2019
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1

Modelling and analysis of 2stage planetary gear train for modular horizontal wind turbine application
http://www.journalaprie.com/article_100626.html
10.22105/jarie.2020.213154.1114
1
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 gearboxrelated problems. In this study, Finite Element Method through ANSYS R15.0 software was employed in modelling and analysis of a 2stage 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 1015, 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 Vonmises stress of 10.168 Pa and 5.9889e+009 Pa for the 5th step, minimum and maximum equivalent elastic strain of 5.0839e011 and 2.9944e002 for the 5th step and maximum total deformation of 1.7318e003 m at the 5th step. The findings revealed that the higher the design FOS, the lower the stressstrain 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.
0

268
282


Aniekan
Ikpe
Department of Mechanical Engineering, University of Benin, Benin City, Nigeria.
Nigeria
aniekan.ikpe@eng.uniben.edu


Ekom
Etuk
Department of Production Engineering, University of Benin, Benin City, Nigeria.
Nigeria
alwaysetuk@gmail.com


Azum
Adoh
National Board of Technology Incubation, Warrior, Delta State, Nigeria.
Nigeria
adoh.azum@gmail.com
modelling
Planetary gear
Wind Turbine
Stress
Strain
deformation
FOS
[[1] Filliz, H. i., Olguner, S. andEvyapan, E. (2017) A Study on Optimization of Planetary Gear Trains. 3rd International Conference on Computational and Experimental Science and Engineering, 132(3), 727733.##[2] Ragheb, A. and Ragheb, M. (2010) Wind Turbine Gear Box Technologies. Proceedings of the 1st International Nuclear and Renewable Energy Conference, Amman, Jordan, March, 2124, 2010.##[3] Cooley, C. G. and Parker, R. G. (2014) A Review of Planetary and Epicyclic Gear Dynamics and Vibrations Research. Transactions of the ASME, Applied Mechanics Reviews, 66, (040804), 115.##[4] Hohn, B., Stahl, K. and Gwinner, P. (2013) Lightweight Design for Planetary Gear Transmissions. Gear Technology, 96103.##[5] Hamand, Y. C. and Kalamkar, V. (2011) Analysis of Stresses and Deflection of Sun Gear by Theoretical and ANSYS Method. Modern Mechanical Engineering, 1, 5668.##[6] Pawar, P. V. and Kulkarni, P. R. (2015) Design of Two Stage Planetary Gear Train for High Reduction Ratio. International Journal of Research in Engineering and Technology, 4(6), 150157.##[7] Manwell, J. F., Mc Gowan, J. G. and Rogers, A. L. (2002) Wind Energy Explained: Theory, Design and Application. Chichester, NY: Wiley.##[8] Norton, R. L. (2006) Machine Design: An Integrated Approach, 3rd edition. Upper Saddle River, NJ: Pearson Prentice Hall.##[9] Schulze, T., HartmannGerlach, C. and Schlecht, B. (2010) Calculation of Load Distribution in Planetary Gears for an Effective Gear Design Process. American Gear Manufacturers Association, Alexandria, Virginia.##[10] Uematsu, S. (2002) Effect of Variation of Angular Velocity in Gear Rolling Process on Profile Error. Precision Engineering, 26(4), 425429.##[11] Do, T. P., Ziegler, P. and Eberhard, P. (2014) Simulation of Contact Forces and Contact Characteristics during Meshing of Elastic Beveloid Gears. Computer Assisted Methods in Engineering and Science, 21, 91111.##[12] Arnett, E. B., Schirmacher, M., Huso, M. M., Hayes, J. P. (2010) Effectiveness of Changing Wind Cutin speed to reduce Bat Fatalities at Wind Facilities. Bats and Wind Energy Coperative, Bats Conservation International, Texas, USA.##[13] Ikpe, A. E., Owunna, I. and Ebunilo, P. O. (2016) Determining the Accuracy of Finite Element Analysis when Compared to Experimental Approach for Measuring Stress and Strain on a Connecting Rod Subjected to Variable Loads. Journal of Robotics, Computer Vision and Graphics, 1(1), 1220.##[14] Ikpe A. E. and Owunna I. B. (2019) Design of Remotely Controlled Hydraulic Bottle Jack for Automobile Applications. International Journal of Engineering Research and Development, 11(1), 124134.##[15] Ikpe, A E., Orhorhoro, E. K. and Gobir, A. (2017) Design and Reinforcement of a BPillar for Occupants Safety in Conventional Vehicle Applications. International Journal of Mathematical, Engineering and Management Sciences, 2(1), 3752.##[16] Ikpe, A. E. and Owunna I. B. (2017) Design of Vehicle Compression Springs for Optimum Performance in their Service Condition. International Journal of Engineering Research in Africa, 33, 2234.##[17] Stefanescu, T. M., Volintiru, O. N. and Scurtu, I. C. (2018) Considerations Regarding the Lubrication of Marine Gearboxes. IOP Conference Series, Journal of Physics, 1122, 012025.##]
1

