فهرست مطالب

Applied Research on Industrial Engineering - Volume:6 Issue: 4, 2019
  • Volume:6 Issue: 4, 2019
  • تاریخ انتشار: 1398/09/10
  • تعداد عناوین: 7
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  • Aniekan Ikpe *, Ekom Etuk, Azum Adoh Pages 268-282
    Wind turbine incorporates a gear box which aids the transmission of torque for the generation of wind energy, industry professionals have streamlined the gearbox design to suite this purpose. Despite the advancement in the gear box design, most wind turbine downtime is attributed to gearbox-related problems. In this study, Finite Element Method through ANSYS R15.0 software was employed in modelling and analysis of a 2-stage planetary gear train for modular horizontal wind turbine. The ring gear was considered as statically constrained member because it is practically fixed to the gearbox housing while the dynamics of the planet gear, planet carrier and the sun pinion were considered as rotating members. Using Factor of Safety (FOS) ranging from 10-15, the gear model was simulated to determine the equivalent stresses, strains and total deformation. The simulation which was conducted for five (5) steps at 2.5 seconds each yielded minimum and maximum Von-mises stress of 10.168 Pa and 5.9889e+009 Pa for the 5th step, minimum and maximum equivalent elastic strain of 5.0839e-011 and 2.9944e-002 for the 5th step and maximum total deformation of 1.7318e-003 m at the 5th step. The findings revealed that the higher the design FOS, the lower the stress-strain deformations, indicating longevity and optimum performance of the gear system. It was observed that increase in contact forces between the meshing gear teeth may cause larger elastic deformations, increasing tooth bending deformation as well as larger backlash on the gear teeth while continuously varying gear mesh stiffness with time can result in excessive vibration and noise.
    Keywords: modelling, Planetary gear, Wind Turbine, Stress, Strain, deformation, FOS
  • Seyed Hamzeh Mirzaei * Pages 283-293
    In aggregation of preferences system, each decision maker (DM) selects a subset of the alternative and places them in a ranked order. The key issue of the aggregation preference is how to determine the weights associated with different ranking places. To avoid the subjectivity in determining the weights, data envelopment analysis (DEA) is used in Cook and Kress to determine the most favorable weights for each alternative. With respect to DEA-based models, two main criticisms appear in the literature: multiple top-ties and overly diverse weights. DEA models use assignments of the same aggregate value (equal to unity) to evaluate multiple alternatives as efficient. There is no criterion to discriminate among these alternatives in order to construct a ranking of alternatives. furthermore, overly diverse weights can appear, given that each alternative can have its own vector of weights (i.e., the one that maximizes its aggregate value). Thus, the efficiencies of different alternatives obtained by different sets of weights may be unable to be compared and ranked on the same basis In order to solve these two problems above, In order to rank all the alternatives on the same scale, In this paper we proposed an improvement to Kornbluth’s approach by introducing an multiple objective linear programming (MOLP) approach for generating a common set of weights in the DEA framework. In order to solve the MOLP model we use a goal programming (GP) model. solving the GP model gives us a common set of weights and then the efficiency scores of candidate can be obtained by using these common weights and finally we can rank all alternative.
    Keywords: aggregation of preferences, Data Envelopment Analysis, Goal programing, Common set of weights, Ranking
  • SUPRIYATI . *, Humiras Purba Pages 294-314
    The development of motorcycle industry in Indonesia is quite rapid. The mode of transportation is a favorite the people of Indonesia, especially in industrial area. The average motorcycle user is a company employee because it facilitates access and avoids traffic. Motorcycle component production in Indonesia is spread across several companies, one of the companies that manufactures components made of plastic material has 16 injection machines. These machines have different performance, when analyzed using the OEE approach it is known that Machine 16 has the lowest performance compared to others at only 91.2%. Factors that affect the low efficiency of the machine due to the 7 biggest losses namely Dandori, Mold Repair, Machine Damage, re-setting, Material jams, robot damage and Cleaning Mold
    Keywords: OEE, Six Big Losses, TPM, maintenance, Equipment
  • Soheila Badrloo *, Ali Husseinzadeh Kashan Pages 314-336

    A lot of real-world problems such as the assignment of special rooms in hospitals, operating room layout, image processing, etc., could be formulated in terms of Quadratic assignment problem. Different exact methods are suggested to solve these problems, but because of the special structure of these problems, by increasing the size of the problem, finding an exact solution become more complicated and even impossible. So, employing meta-heuristic algorithms is inevitable, due to this problem we use optics inspired optimization (OIO) in this paper. The obtained results and its comparison with the solutions of the central library of Quadratic assignment problem (QAPLIB) show that the proposed algorithm can exactly solve small-sized problems with 100% efficiency while the efficiency of medium-to-large size instances is 96%. Accordingly, one can conclude that the proposed OIO has generally high efficiency for solving permutation-based problems.

    Keywords: Quadratic assignment problem, Optics inspired optimization, NP-complete, Metaheuristics
  • Reza Darvishinia, Hossein Ebrahimzadeh Shermeh *, Samira Barzkar Pages 333-363

    The present study aims at suggesting a model for intelligent investment, through enabling us to be Autoregressive Integrated Moving Average of ARIMA and seasonal coefficient. In this study, the researcher uses seasonal fluctuation Model. The previous trend of time series, related to the companies for a period of 11 years, from 2006 to 2017, was carried out based on seasonal data. Then the researcher predicted the final price based on moving average method. In the next stage, the proportion of real final price and predicted the final price is calculated regarding each period. Then, the seasonal coefficient average is calculated for similar seasons. In the final stage, the value of a prediction, for a given period, is calculated when moving average method is multiplied by a seasonal coefficient average. As a result, seasonal coefficient of a given stock is derived.

    Keywords: exchange, investment on exchange, seasonal coefficient, ARIMA time series
  • Seyed Ahmad Edalatpanah * Pages 367-373

    Traditional linearl programming usually handles optimization problems involving deterministic objective functions and/or constrained functions. However, uncertainty also exists in real problems. Hence, many researchers have proposed uncertain optimization methods, such as approaches using fuzzy and stochastic logics, interval numbers, or uncertain variables. However, In practical situations, we often have to handle programming problems involving indeterminate information. The aim of this paper is to put forward two new algorithms, for solving the Single-Valued Neutrosophic linear Problem. A numerical experiments are reported to verify the effectiveness of the new algorithms.

    Keywords: Single valued neutrosophic number, Neutrosophic linear programming problem, Linear programming problem
  • Abdella Ali *, Jemal Hassen, Gebrekidan Wendim Pages 374-380

    This paper uses forecasting model to prevent over production of uneaten food in student’s cafeteria in Woldia University (Ethiopia). Students arrival in the university is highly variable. And it is difficult for the canteen management to estimate the number of students attend the meal during first two weeks of operation. The moving average and exponential smoothing forecasting methods were used to forecast the student’s arrival for the year 2019. Mean absolute deviation (MAD) was used as a measure of forecasting accuracy. Finally, it is found that moving average were more accurate forecasting method than exponential smoothing for forecasting student’s arrival in Woldia University.

    Keywords: moving average, exponential smoothing, student’s arrival, Students cafeteria, Food waste