ORIGINAL_ARTICLE
Analytical Hierarchy Process Applied to Supermarket Layout Selection
In many cases, supermarkets are founded on rental of already built property and in this case, the space is accepted as is. It is imperative in this case that the supermarket be designed to achieve multiple objectives such as ease of roaming, display area, special display areas etc. It is important for any store to have a good design, because it will be reflected on the customer’s satisfaction, which in turn will increase sales and will ease customer flow. Several possible ready-made designs are available in the literature such as Grid, loop, and mixed. When implemented on the current space limitations, they can have different achievement for the objectives of the design. Analytical Hierarchy process (AHP) represents a suitable technique for comparing these designs and reaching the final layout design selection. AHP layout selection is applied to a local supermarket and different layouts were generated. The design objectives are then evaluated for each of the layouts. It was concluded initially there is variability in the objectives achieved by the different designs, and a suitable multi-objective technique is required to select the final design. AHP is used to determine which of the designs was the most suitable through multiple comparison and consistency check. The results showed that the grid design was the most suitable for the current case study.
https://www.journal-aprie.com/article_54706_af86362d8feb4ea6b96e7a3d2cd2dd40.pdf
2017-12-01
215
226
10.22105/jarie.2017.54706
Supermarket Layout
Selection Criteria
Analytical Hierarchy Process
Mahmoud. A.
Barghash
mabargha@gmail.com
1
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
LEAD_AUTHOR
Lina
Al-Qatawneh
2
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
AUTHOR
Saleem
Ramadan
3
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
AUTHOR
Awwad
Dababneh
4
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
AUTHOR
[1] Ebster, C. (2011). Store design and visual merchandising: Creating store space that encourages buying. Business Expert Press.
1
[2] Golden, B. L., Wasil, E. A., & Harker, P. T. (1989). The analytic hierarchy process. Applications and Studies, Berlin, Heidelberg.
2
[3] Bhushan, N., & Rai, K. (2007). Strategic decision making: applying the analytic hierarchy process. Springer Science & Business Media.
3
[4] Saaty, T. L., & Vargas, L. G. (2012). Models, methods, concepts & applications of the analytic hierarchy process (Vol. 175). Springer Science & Business Media.
4
[5] Dolan, J. G. (2008). Shared decision-making–transferring research into practice: the Analytic Hierarchy Process (AHP). Patient education and counseling, 73(3), 418-425.
5
[6] AlKaabneh, F. A., Barghash, M., & Mishael, I. (2013). A combined analytical hierarchical process (AHP) and Taguchi experimental design (TED) for plastic injection molding process settings. The International Journal of Advanced Manufacturing Technology, 1-16.
6
[7] Levy, M., & Weitz, B. A. (2004). Retailing management. McGraw-Hill/Irwin.
7
ORIGINAL_ARTICLE
Root Cause Analysis and Productivity Improvement Of An Apparel Industry In Bangladesh Through Kaizen Implementation
Garments industry is playing the pioneering role in improving Bangladesh economic condition. It was started in late 1970’s and now the leading foreign currency earner for Bangladesh. It’s no dubiousness to say that, the Bangladesh garment industry is ameliorating garment’s service quality and innovative design features to exist in the global competitive market. Global competition in the garment’s market is changing day to day. Leading garment manufacturer from all over the world are adopting new innovative features and techniques to sustain global fierce competitive market. But the point is, Bangladeshi garment manufacturers are not lingered. They are also emphasizing on better service quality by adding latest design features and using the latest technologies to the garments. The sole purpose of this paper is to identify the root causes of sewing defects of an apparel industry in Bangladesh and continuous improvement in reducing the defects through Kaizen (Continuous Improvement) system. In short, productivity improvement of the apparel industry. Our studied garment manufacturing company is “ABONTI Color Tex. Ltd.”. Pareto Analysis is used to identify the top defect items. Cause-Effect Analysis helped to identify the root causes of sewing defects. Then, Kaizen is used for continuous improvement of the minimization of sewing defects.
https://www.journal-aprie.com/article_54267_7fd87a9d5bccb2e1c0afdae08e6618b7.pdf
2017-12-01
227
239
10.22105/jarie.2017.108637.1025
Kaizen
Productivity Improvement
Pareto
Cause-Effect
Lean manufacturing
Taposh Kumar
Kapuria
sabita.kapuria@gmail.com
1
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
LEAD_AUTHOR
Mustafizur
Rahman
mostafizkuet09@gmail.com
2
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
Shuvo
Haldar
shuvoh38@gmail.com
3
Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore-7408, Bangladesh.