Ranking aggregation of preferences with common set of weights using goal programming method
http://www.journalaprie.com/article_100627.html
10.22105/jarie.2020.210568.1113
1
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 DEAbased models, two main criticisms appear in the literature: multiple topties 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.
0

283
293


Seyed Hamzeh
Mirzaei
Department of Mathematics, Arak Branch, Islamic Azad University, Arak, Iran.
Iran
sh_mirzaei82@yahoo.com
aggregation of preferences
Data Envelopment Analysis
Goal Programming
Common set of weights
Ranking
[Andersen. P., Petersen N.C. (1993). A procedure for ranking efficient units in data envelopment analysis, Management Science 39, 1261–1264.##[2] Charnes, A., Cooper, W.W. (1952) Chance constraints and normal deviates, J. Am. Stat. Assoc. 57 (1952) 134–148.##[3] Charnes A., Cooper W.W. (1961), Management Models and Industrial Applications of Linear Programming; John Wiley, New York.##[4] Charnes, A., Cooper W.W., E. Rhodes. (1978). Measuring the efficiency of decision making units, European Journal of Operational Research 2, (4) 429–444.## [5] Cook, W.D. and Krees, M., (1990). A data envelopment model for aggregating preference rankings. Management Science 36, 11, pp. 13021310.##[6] Foroughi, A.A., Tamiz, M. (2005). An effective total ranking model for a ranked voting system, Omega 33, 491–496.## [7] Green, R.H., Doyle, J.R. and Cook, W.D. (1996). Preference voting and project ranking using DEA and crossevaluation. European Journal of Operational Research 90, 3, pp. 461472.##[8] Ganley, J.A, Cubbin, J.S. (1992) Public sector efficiency measurement: applications of data envelopment analysis. Amsterdam: NorthHolland;##[9] Hashimoto, A. (1997). A ranked voting system using a DEA/AR exclusion model: A note. European Journal of Operational Research 97, 3, pp. 600604.##[10] Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Khanmohammadi, M., & Kazemimanesh, M. (2012) A method for discrimination efficient candidates with ranked voting data by common weightMathematical and Computational Applications, Vol. 17, No. 1, pp.18.##[11] Kao, C., Hung, H.T. (2005) Data envelopment analysis with common weights: the compromise solution approach, J. Oper. Res. Soc. 56 (10), 1196–1203.##[12] Kornbluth J. (1991), Analysing policy effectiveness using cone restricted data envelopment analysis; Journal of the Operational Research Society 42; 10971104.##[13] Lee, S.M. (1973) Goal programming for decision analysis of multiple objectives, Sloan Manage. Rev. 14 , 11–24.##[14] Lee, S.M., Clayton, S.R. (1972) A goal programming model for academic resource allocation, Manage. Sci. 18 (8), B395–B408.##[15] Liu, F.H.F., Peng, H.H. (2008) Ranking of units on the DEA frontier with common weights, Comput. Oper. Res. 35 (5), 1624–1637.##[16] Noguchi, H., Ogawa, M., and Ishii, H. (2002). The appropriate total ranking method using DEA for multiple categorized purposes. Journal of Computational and Applied Mathematics, 146, 155–166.##[17] Obata, T. and Ishii, H. (2003). A method for discriminating efficient candidates with ranked voting data. European Journal of Operational Research 151, 1, pp. 233237.##[18] Tompson, F.D., Singleton, JR., Thrall, R.M., Smith B.A. (1986) Comparative Site Evaluations for Locating a High Energy Lab in Texas, Intetfaces, 13801395##[19] Wang, Y.M., Chin, K.S., and Yang, J.B. (2007). Three new models for preference voting and aggregation. Journal of the Operational Research Society, 58 1389–1393.##[20] Wang, Y.M., Luo, Y., Lan, Y.X. (2011) Common weights for fully ranking decision making units by regression analysis, Expert Syst. Appl. 38 (8), 9122–9128.## [21] Wang, Y.M., Luo, Y. (2006) DEA efficiency assessment using ideal and antiideal decision making units, Appl. Math. Comput. 173 (2) (2006) 902–915.##[22] Wang, Y. M., Luo, Y., and Liang, L. (2009). Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis. Journal of Computational and Applied Mathematics, 223(1), 469–484.##[23] Zerafat Angiz, M. Z., Emrouznejad, A., Mustafa, A., and Rashidi Komijan, A. (2009). Selecting the most preferable alternatives in group decision making problemusing DEA. Expert Systems with Applications, 36, 9599–9602.##]
1

