliers are ranked in accordance with the obtained scores and then the best ones are chosen. Eventually, to examine the applicability and usefulness of the proposed method, a case study is conducted via which important managerial outcomes are extracted.

This paper discusses making decisions in the glass container industry. The production of glass containers for the packaging of food and beverages is one of the most important parts of glass industries. In this research, the decision is made on the production plan for the glass container industries from the perspective of various executive stakeholders. In this regard, two models are initially presented: 1) the first model with a production approach, i. e. considering the objectives and constraints of the production stakeholders, and 2) the second model with a sales approach, taking into account the objectives and constraints of the sales stakeholders. Also, in the sales approach, by defining a new index, the importance of meeting customers’ demands is considered separately and according to different criteria. TOPSIS technique as one of the multi-attribute decision making methods is employed to calculate the noted parameter. Then, a multi-objective integrated model with a managerial approach for decision making on the production planning in the glass container industry is proposed, in which it is attempted to consider the viewpoints of various stakeholders. Finally, the proposed approach is implemented in one of the largest companies producing glass containers in Iran. In this regard, compromise programming is used to solve the final model. It is one of the multi-objective optimization methods which are classified under non-preferred methods. The obtained results show the efficiency of the proposed integrated approach for the studied company. It is also worth noting that the obtained results are presented for the management of the studied company and the results are found to be useful.

In this paper, we consider a three-stage network comprised of a leader and two followers in respect to the additional desirable and undesirable inputs and outputs. We utilize the non-cooperative approach multiplicative model to measure the efficiency of the overall system and the performances of decisionmaking units (DMUs) from both, the optimistic and pessimistic views. Moreover, we utilize the concept of a goal programming and define a kind of cooperation between the leader and followers, so that the objectives of the managers are capable of being inserted in the models. In actual fact, a kind of collaboration is considered in a non-cooperative game. The non-cooperative models from this view cannot be converted into linear models. Therefore, a heuristic method is proposed to convert the nonlinear models into linear models. After obtaining the efficiencies based on the double-frontier view, the DMUs are ranked and classified into three clusters by the k-means algorithm. Finally, this paper considers a genuine world example, in relevance to production planning and inventory control, for model application and analyzes it from the double-frontier view. The proposed models are simulations of a factory in a real world, with a production area as leader and a warehouse and a delivery point as two followers. This factory has been regarded as a dynamic network with a time period of 24 intervals.

Component reliability is usually estimated based on economical sampling plan and historical data analysis. In such process, two types of errors may occurs. According to a conventional view, the type 1 and 2 errors respectively referred to lower and higher component reliability estimation are arisen. Generally, it is commonly thought that the first type error leads to an under-estimation of the whole system's reliability, and the second type over-estimates it, which in turn, causes false amplification or ignores the need to boost the system by using redundant components. This article is devoted to the role of component reliability estimation error in the design of a multi-state system (MSS). To this aim and from the literature survey, two optimal designed MSS evaluated by a proposed validated computer simulation model under assumption of positive and negative errors. Result revealed that any type of uncertain estimation increases with the overdesigning risk and applying more number of components in the optimum system designing, but fortunately no weakness in its functionality. The greater the error, the more redundant components in MSS design.

Neutrosophic set is considered as a generalized of crisp set, fuzzy set, and intuitionistic fuzzy set for representing the uncertainty, inconsistency, and incomplete knowledge about a real world problem. This paper aims to develop two-person zero-sum matrix games in a single valued neutrosophic environment. A method for solving the game problem with indeterminate and inconsistent information is proposed. Finally, two examples are given to illustrate the practically and the efficiency of the method.

Given the increasing use of gas energy and the dependence of large segments of industries and domestic, commercial, and administrative customers on gas energy, the need for sustained monitoring and the avoidance of any interruptions in the provision of gas services is essential and inevitable. One of the vital parts of the gas industry is gas pressure reduction stations, namely CGSs. In this research, a new fuzzy expert system is designed to troubleshoot and control safety and shutoff valves, which are regarded as main elements in controls of the safety of stations. In the presented expert system, the knowledge about the control of the safety and shutoff valves has been obtained from experts and has been entered in a knowledge-base as "if. . . then. . . else", and CLIPS language has been used for the system implementation. In this system, 164 rules have been utilized. The expert system is designed to be able to make deductions in both certain and uncertain conditions. Decision trees and control flowcharts have been applied in certain conditions. Fuzzy logic and certainty factors have been employed to implement uncertainty conditions in a case study. Concerning the importance of appropriate control of the safety and shutoff valves, increased responsiveness, increased reliability, increased availability, reduced accidents, reduced costs, reduced natural gas loss, and improved safety of the CGSs are expected by the implementation of the fuzzy expert system.

Emergency blood distribution seeks to employ different means in order to optimize the amount of blood transported while timely provision. This paper addresses the concept of blood distribution management in disastrous conditions and develops a fuzzy scenario-based bi-objective model whereas blood compatibility concept is incorporated in the model, and the aim is to minimize the level of unsatisfied demand of Affected Areas (AAs) while minimizing the cost of the supply chain. The blood supply chain network under investigation consists of blood suppliers (hospitals or blood centers), Blood Distribution Centers (BDCs), and AAs. Demand and capacity, as well as cost, are the sources of uncertainty and in accordance with the nature of the problem, the fuzzy-stochastic programming method is applied to deal with these uncertainties. After removing nonlinear terms, Ɛ-constraint solves the bi-objective model as a single objective one. Finally, we apply a case from Iran to show the applicability of the model, results prove the role of blood distribution management in decreasing the unsatisfied demand about 38%.

Depictions of the service process, usually is done through the service cycle assessment. This cycle includes a process of mapping that begins by the service of the first customer by the related service providers, until the completion of transaction processes. Even after the transaction is resolved, there is still a service that can be categorized as after sales service. Healthcare, is among the companies engaged in public pelayananan sector, particularly in public health services. To fulfill the needs of the customer / patient, company must always work to provide the health services in accordance with customer expectations. Measuring the level that patients are satisfied can be done by using several indicators such as patient satisfaction on health services access, quality health services, process health services and health service system. It could be argued that the measurement of quality of health services, and all the results or consequences of health services contained in the output has the potential to be a problem of quality in health service. The method used is by applying the concept of lean thinking, to reduce the 7 (seven) wastes that are considered to be very harmful. The achievement of the improvement process in the handling of services, is expected to accelerate and reduce wastes in the process of health service. Another technique used to identify and help search for potential problems is the use of FMEA (Failure Mode and Effect Analysis).

This study proposes a real-time framework for performance optimization of proactive safety culture in the oil and gas industry. Safety culture indicators were extracted from the literature using a comprehensive literature review. The proposed framework is based on fuzzy data envelopment analysis (FDEA), artificial neural networks (ANN), and statistical methods. It is able to evaluate the real-time performance of any safety-critical plant in the oil and gas industry and determines the current status of each indicator. The required data were collected using a questionnaire which was distributed as a self-administered survey to 210 employees in Shiraz Petrochemical Company and 174 surveys were returned with a high response rate. The application of fuzzy logic along with stochastic efficiency frontier analysis has empowered the proposed hybrid framework to deal with deep uncertainty, and result in more reliable findings. The obtained results can help safety managers to improve the proactive safety culture of the organization. They also can use the presented framework for periodic safety evaluations and determine the effectiveness of the implemented correction plans. To the best of our knowledge, this is the first study that presents a real-time framework for performance optimization of safety culture under deep uncertainty in the oil and gas industry.