AUTHOR
[1] Bangladesh Garment Manufacturers and Exporters Association (BGMEA) –Government recognized trade body of garment factories of Bangladesh. (2015). Retrieved from: http://en.banglapedia.org/index.php?title=Bangladesh_Garment_Manufacturers_and_Exporters_Association
1
[2] Wilson, P. F. (1993). Root cause analysis: A tool for total quality management. ASQ Quality Press.
2
[3] Siekański, K., & Borkowski, S. (2003). Analysis of foundry defects and preventive activities for quality improvement of castings. Metalurgija, 42(1), 57-59.
3
[4] Jadhav, B., & Jadhav, S. J. (2013). Investigation and analysis of cold shut casting defect and defect reduction by using 7 quality control tools. International Journal of Applied Engineering Research (IJAER), 28-30.
4
[5] Joshi, A., & Jugulkar, L. M. Investigation and analysis of metal casting defects and defect reduction by using quality control tools. International journal of mechanical and production engineering, ISSN, 2320-2092.
5
[6] Juriani, A. (2015). Casting defects analysis in foundry and their remedial measures with industrial case studies. IOSR journal of mechanical and civil engineering, 12(6), 43-54.
6
[7] Kamble, B. S. (2016). Analysis of different sand casting defects in a medium scale foundry industry-A review. International journal of innovative research in science, engineering and technology, 5(2).
7
[8] Hossen, J., Ahmad, N., & Ali, S. M. (2017). An application of Pareto analysis and cause-and-effect diagram (CED) to examine stoppage losses: a textile case from Bangladesh. The journal of the textile institute, 1-9.
8
[9] Chandna, P., & Chandra, A. (2009). Quality tools to reduce crankshaft forging defects: an industrial case study. Journal of industrial and systems engineering, 3(1), 27-37.
9
[10] Khekalei, S. N., Chatpalliwar, A. S., & Thaku, N. (2010). Minimization of cord wastages in belt industry using DMAIC. International journal of engineering science and technology, 2(8), 3687-3694.
10
[11] Ahmed, M., & Ahmad, N. (2011). An application of Pareto analysis and cause-and-effect diagram (CED) for minimizing rejection of raw materials in lamp production process. Management science and engineering, 5(3), 87-95.
11
[12] Dabade, U. A., & Bhedasgaonkar, R. C. (2013). Casting defect analysis using design of experiments (DoE) and computer aided casting simulation technique. Procedia CIRP, 7, 616-621.
12
[13] Farris, J. A., Van Aken, E. M., Doolen, T. L., & Worley, J. (2008). Learning from less successful Kaizen events: a case study. Engineering management journal, 20(3), 10-20.
13
[14] Singh, J., & Singh, H. (2009). Kaizen philosophy: a review of literature. IUP journal of operations management, 8(2), 51.
14
[15] Glover, W. J. (2010). Critical success factors for sustaining Kaizen Event outcomes (Doctoral dissertation). Retrieved from https://vtechworks.lib.vt.edu/handle/10919/26914
15
[16] Kr, V. (2011). An Overview of Kaizen Concept. VSRD international journal of mechanical. Automobile & production engineering, 1(3), 120-125.
16
[17] Suárez-Barraza, M. F., & Miguel-Dávila, J. Á. (2011). Implementation of Kaizen in Mexico: An exploratory study for a japanese managerial approach in the Latinamerican context. Innovar, 21(41), 19-38.
17
[18] Rivera-Mojica, D., & Rivera-Mojica, L. (2014). Critical success factors for kaizen implementation. Lean manufacturing in the developing world (pp. 157-178). Springer International Publishing.
18
[19] Sharma, P., Sharma, N. K., & Singh, M. P. (2015). Process improvement by implementation of kaizen as a quality tool within defined constraints: a case study in manufacturing industry. Matter: International journal of science and technology, 1(1).
19
[20] Mohd Asaad, M. N., & Yusoff, R. Z. (2015). Kaizen implementation in improving organizational excellence. Ulum islammiyah journal. 15, 23-43.
20
[21] Desta, A., Asgedom, H. B., Gebresas, A., & Asheber, M. (2014). Analysis of kaizen implementation in Northern Ethiopia’s manufacturing industries. International journal of business and commerce, 3(8), 39.
21
[22] Ratnawati, J., Ingsih, K., & Nuryanto, I. (2016). The implementation of Kaizen philosophy to improve industrial productivity: A case study of ISO manufacturing companies in Indonesia. International journal of business and economics research, 14(2), 1343-1357.