TPM implementation in automotive component manufacturing companies to analyze efficiency injection machine
http://www.journalaprie.com/article_100628.html
10.22105/jarie.2020.208271.1112
1
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, resetting, Material jams, robot damage and Cleaning Mold
0

294
313


Supriyati
.
Department of Industrial Engineering, Mercu Buana University, Jakarta 11650, Indonesia.
Indonesia
supriyati0181@gmail.com


Humiras
Purba
Department of Industrial Engineering, Mercu Buana University, Jakarta 11650, Indonesia.
Indonesia
hardipurba@yahoo.com
OEE
Six bg losses
TPM
maintenance
Equipment
[[1] P. Tsarouhas, “Improving operation of the croissant production line through overall equipment effectiveness ( OEE ) A case study,” Int. J. Product. Perform. Manag., 2018.##[2] D. M. S. N. Manjeet Singh, “Measurement of Overall Equipment Effectiveness ( OEE ) of a Manufacturing Industry : An Effective Lean Tool,” Int. J. Recent Trends Eng. Res., no. 2005, pp. 268–275, 2017.##[3] I. K. Saša Ranđelović*, Milutinović Mladomir, Saša Nikolić, “Risk Assessment In Injection Molding,” J. Technol. Plast., vol. 40, no. 2, 2015.##[4] M. Alorom, “The Implementation of Total Productive Maintenance in The Libyan Heavy Industry,” Dr. THESIS, no. March, 2015.##[5] Kiran, Total Quality Management : Key Concepts and case studies. 2017.##[6] F. Nurprihatin, M. Angely, and H. Tannady, “Total Productive Maintenance Policy to Increase Effectiveness and Maintenance Performance Using Overall Equipment Effectiveness,” vol. 6, no. 3, pp. 184–199, 2019.##[7] A. Muñozvillamizar, J. Santos, J. R. Montoyatorres, and C. Jaca, “AC SC,” Int. J. Prod. Econ., 2018.##[8] S. Perdana and D. Santoso, “Implementation of Repairing Production Machine Productivity of Spare Parts Speaker Based on OEE Value Achievement,” vol. 6, no. 1, pp. 26–32, 2019.##[9] R. Domingo and S. Aguado, “Overall Environmental Equipment Effectiveness as a Metric of a Lean and Green Manufacturing System,” www.mdpi.com/journal/sustainability, pp. 9031–9047, 2015.##[10] L. M. A. Pintelon and P. N. Muchiri, “Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion,” Int. J. Prod. Res. Date, 2010.##[11] B. Yusuf, A. Rahman, and R. Himawan, “Analisa Overall Equipment Effectiveness Untuk Memperbaiki Sistem Perawatan Mesin Dop Berbasis Total Productive ( Studi Kasus : PT XYZ – Malang ),” vol. 3, no. 1, 2015.##[12] M. Braglia, D. Castellano, M. Gallo, and M. Braglia, “A novel operational approach to equipment maintenance : TPM and RCM jointly at work,” J. Qual. Maint. Eng., 2019.##[13] M. S. Y. and H. A. S. N A A Azid , S N A Shamsudin, “Conceptual Analysis and Survey of Total Productive Maintenance ( TPM ) and Reliability Centered Maintenance ( RCM ) Relationship,” IOP Conf. Ser. Mater. Sci. Eng., 2019.##[14] R. Singh, A. M. Gohil, D. B. Shah, and S. Desai, “Total Productive Maintenance ( TPM ) Implementation in a Machine Shop : A Case Study,” Procedia Eng., vol. 51, no. NUiCONE 2012, pp. 592–599, 2013.##[15] A. O. N. Maideen, S. Sahudin, N.H. Mohd yahya, “Practical Framework : Implementing OEE Method in Manufacturing Process Environment,” 2016.##[16] E. Nursubiyantoro and I. Rozaq, “Implementasi Total Productive Maintenance ( TPM ) Dalam Penerapan Overall Equipment,” vol. 9, no. 1, pp. 24–32, 2016.##[17] S. P. and R. U. Zeny Fatimah Hunusalela and JurusanTeknik, “Analysis of productivity improvement in hard disc spare parts production machines based on OEE , FMEA , and fuzzy value in Batam,” IOP Conf. Ser. Mater. Sci. Eng. Pap., 2019.##[18] T. M. Akhmad Sutoni, Widy Setyawan, “Total Productive Maintenance ( TPM ) Analysis on Lathe Machines using the Overall Equipment Effectiveness Method and Six Big Losses,” 2019.##[19] A. D. F. Imam Sodikin, Cyrilla Indri Parwati, “No Title,” Semin. Nas. IENACO  2017, pp. 57–62, 2017.##[20] M. T. Jaroslaw Brodny, “Application of Elements of TPM Strategy for Operation Analysis of Mining Machine Application of Elements of TPM Strategy for Operation Analysis of Mining Machine,” IOP Conf. Ser. Earth Environ. Sci., 2017.##[21] A. Gupta, “Total Productive Maintenance,” Int. J. Recent Technol. Mech. Electr. Eng., no. June, pp. 31–37, 2019.##[22] F. U. Nisbantoro, R. Jinan, and H. H. Purba, “Measurement Overall Equipment Effectiveness on Injection Moulding Machine : A Case Study in Injection Moulding Manufacturing Industry,” no. 8, pp. 62–69, 2018.##[23] I. P. S. Ahuja, “REVIEWS AND CASE STUDIES A case study of total productive maintenance implementation at precision tube mills,” J. Qual. Maint. Eng., vol. 15, no. 3, pp. 241–258, 2009.##[24] C. K. Jha and A. Singh, “Study Of Total Productive Maintenance : A Case Study Of Oee Improvement In Automobile Industry , Benefits And Barriers In Tpm Implementation,” Int. J. Technol. Res. Eng., Vol. 3, No. 9, Pp. 2400–2406, 2016.##[25] D. T. R. S. and M. J. R. M Prashanth Pai, Dr. Ramachandra C G2, “A Study on Usage of Total Productive Maintenance ( TPM ) in Selected SMEs A Study on Usage of Total Productive Maintenance ( TPM ) in Selected SMEs,” 2018.##[26] D. K. N. N. Anand S Relkar, “Optimizing & Analysing Overall Equipment Effectiveness (OEE) Through Design of Experiments (DOE),” vol. 38, pp. 2973–2980, 2012.##[27] D. H. Stamatis, The OEE Primer : Understanding Overall Equipment Effectiveness, Reliability, and Maintainability. 2010.##]
1

Combinatorial optimization of permutationbased quadratic assignment problem using optics inspired optimization
http://www.journalaprie.com/article_95859.html
10.22105/jarie.2019.200177.1106
1
A lot of realworld 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 metaheuristic 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 smallsized problems with 100% efficiency while the efficiency of mediumtolarge size instances is 96%. Accordingly, one can conclude that the proposed OIO has generally high efficiency for solving permutationbased problems.
0

314
332


Soheila
Badrloo
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Iran
soheylabaderloo87@yahoo.com