22
ORIGINAL_ARTICLE
Understanding Econometric Modeling: Domestic Air Travel in Nigeria and Implication for Planning Process
For planning process, this study examined the econometric model of domestic air travel in Nigeria vis-à-vis some selected economic variables. Furthermore, quantitative (inferential) statistics has used which relies on data obtained from relevant government institutions in Nigeria. Also the model was estimated using Ordinary Least Square (OLS) regression. From the estimate; the predictor variables constant revealed that Domestic Passenger demand is a negative value which signifies that the predictors (economic variables) cannot give true estimate of the domestic airline forecast regardless of the positive regression coefficient for the predictors. On the other side, Domestic Passenger demand positively contributes to economic indicators. When validating the model estimate, test of significance revealed that there is no statistically significant relationship between the variables. Based on the insignificance, the model estimate cannot give a good forecast. Test for multicollinearity revealed that the coefficient of determination (R2) is 0.805 which is greater than 0.8. This signifies that there is a problem of multicollinearity. Based on this problem, the model estimate cannot give a good forecast. Goodness of fit test revealed that 80.5% of the dependent variable (Domestic Passenger demand) can be explained by the independent variables. The regression value signifies that the model can give a true forecast. Finally, based on the issues of validation, it is therefore concluded that the model cannot give a true forecast, hence economic indicators contributes little or no to air transport demand but rather air transport demand contributes significantly to economic indicators.
https://www.journal-aprie.com/article_54268_efff4339385cb848aaaadd330d836a1e.pdf
2017-12-01
240
251
10.22105/jarie.2017.97899.1018
Econometric Model
Air transport demand
Transportation
Adetayo
Olaniyi Adeniran
4tynil@gmail.com
1
Department of Transport Management Technology, Federal University of Technology, Akure, Nigeria.
LEAD_AUTHOR
Sidiq Okwudili
Ben
sidiqoben73@gmail.com
2
Department of Geography, College of Education, Ikere, Nigeria.
AUTHOR
[1] Adeniran, A. O. & Adeniran, A. A. (2017). Econometric Modeling of Passenger Demand for International Air Transport in Nigeria Airports. American journal of traffic and transportation engineering,2(4), 39- 44.doi: 10.11648/j.ajtte.20170204.11
1
[2] Asiyanbola, R. A., & Akinpelu, A. A. (2012). The challenges of on-street parking in Nigerian Cities’ transportation routes. International journal of development and sustainability, 1(2), 476-489.
2
[3] Gbadamosi, K. T. (2002). Traffic regulations and road traffic accidents in Nigeria–A spatial analysis (Unpublished PhD dissertation). Ibadan: University of Ibadan.
3
[4] Ahmadzade, F. (2010, January). Model for forecasting passenger of airport. Proceedings of the 2010 international conference on industrial engineering and operations management. 9-10. Dhaka, Bangladesh January.
4
[5] [5]. Carmona-Benitez, R. B. (2012). The design of a large scale airline network. thesis(Delft University of Technology). Thesis Series T 2012/2, Delft.
5
[6] Smith, M. J. (2002). The airline encyclopedia, 1909-2000. Lanham, MD: Scarecrow Press.
6
[7] Leahy, J. (2011). Delivering the future: Global market forecast. Retrieved from:
7
www.team.aero/.../Airbus_GMF_2011-2030_delivering_the_future_-_press_conferen...
8
[8] Dargay, J., & Hanly, M. (2001). The determinants of the demand for international air travel to and from the UK. ESRC transport studies unit, centre for transport studies. University College: London.
9
[9] Alperovich, G., & Machnes, Y. (1994). The role of wealth in the demand for international air travel. Journal of transport economics and policy, 163-173.
10
[10] Saheed, A. A. A., & Iluno, S. Z. C. (2015). Air Transportation Development and Economic Growth in Nigeria. Journal of economics and sustainable development, 6(2).
11
[11] Isma’il, M., Musa, I. J., Daniel, D., Musa, I., & Adamu, G. (2014). Analysis of the economic benefits of gombe international airport in Nigeria. Global journal of research and review, 1(3).
12
[12] Afolayan, O. S., Asaju, A. J., & Malik, N. A. (2012). Variation in spatial trend of passengers and aircrafts movement in Nigerian international airports. International journal of humanities and social science, 2 (10), 126-133.
13
[13] Nigeria air passenger traffic. (2016). Retrieved from https://nairametrics.com/
14
[14] National bureau of statistics. (2017). Retrieved from http://www.nigerianstat.gov.ng/
15
[15] Adeniran, A. O., Adekunle, E. A., & Oyedele, O. J. (2017).Establishing the concept of research hypothesis through the relationship between demand in Nigeria international air passenger traffic and economic variables. International journal of economic behavior and organization, 5(5), 105-113.
16
[16] Gujarati, D. N. (2003). Basic Econometrics Fourth Edition McGraw Hill Gujarati, DN, (2003). Basic Econometrics.
17
ORIGINAL_ARTICLE
Life Cycle Costing of PV Generation System
Life cycle costing (LCC) is a methodology used first time by the Department of Defense of United State, it’s an economic calculation of all costs propagated during the life span of any technical system. For Renewable Energy (RE) systems, LCC is a good methodology, which shows the cost-effectiveness of using RE as an alternative source compared to conventional power generations. A LCC model was introduced for PV generation system. Data collection was done through four different cost data sources. The results shows that the average module price is $0.56/Wp and the capital investment cost is $1.184/Wp. For a 20 years PV project life-time, the operation and maintenance cost forms 27% of the total LCC of the system.
https://www.journal-aprie.com/article_54724_4e5a256ff89a93cd0a5b12c5116c96f3.pdf
2017-12-01
252
258
10.22105/jarie.2017.54724
Life Cycle Costing
PV system
PV Module
Maintenance Cost
Ala’ K.
Abu-Rumman
1
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
AUTHOR
Iyad
Muslih
2
Department of Mechanical and Industrial Engineering, Applied Science University, Jordan.
AUTHOR
Mahmoud. A.
Barghash
mabargha@gmail.com
3
Department of Industrial Engineering, University of Jordan, Amman, Jordan.
LEAD_AUTHOR
[1] Barringer, H. P., Weber, D. P., & Westside, M. H. (1995). Life-cycle cost tutorials. Fourth international conference on process plant reliability, Gulf Publishing Company.
1
[2] Fuller, S., & Petersen, S. (1996). Life-cycle costing manual for the federal energy management program, NIST Handbook 135. Handbook (NIST HB), 135.
2
[3] Sayigh, A. A. M. (Ed.). (2012). Solar energy engineering. Elsevier.
3
[4] Whiteman, A., Rinke, T., Esparrago, J., & Elsayed, S. (2016). Renewable capacity statistics 2016. IRENA, 3, 29.
4
[5] Agency, I. E. (2015). Renewable energy medium-term market report 2015. Retrieved from https://www.iea.org/Textbase/npsum/MTrenew2015sum.pdf
5
[6] Fraunhofer institute for solar energy systems. (2016). Retrieved from:
6
https://www.ise.fraunhofer.de/en.html
7
[7] Hastings, S. & Dronkers, B. (2016). Fact sheet : The true price of wind and solar electricity generation. Pembin institude. Retrieved from http://www.pembina.org/
8
[8] Mehta, S. (2013). PV Technology and Cost Outlook, 2013-2017. Retrieved from:
9
https://www.greentechmedia.com/.../pv-technology-and-cost-outlook-2013-2017
10
[9] Feldman, D. et al. (2015). Photovoltaic system pricing trends - historical, recent, and near-term projections projections. Retrieved from https://www.nrel.gov/docs/fy14osti/62558.pdf
11
[10] Kimura, K. & Zissler, R. (2016). Comparing prices and costs of solar PV in Japan and Germany- The reasons why solar PV is more expensive in Japan. Renewable energy institute. Retrieved from https://www.renewable-ei.org/.../JREF_Japan_Germany_solarpower_costcomparison_...
12
[11] Rehman, S., Bader, M. A. & Al-Moallem, S. A. (2007). Cost of solar energy generated using PV panels. Renewable and sustainable energy reviews, 11, 1843–1857.
13
[12] Campbell, M., Aschenbrenner, P., Blunden, J., Smeloff, E., & Wright, S. (2008). The drivers of the levelized cost of electricity for utility-scale photovoltaics. White paper: SunPower corporation.
14
[13] Yang, C. J. (2010). Reconsidering solar grid parity. Energy policy, 38(7), 3270-3273.
15
[14] Chung, D., Davidson, C., Fu, R., Ardani, K., & Margolis, R. (2015). US photovoltaic prices and cost breakdowns. Q1 2015 benchmarks for residential, commercial, and utility-scale systems. National Renewable Energy Lab.(NREL).