Ali
Husseinzadeh Kashan
Department of Industerial Engineering, Tarbiat Modares University, Tehran, Iran.
Iran
a.kashan@modares.ac.ir
Quadratic assignment problem
Optics inspired optimization
NPcomplete
Metaheuristics
[[1] Ahmed, Z. H. (2015). An improved genetic algorithm using adaptive mutation operator for the quadratic assignment problem. In Telecommunications and Signal Processing (TSP), th International Conference on, pp. 15. IEEE.##[2] Azarbonyad, H., Babazadeh, R. (2014). A genetic algorithm for solving quadratic assignment problem (QAP). arXiv preprint arXiv:1405.5050.##[3] AbdelBaset, M., Gunsekaran, Doaa., ElShahat, Seyedali Mirjalili.(2018). Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments, Applied Soft Computing Journal.##[4] Armour, GC., Buffa, ES. (1963). Heuristic algorithm and simulation approach to relative location of facilities. Journal of Management Science. Volume 9 Issue 2, Pages 294309.##[5] Ahmed, Z. H. (2018). A hybrid algorithm combining lexisearch and genetic algorithms for the quadratic assignment problem. Cogent Engineering, 5(1), 1423743.##[6] AbdelBaset, M., Wu, H., Zhou, Y., & AbdelFatah, L. (2017). Elite opposition flower pollination algorithm for quadratic assignment problem. Journal of Intelligent & Fuzzy Systems, 33(2), 901911.##[7] Burkard, R.E. (2002). Selected topics on assignment problems. Discrete Applied Mathematics, 123(13), 257302.##[8] Billionnet, A., Elloumi, S. (2001). Best reduction of the quadratic semiassignment problem. Discrete Applied Mathematics, 109(3), 197213.##[9] Burkard, R.E. (1975). Numerische Erfahrungen mit Summen und BottleneckZuordnungsproblemen, in Numerische Methoden bei graphentheoretischen und kombinatorischen Problemen, hrsg. von L. Collatz, G. Meinardus und H. Werner, Birkäuser Verlag BaselStuttgart ISNM Bd. 29(1975), 9–25.##[10] Dickey, J.W.,Hopkins, J.W. (1972). Campus building arrangement using topaz. Transportation Research, 6, 5968.##[11] Dorigo, M., Birattari, M., Stutzle, T.(2006). Ant colony optimization, IEEE Comput. Intell. Mag. 1 (4) pp, 28–39.##[12] Formato, A. (2007). Central force optimization: a new metaheuristic with applications in applied electromagnetics. Progress in Electromagnetics Research, PIER 77, 425491.##[13] Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, AddisonWe.##[14] Kashan, A. H., Karimiyan, S., Karimiyan, M., & Kashan, M. H. (2012, November). A modified League Championship Algorithm for numerical function optimization via artificial modeling of the “between two halves analysis”. In The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems (pp. 19441949). IEEE.##[15] Helber, S., Böhme, D., Oucherif, F., Lagershausen, S., & Kasper, S. (2016). A hierarchical facility layout planning approach for large and complex hospitals. Flexible Services and Manufacturing Journal, 28(12), 529.##[16] Haydar, Kiliç., Ugur, Yuzgeç. (2019). Tournament selection based antlion optimization algorithm for solving quadratic assignment problem. Engineering Science and Technology, an International Journal, Volume 22, Issue 2, Pages 673691.##[17] Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artif. Intell. Rev. 42 (1), pp 21–57.##[18] Kaveh, M. Khayatazad. (2012). A new metaheuristic method: Ray Optimization. Computers and Structures, 112113, 283294.##[19] Koopmans, T.C., Beckmann, M. (1957). Assignment problems and the location of economic activities, Econometrica 25 (1).##[20] Nanda, S.J., Panda, G. (2014). A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm Evol. Comput. 16, pp 1–18.##[21] Pradeepmon, T. G., Sridharan, R., & Panicker, V. V. (2018). Development of modified discrete particle swarm optimization algorithm for quadratic assignment problems. International Journal of Industrial Engineering Computations, 9.##[22] Price, K.V., Storn, R.M., Lampinen, J.A. (2005). Differential evolution: a practical approach to global, Optimization vol 28.##[23] Poli,R., Kennedy,J., Blackwell,T .(2007). Particle swarm optimization, Swarm Intell. 1 (1) PP,33–57.##[24] Tamura, K. Yasuda. (2011). Primary study of spiral dynamics inspired optimization. IEEJ Transactions on Electrical and Electronic Engineering, 6, 98100.##[25] Umut, Tosun. (2015). On the performance of parallel hybrid algorithms for the solution of the quadratic assignment problem”, Engineering Applications of Artificial Intelligence, Vol. 39.##[26] XIA, X., ZHOU, Y. (2018). Performance Analysis of ACO on the Quadratic Assignment Problem. Chinese Journal of Electronics, 27(1).##[27] Burkard, R. E. (1997). Efficiently solvable special cases of hard combinatorial optimization problems. Mathematical programming, 79(13), 5569.##[28] Kashan, A. H. (2015). A new metaheuristic for optimization: optics inspired optimization (OIO). Computers & Operations Research, 55, 99125.##[29] Ugi, I., Brandt, J., Friedrich, J., Gasteiger, J., Jochum, C., Lemmen, P., & Schubert, W. (1979). The deductive solution of chemical problems by computer programs on the basis of a mathematical model of chemistry. In Organic Chemistry (pp. 13031318). Pergamon.##[30] Dokeroglu, T., Sevinc, E., & Cosar, A. (2019). Artificial bee colony optimization for the quadratic assignment problem. Applied Soft Computing, 76, 595606.##[31] Kılıç, H., & Yüzgeç, U. (2019). Tournament selection based antlion optimization algorithm for solving quadratic assignment problem. Engineering Science and Technology, an International Journal, 22(2), 673691.##[32] Konkar, R. (2012). Analysing spherical aberration in concave mirrors. Resonance, 17(8), 779790.##]
1