16
[15] Shah, V., & Booream-Phelps, J. (2015). FITT for investors: Crossing the chasm. Deutsche bank markets research. Retrieved from:
17
www. db. com/newsroom_news/markets_research_solar_industry. pdf
18
[16] Parida, B., Iniyan, S., & Goic, R. (2011). A review of solar photovoltaic technologies. Renewable and sustainable energy reviews, 15(3), 1625-1636.
19
[17] Mulligan, C. J., Bilen, C., Zhou, X., Belcher, W. J., & Dastoor, P. C. (2015). Levelised cost of electricity for organic photovoltaics. Solar energy materials and solar cells, 133, 26-31.
20
[18] El-Shimy, M. (2012, December). Analysis of levelized cost of energy (LCOE) and grid parity for utility-scale photovoltaic generation systems. Proceeding of 15th International Middle East Power Systems Conference (MEPCON’12) (pp. 1-7). DOI: 10.13140/RG.2.2.10311.29603
21
[19] Stavy, M. (2002). Computing the levelized cost (B R¢/kWh [US ¢/kWh]) of solar electricity generated at grid connected Photovoltaic (PV) generating plants. Proceeding of World Climate & Energy Event. (pp. 6-11). Rio de Janeiro, Brazil.
22
[20] Ragnarsson, B. F., Oddsson, G. V., Unnthorsson, R., & Hrafnkelsson, B. (2015). Levelized cost of energy analysis of a wind power generation system at burfell in iceland. Energies, 8(9), 9464-9485.
23
[21] Myhr, A., Bjerkseter, C., Ågotnes, A., & Nygaard, T. A. (2014). Levelised cost of energy for offshore floating wind turbines in a life cycle perspective. Renewable energy, 66, 714-728.
24
[22] Kost, C., Mayer, J. N., Thomsen, J., Hartmann, N., Senkpiel, C., Philipps, S., ... & Schlegl, T. (2013). Levelized cost of electricity renewable energy technologies. Fraunhofer institute for Solar Energy Systems ISE.
25
[23] Desideri, U., & Campana, P. E. (2014). Analysis and comparison between a concentrating solar and a photovoltaic power plant. Applied energy, 113, 422-433.
26
[24] Gielen, D. (2012). Renewable energy technologies: cost analysis series. Sol photovolt, 1(1), 52.
27
[25] Renewable energy technologies: Cost analysis series. (2012). International Renewable Energy Agency (IRENA). Retrieved from:
28
https://www.irena.org/.../RE_Technologies_Cost_Analysis-SOLAR_PV.pdf
29
[26] Hearps, P., & McConnell, D. (2011). Renewable energy technology cost review. Melbourne energy institute technical paper series.
30
[27] Dale, M. (2013). A comparative analysis of energy costs of photovoltaic, solar thermal, and wind electricity generation technologies. Applied sciences, 3(2), 325-337.
31
[28] Branker, K., Pathak, M. J. M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and sustainable energy reviews, 15(9), 4470-4482.
32
[29] Darling, S. B., You, F., Veselka, T., & Velosa, A. (2011). Assumptions and the levelized cost of energy for photovoltaics. Energy & environmental science, 4(9), 3133-3139.
33
ORIGINAL_ARTICLE
The new approach in market segmentation by using RFM model
Data analytics allows companies mining the patterns and trends in their customers data to implement more effective market segmentation strategies, then customize promotional offers, allocate marketing resources efficiently, and improve customer relationship management. However the implementation of such strategies often hampered by limited budgets and the ever-changing priorities and goals of marketing campaigns. So, This paper suggests and demonstrates the novel approach dividing a broad target market into subsets of consumers who have common needs, interests, and priorities, and then designing and implementing strategies to target them to achieve profit maximization. Therefore, the aims of this study are twofold, first, is to use historical data (such as purchased items and the associative monetary expenses), the proposed model identifies customer segments based on Firefly Algorithm (FA). Second, is the identification of the most profitable segment according to the RFM model (recency, frequency and monetary). In this article real marketing data are used to illustrate the proposed approach.
https://www.journal-aprie.com/article_53422_536598f5e6bb29cb85ae20b09930b8ce.pdf
2017-12-01
259
267
10.22105/jarie.2017.91297.1011
Customer segmentation
Profitably
Fire fly algorithm
RFM model
Hadi
Roshan
roshan.hadi@gmail.com
1
Department of Engineering, University of East of Guilan,Iran.
LEAD_AUTHOR
Masoumeh
Afsharinezhad
masoumehafshar93@gmail.com
2
Department of Management, University of Payame noor, Iran.