Development of a forecasting model for investment in Tehran stock exchange based on seasonal coefficient
http://www.journalaprie.com/article_96786.html
10.22105/jarie.2019.196392.1103
1
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.
0

333
366


Reza
Darvishinia
Department of Industrial Engineering, Productivity Management System, Industrial Management Institute (IMI), Tehran, Iran.
Iran
r.darvishinia.tse@gmail.com


Hossein
Ebrahimzadeh Shermeh
Technology Enterprises Incubator Center, University of Mazandaran, Babolsar, Iran.
Iran
shermeh65@yahoo.com


Samira
Barzkar
Department of Economy and Political Science, Central Branch, Islamic Azad University, Tehran, Iran.
Iran
s.barzkar2016@gmail.com
exchange
investment on exchange
seasonal coefficient
ARIMA time series
[[1] Abounoori, E., Tour, M. J. P. A. S. M., & Applications, i. (2019). Stock market interactions among Iran, USA, Turkey, and UAE. 524, 297305.##[2] Ajayi, R. A., Mehdian, S., & Perry, M. J. (2004). The dayoftheweek effect in stock returns: further evidence from Eastern European emerging markets. Emerging Markets Finance and Trade, 40(4), 5362.##[3] Alam, I. M. S., & Sickles, R. C. (1998). The relationship between stock market returns and technical efficiency innovations: evidence from the US airline industry. Journal of Productivity Analysis, 9(1), 3551.##[4] Ariel, R. A. (1987). A monthly effect in stock returns. Journal of Financial Economics, 18(1), 161174.##[5] Balaban, E. (1995). Day of the week effects: new evidence from an emerging stock market. Applied Economics Letters, 2(5), 139143.##[6] Baldwin, G. H. (1982). The Delphi technique and the forecasting of specific fringe benefits. Futures, 14(4), 319325.##[7] Bodie, Z., Kane, A., & Marcus, A. J. (2002). Investments. International Edition. New York, Boston, London.##[8] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control: John Wiley & Sons.##[9] Brockwell, P., & Davis, R. Time series: theory and methods. 1991. Springer, 21, 3334.##[10] Brooks, C., & Persand, G. (2001). Seasonality in Southeast Asian stock markets: some new evidence on dayoftheweek effects. Applied Economics Letters, 8(3), 155158.##[11] Brown, P., Keim, D. B., Kleidon, A. W., & Marsh, T. A. (1983). Stock return seasonalities and the taxloss selling hypothesis: Analysis of the arguments and Australian evidence. Journal of Financial Economics, 12(1), 105127.##[12] Cataldo, I., Anthony, J., & Savage, A. A. (2000). The January effect and other seasonal anomalies: A common theoretical framework: Emerald Group Publishing Limited.##[13] Cholette, P. A. (1982). Prior information and ARIMA forecasting. Journal of Forecasting, 1(4), 375383.##[14] Cholette, P. A., & Lamy, R. (1986). Mutivariate ARIMA forecasting of irregular time series. International Journal of Forecasting, 2(2), 201216.##[15] Coutts, J. A., & Sheikh, M. A. (2002). The anomalies that aren't there: the weekend, January and preholiday effects on the all gold index on the Johannesburg Stock Exchange 19871997. Applied Financial Economics, 12(12), 863871.##[16] De Alba, E. (1993). Constrained forecasting in autoregressive time series models: A Bayesian analysis. International Journal of Forecasting, 9(1), 95108.##[17] De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443473.##[18] Ebrahimzadeh Shermeh, H., Alavidoost, M. H., & Darvishinia, R. J. J. o. A. R. o. I. E. (2018). Evaluating the efficiency of power companies using data envelopment analysis based on SBM models: a case study in power industry of Iran. 5(4), 286295.##[19] Erdugan, R. (2012). The effect of economic factors on the performance of the Australian stock market. Victoria University,##[20] Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of finance, 25(2), 383417.