AUTHOR
[1] Apostolopoulos, T., & Vlachos, A. (2010). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International journal of combinatorics. http://dx.doi.org/10.1155/2011/523806
1
[2] Baecke, P., & Van den Poel, D. (2011). Data augmentation by predicting spending pleasure using commercially available external data. Journal of intelligent information systems, 36(3), 367-383.
2
[3] Baier, M., Ruf, K. M., & Chakraborty, G. (2002). Contemporary database marketing: concepts and applications. Racom Communications.
3
[4] Alex, B., Stephen, S., & Kurt, T. (2000). Building data mining applications for CRM. New York (etc.): McGraw-Hill.
4
[5] Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer equity: Building and managing relationships as valuable assets. Harvard Business Press.
5
[6] Malhotra, N. K., Birks, D. F., Palmer, A., & Koenig-Lewis, N. (2003). Market research: an applied approach. Journal of marketing management, 27, 1208-1213.
6
[7] Chan, S. L., & Ip, W. H. (2011). A dynamic decision support system to predict the value of customer for new product development. Decision support systems, 52(1), 178-188.
7
[8] Chang, E. C., Huang, S. C., Wu, H. H., & Lo, C. F. (2007, December). A case study of applying spectral clustering technique in the value analysis of an outfitter’s customer database. Proceedings of international conference on industrial engineering and engineering management. 1743-1746. IEEE.
8
[9] Chiliya, N., Herbst, G., & Roberts-Lombard, M. (2009). The impact of marketing strategies on profitability of small grocery shops in South African townships. African journal of business management, 3(3), 70.
9
[10] Chiu, C. Y., Chen, Y. F., Kuo, I. T., & Ku, H. C. (2009). An intelligent market segmentation system using k-means and particle swarm optimization. Expert systems with applications, 36(3), 4558-4565.
10
[11] Croft, M. J. (1994). Market segmentation: A step-by-step guide to profitable new business. Cengage Learning Emea.
11
[12] Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of marketing research, 42(4), 415-430.
12
[13] Farsi,D., Fatahi, N., & Kosha, H. (2008). Market segmentation based on customers lifetime value (CLV). Proceedings of 6th international conference on engineering. Tehran, Iran.
13
[14] Hong, C. W. (2012). Using the Taguchi method for effective market segmentation. Expert systems with applications, 39(5), 5451-5459.
14
[15] Hsu, F. M., Lu, L. P., & Lin, C. M. (2012). Segmenting customers by transaction data with concept hierarchy. Expert systems with applications, 39(6), 6221-6228.
15
[16] Hughes, A. M. (2005). Strategic database marketing. McGraw-Hill Pub. Co..
16
[17] Joh, C. H., Timmermans, H. J., & Popkowski-Leszczyc, P. T. (2003). Identifying purchase-history sensitive shopper segments using scanner panel data and sequence alignment methods. Journal of retailing and consumer services, 10(3), 135-144.
17
[18] Kamakura, W., Mela, C. F., Ansari, A., Bodapati, A., Fader, P., Iyengar, R., ... & Wedel, M. (2005). Choice models and customer relationship management. Marketing letters, 16(3), 279-291.
18
[19] Kohavi, R., & Parekh, R. (2004, April). Visualizing RFM segmentation. Proceedings of the 2004 SIAM international conference on data mining (pp. 391-399). Society for Industrial and Applied Mathematics.
19
[20] Kotler, P., & Gordon, M. (1983). Principles of market. Canada: Prentice Hall.
20
[21] Kottler, P., & Keller, K. L. (2003). Marketing management. Analyse, Planung, Umsetzung und.
21
[22] Liu, H. H., & Ong, C. S. (2008). Variable selection in clustering for marketing segmentation using genetic algorithms. Expert systems with applications, 34(1), 502-510.
22
[23] Berzosa, D. L., Davila, J. A. M., & de Pablos Heredero, C. (2012). Business model transformation in the mobile industry: co-creating value with customers. Transformation in business & economics, 11(2).
23
[24] McLoughlin, F., Duffy, A., & Conlon, M. (2015). A clustering approach to domestic electricity load profile characterisation using smart metering data. Applied energy, 141, 190-199.
24
[25] Holloway, J. C. (2004). Marketing for tourism. Pearson education.
25
[26] Mutandwa, E., Kanuma, N. T., Rusatira, E., Kwiringirimana, T., Mugenzi, P., Govere, I., & Foti, R. (2009). Analysis of coffee export marketing in Rwanda: Application of the Boston consulting group matrix. African journal of business management, 3(5), 210.
26
[27] Myers, J. H. (1996). Segmentation and positioning for strategic marketing decisions. American Marketing Association.
27
[28] Seret, A., Maldonado, S., & Baesens, B. (2015). Identifying next relevant variables for segmentation by using feature selection approaches. Expert systems with applications, 42(15), 6255-6266.
28
[29] Assael, H. (1984). Consumer behavior and marketing action. Kent Pub. Co..
29
[30] Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of marketing, 21(1), 3-8.
30
[31] Stone, B., & Jacobs, R. (1988). Successful direct marketing methods. Lincolnwood, IL: NTC Business Books.
31
[32] Tsai, C. Y., & Chiu, C. C. (2004). A purchase-based market segmentation methodology. Expert systems with applications, 27(2), 265-276.
32
[33] Wang, C. H. (2010). Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques. Expert systems with applications, 37(12), 8395-8400.
33
[34] Weinstein, A. (1987). Market segmentation: Using Niche marketing to exploit new markets. Probus Publishing.
34
[35] Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons.
35
[36] Yang, M. S., Hung, W. L., & Chung, T. I. (2006). Alternative fuzzy clustering algorithms with l1-norm and covariance matrix. Proceedings of international conference advanced concepts for intelligent vision systems. 654-665. Springer Berlin/Heidelberg.
36
ORIGINAL_ARTICLE
A new Technique for Investigating the Dynamic Response of a Beam Subjected to a Load-Moaving System
The dynamic response of a homogeneous elastic simply-supported beam subjected to a load system moving with a uniform velocity is studied in detail in this paper. Analytical expressions for the dynamic responses of the beam and the load-moving system are obtained by means of a new technique using decomposition method whereby the generalized displacement of the beam is written as an infinite series. The method is versatile and simple so that its application to other related problems is possible. Comparisons between different cases of load-moving systems are made clear. Interaction, load, mass, velocity effects on the beam as well as on the load-moving system are investigated. It is concluded that the inertia effect of the load-moving system cannot be neglected when the traveling velocity and its mass ratio to that of the beam are large.
https://www.journal-aprie.com/article_54714_a8c1d4c6e47e739bcc739bfd75a00910.pdf
2017-12-01
268
278
10.22105/jarie.2017.54714
Decomposition Method
Load-Moving Systems
Simply-Supported Beam
Ibrahim Mousa
Abu-Alshaikh
i.abualshaikh@ju.edu.jo
1
Department of Mechanical Engineering, University of Jordan, Amman-11942, Jordan.
LEAD_AUTHOR
[1] Frýba, L. (2013). Vibration of solids and structures under moving loads (Vol. 1). Springer science & business media.
1
[2] Frýba, L. (1976). Non-stationary response of a beam to a moving random force. Journal of sound and vibration, 46(3), 323-338.
2
[3] Śniady, P. (1989). Dynamic response of linear structures to a random stream of pulses. Journal of sound and vibration, 131(1), 91-102.
3
[4] Zibdeh, H. S. (1995). Stochastic vibration of an elastic beam due to random moving loads and deterministic axial forces. Engineering structures, 17(7), 530-535.
4
[5] Zibdeh, H. S., & Rackwitz, R. (1995). Response moments of an elastic beam subjected to poissonian moving loads. Journal of sound and vibration, 188(4), 479-495.
5
[6] Savin, E. (2001). Dynamic amplification factor and response spectrum for the evaluation of vibrations of beams under successive moving loads. Journal of sound and vibration, 248(2), 267-288.
6
[7] Yang, Y. B., & Lin, C. W. (2005). Vehicle–bridge interaction dynamics and potential applications. Journal of sound and vibration, 284(1), 205-226.
7
[8] Zibdeh, S. H., & Juma, S. H. (1999). Dynamic response of a rotating beam subjected to a random moving load. Journal of sound and vibration, 223(5), 741-758.
8
[9] Katz, R., Lee, C. W., Ulsoy, A. G., & Scott, R. A. (1988). The dynamic response of a rotating shaft subject to a moving load. Journal of sound and vibration, 122(1), 131-148.
9
[10] Argento, A., Morano, H. L., & Scott, R. A. (1994). Accelerating load on a rotating Rayleigh beam. Journal of vibration and acoustics, 116(3), 397-403.
10
[11] Thambiratnam, D., & Zhuge, Y. (1996). Dynamic analysis of beams on an elastic foundation subjected to moving loads. Journal of sound and vibration, 198(2), 149-169.