##[21] Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 15751617.##[22] Fogarty, D. W., Hoffmann, T. R., & Stonebraker, P. W. (1989). Production and operations management: SouthWestern Pub.##[23] Guo, S., & Wang, Z. (2008). Market efficiency anomalies: A study of seasonality effect on the Chinese stock exchange. In: Handelshögskolan vid Umeå universitet.##[24] Jaffe, J., & Westerfield, R. (1985). The week‐end effect in common stock returns: the international evidence. The journal of finance, 40(2), 433454.##[25] Kohli, R. K., & Kohers, T. (1992). The weekofthemonth effect in stock returns: The evidence from the S&P composite index. Journal of Economics and Finance, 16(2), 129.##[26] Kuria, A. M., & Riro, G. K. (2013). Stock market anomalies: A study of seasonal effects on average returns of Nairobi securities exchange. Research Journal of Finance and Accounting, 4(7), 207215.##[27] Liano, K., Marchand, P. H., & Huang, G.C. (1992). The holiday effect in stock returns: Evidnece from the OTC market. Review of Financial Economics, 2(1), 45.##[28] Lim, S., Oh, K. W., & Zhu, J. (2014). Use of DEA crossefficiency evaluation in portfolio selection: An application to Korean stock market. European Journal of Operational Research, 236(1), 361368.##[29] Mazal, L. (2009). Stock market seasonality: Day of the week effect and January effect. Master’s thesis, Universidad del Pas Vasco/Euskal Herriko Unibertsitatea,##[30] Pankratz, A. (2009). Forecasting with univariate BoxJenkins models: Concepts and cases (Vol. 224): John Wiley & Sons.##[31] Raj, M., & Kumari, D. (2006). Dayoftheweek and other market anomalies in the Indian stock market. International Journal of Emerging Markets, 1(3), 235246.##[32] Ramezanian, R., Peymanfar, A., & Ebrahimi, S. B. J. A. S. C. (2019). An integrated framework of genetic network programming and multilayer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market. 105551.##[33] Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379402.##[34] Schwert, W. (2003). Anomalies and Market Efficiency, Handbook of the Economics of Finance, ed. Constantinides, Harris and Stultz. In: Elsevier.##[35] Seyyed, F. J., Abraham, A., & AlHajji, M. (2005). Seasonality in stock returns and volatility: The Ramadan effect. Research in International Business and Finance, 19(3), 374383.##[36] Shermeh, H. E., Najafi, S., & Alavidoost, M. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686697.##[37] Thaler, R. H. (1987). Anomalies: the January effect. Journal of Economic Perspectives, 1(1), 197201.##[38] Wachtel, S. B. (1942). Certain observations on seasonal movements in stock prices. The journal of business of the University of Chicago, 15(2), 184193.##[39] Weigerding, M., & Hanke, M. J. B. R. (2018). Drivers of seasonal return patterns in German stocks. 11(1), 173196.##[40] Wissema, J. (1987). Trends in Technology Forecasting: R&D Management. 研究技術計画, 2(4), 520.##[41] Yakob, N. A., Beal, D., & Delpachitra, S. (2005). Seasonality in the Asia Pacific stock markets. Journal of Asset Management, 6(4), 298318.##[42] Ledolter, J., Box, G. E., Tiao, G. C., & WISCONSIN UNIV MADISON DEPT OF STATISTICS. (1976). Topics in Time Series Analysis IV. Various Aspects of Parameter Changes in ARMA Models (No. 499). Technical Report.##]
1

A nonlinear approach for neutrosophic linear programming
http://www.journalaprie.com/article_102443.html
10.22105/jarie.2020.217904.1137
1
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 SingleValued Neutrosophic linear Problem. A numerical experiments are reported to verify the effectiveness of the new algorithms.
0