11
[12] Mallik, A. K., Chandra, S., & Singh, A. B. (2006). Steady-state response of an elastically supported infinite beam to a moving load. Journal of sound and vibration, 291(3), 1148-1169.
12
[13] Foda, M. A., & Abduljabbar, Z. (1998). A dynamic green function formulation for the response of a beam structure to a moving mass. Journal of sound and vibration, 210(3), 295-306.
13
[14] G. Foda, M. A., & Abduljabbar, Z. (1998). A dynamic green function formulation for the response of a beam structure to a moving mass. Journal of sound and vibration, 210(3), 295-306.
14
[15] Yang, B., Tan, C. A., & Bergman, L. A. (2000). Direct numerical procedure for solution of moving oscillator problems. Journal of engineering mechanics, 126(5), 462-469.
15
[16] Pesterev, A. V., & Bergman, L. A. (2000). An improved series expansion of the solution to the moving oscillator problem. Journal of vibration and acoustics, 122(1), 54-61.
16
[17] Adomian, G. (1984). A new approach to nonlinear partial differential equations. Journal of Mathematical Analysis and Applications, 102(2), 420-434.
17
[18] Adomian, G. (1994). Solving frontier problems of physics: The decomposition method. Klumer, Boston.
18
ORIGINAL_ARTICLE
Finite Element Coding of Functionally Graded Beams under Various Boundary and Loading Conditions
Detailed formulation and coding of exact finite element is carried out to study the static behavior of a layered beam structure. The beam element is modelled based on the first-order shear deformation theory and it is assumed to be composed of three layers whereas the middle layer is made of functionally graded material (FGM), i.e. with variable elastic properties in the thickness direction. The shape of the FGM mechanical properties variation in the thickness direction takes the form of exponential or power-law. The governing equations and boundary conditions are derived by applying the virtual work principle. Variations of displacements along the beam and stresses across the depth due to mechanical loadings are investigated. Comparative examples are carried out to highlight the static behavior difference between FGM layered beams and pure metal-ceramic beams.
https://www.journal-aprie.com/article_54713_b66b4694db7c9da855711d32d41fa607.pdf
2017-12-01
279
290
10.22105/jarie.2017.54713
Beam Structure
Shear Deformation Theory
Functionally Graded Material
Othman
Al-Hawamdeh
ohawamdeh@philadelphia.edu.jo
1
Department of Mechanical Engineering, University of Jordan, Amman-11942, Jordan.
LEAD_AUTHOR
Ibrahim Mousa
Abu-Alshaikh
i.abualshaikh@ju.edu.jo
2
Department of Mechanical Engineering, University of Jordan, Amman-11942, Jordan.
AUTHOR
Naser
Al-Huniti
3
Department of Mechanical Engineering, University of Jordan, Amman-11942, Jordan.
AUTHOR
[1] Koizumi, M. F. G. M. (1997). FGM activities in Japan. Composites part B: Engineering, 28(1-2), 1-4.
1
[2] Chakraborty, A., Mahapatra, D. R., & Gopalakrishnan, S. (2002). Finite element analysis of free vibration and wave propagation in asymmetric composite beams with structural discontinuities. Composite structures, 55(1), 23-36.
2
[3] Chakraborty, A., Gopalakrishnan, S., & Reddy, J. N. (2003). A new beam finite element for the analysis of functionally graded materials. International journal of mechanical sciences, 45(3), 519-539.
3
[4] García-Vallejo, D., Mikkola, A. M., & Escalona, J. L. (2007). A new locking-free shear deformable finite element based on absolute nodal coordinates. Nonlinear dynamics, 50(1), 249-264.
4
[5] Kadoli, R., Akhtar, K., & Ganesan, N. (2008). Static analysis of functionally graded beams using higher order shear deformation theory. Applied mathematical modelling, 32(12), 2509-2525.
5
[6] Roy, A., & Khan, K. (2013). Static response analysis of a FGM Timoshenko’s Beam subjected to Uniformly Distributed Loading Condition. MIT international journal of mechanical engineering, 3(2), 80–85.
6
[7] Khan, A. A., Naushad Alam, M., & Wajid, M. (2016). Finite element modelling for static and free vibration response of functionally graded beam. Latin American journal of solids and structures, 13(4), 690-714.
7
[8] El-Ashmawy, A. M., Kamel, M. A., & Elshafei, M. A. (2016). Thermo-mechanical analysis of axially and transversally Function Graded Beam. Composites part B: Engineering, 102, 134-149.
8
[9] Ugural, A. (1999). Stresses in plates and shells. McGraw-Hill.
9