367
373


Seyed Ahmad
Edalatpanah
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.
Iran
saedalatpanah@gmail.com
Single valued neutrosophic number
Neutrosophic linear programming problem
Linear programming problem
[[1] Smarandache, F.(1998). Neutrosophy: Neutrosophic Probability, Set, and Logic; American##[2] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338353.##[3] Atanssov, K. T. (1986). Intuitionistic fuzzy set. Fuzzy Sets and Systems, 20, 8796.##[4] Ye, J. (2014). Similarity measures between interval neutrosophic sets and their applications in multicriteria decisionmaking. Journal of Intelligent & Fuzzy Systems, 26(1), 165172.##[5] Broumi, S., Smarandache, F., Talea, M., & Bakali, A. (2016). An introduction to bipolar single valued neutrosophic graph theory. In Applied Mechanics and Materials (Vol. 841, pp. 184191). Trans Tech Publications.##[6] Ji, P., Wang, J. Q., & Zhang, H. Y. (2018). Frank prioritized Bonferroni mean operator with singlevalued neutrosophic sets and its application in selecting thirdparty logistics providers. Neural Computing and Applications, 30(3), 799823.##[7] Peng, H. G., Zhang, H. Y., & Wang, J. Q. (2018). Probability multivalued neutrosophic sets and its application in multicriteria group decisionmaking problems. Neural Computing and## Applications, 30(2), 563583.##[8] Wang, J. Q., Zhang, X., & Zhang, H. Y. (2018). Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbers. Journal of## Intelligent & Fuzzy Systems, 34(1), 381394.##[9] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems, International Journal of Engineering and Advanced Technology. 8, 347353.##[10] Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A Multi Objective Programming Approach to Solve Integer Valued Neutrosophic Shortest Path Problems. Neutrosoph Sets Syst, 24, 134149.##[11] Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of Applied Research on Industrial Engineering, 5(4), 339345.##[12] Edalatpanah, S. A., & Smarandache, F. (2019). Data Envelopment Analysis for Simplified Neutrosophic Sets. Neutrosophic Sets & Systems, 29.215226.##]
1

Forecasting as a framework for reducing food waste in Ethiopian university canteens
http://www.journalaprie.com/article_100629.html
10.22105/jarie.2020.206803.1109
1
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.
0

374
380


Abdella
Ali
Department of Mechanical Engineering, Faculty of Technology, Woldia University, Woldia, Ethiopia.
Ethiopia
abdellayimam1@gmail.com


Jemal
Hassen
Department of Mechanical Engineering, Faculty of Engineering and Technology, Assosa University, Assosa, Ethiopia.
Ethiopia
jemal.m20@gmail.com


Gebrekidan
Wendim
Department of Mechanical Engineering, Faculty of Technology, Woldia University, Woldia, Ethiopia.
Ethiopia
simret87gebrekidan@gmail.com
moving average
exponential smoothing
student’s arrival
Students cafeteria
Food waste
[[1] Jamil Salmi, Andrée Sursock, and Anna Olefir. (2017). Improving the Performance of Ethiopian Universities in Science and Technology. World Bank Group.##[2] Ali, A., Ayele, A. (2019). ‘Contribution of Quality Tools for Reducing Food Waste in University Canteen”, Journal of Applied Research on Industrial Engineering, 6(2), pp.125130. doi: 10.22105/jarie.2019.177566.1086.##[3] Waste minimization. Solid and Hazardous Waste Management. http://dx.doi.org/10.1016/B9780128097342.000080.##[4] European Parliament Council. (2008). Directive 2008/1/EC of the European Parliament and of the Council of 15 January 2008 Concerning Integrated Pollution Prevention and Control. Brussels.##[5] Heizer J. and Render B. (2000), Operation Management, Prentice Hall, New Jersey.##[6] Adeniran, A. O., & Stephens, M. S. (2018). The Dynamics for Evaluating Forecasting Methods for International Air Passenger Demand in Nigeria. Journal of tourism & hospitality, 7(4), 111. doi: 10.4172/21670269.1000366.##[7] Song, H. and Li, G. (2008). Tourism demand modelling and forecasting – a review of recent research. Tourism Management, 29: 203220.##[8] Lim, C. and McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4): 965977.##[9] Goh, C. and Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23: 499510.##]