2019
6
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Quality improvement of molding machine through statistical process control in plastic industry
http://www.journalaprie.com/article_82682.html
10.22105/jarie.2019.163584.1068
1
Enhancing the process capability is a must in quality improvement of process manufacturing in the industry. Usually, Statistical Process Control (SPC) is applied in measure the activity of quality improvement. In this case, the statistical process control used to measure the process capability of molding machine regarding of force result of inner contact rubber button in plastic industry. The forces of inner contact rubber button which produce by molding machine already become the critical point as a judgment of malfunctions or not. Meanwhile, the forces problem still found in molding machine that affected to the inner contact rubber button as defect product. Furthermore, as shown of SPC, the process capability of molding machine was 0.63. This mean the process capability is still need the improvement. As the result of quality improvement was made by applied of PokaYoke, the process capability of molding machine was improved to 1.65.
0

87
96


Teuku
Saputra
Master of Industrial Engineering Program, Mercu Buana University, Indonesia.
Indonesia
teuku_mirwan@yahoo.com


Hernadewita
Hernadewita
Master of Industrial Engineering Program, Mercu Buana University, Indonesia.
Indonesia
hernadewita@mercubuana.ac.id


Akmal Yudha
Prawira Saputra
Master of Industrial Engineering Program, Mercu Buana University, Indonesia.
Indonesia
akmalyidhaps@gmail.com


Lien
Kusumah
Master of Industrial Engineering Program, Mercu Buana University, Indonesia.
Indonesia
lien.herliani@mercubuana.ac.id


Hermiyetti
ST
Faculty of Economic Social Science, Bakrie University, Indonesia.
Indonesia
hermiyetti99@gmail.com
Statistical process control
Process Capability
quality improvement
PokaYoke buzzer
[[1] 2017 plastics manufacturing survey results: U.S. manufacturing at alltime high, growth predicted, shifts in process popularity (n.d.). Retrieved May 15, 2019 from https://www.rayplastics.com/plasticsmanufacturingsurveyresults/##[2] Das, S., & Nanda, A. K. (2013). Some stochastic orders of dynamic additive mean residual life model. Journal of statistical planning and inference, 143(2), 400407.##[3] Motorcu, A. R., & Güllü, A. (2006). Statistical process control in machining, a case study for machine tool capability and process capability. Materials & design, 27(5), 364372.##[4] Baldassarre, T., Boffoli, N., Caivano, D., & Visaggio, G. (2004, April). Managing software process improvement (SPI) through statistical process control (SPC). International conference on product focused software process improvement (pp. 3046). Springer, Berlin, Heidelberg.##[5] Woodall, W. H. (2000). Controversies and contradictions in statistical process control. Journal of quality technology, 32(4), 341350.##[6] John, B., & Areshankar, A. (2018). Reduction of rework in bearing end plate using six sigma methodology: a case study. Journal of applied research on industrial engineering, 5(1), 1026.##[7] Jabnoun, N. (2002). Control processes for total quality management and quality assurance. Work study, 51(4), 182190.##[8] Xie, M., & Goh, T. N. (1999). Statistical techniques for quality. The TQM magazine, 11(4), 238242.##[9] Sanches Jr, L., & Batalha, G. F. (2008). Automotive bodyinwhite dimensional stability through precontrol application in the subassembly process. Journal of achievements in materials and manufacturing engineering, 31(2), 705711.##[10] Škulj, G., Butala, P., & Sluga, A. (2013). Statistical process control as a service: an industrial case study. Procedia CIRP, 7, 401406.##[11] Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons.##[12] Fournier, B., Rupin, N., Bigerelle, M., Najjar, D., & Iost, A. (2006). Application of the generalized lambda distributions in a statistical process control methodology. Journal of process control, 16(10), 10871098.##[13] Srikaeo, K., Furst, J. E., & Ashton, J. (2005). Characterization of wheatbased biscuit cooking process by statistical process control techniques. Food control, 16(4), 309317.##[14] Cook, D. F., Zobel, C. W., & Wolfe, M. L. (2006). Environmental statistical process control using an augmented neural network classification approach. European journal of operational research, 174(3), 16311642.##[15] Rungtusanatham, M. (2001). Beyond improved quality: the motivational effects of statistical process control. Journal of operations management, 19(6), 653673.##[16] Pearn, W. L., & Chen, K. S. (2002). Onesided capability indices C PU and C PL: decision making with sample information. International journal of quality & reliability management, 19(3), 221245.##[17] Opit, P. F., Samadhi, T. A., & Singal, Y. M. (2008). Penerapan Six Sigma Untuk Peningkatan Kualitas Produk Bimoli Classic (Studi Kasus: PT. Salim Ivomas Pratama–Bitung). J@ TI UNDIP: JURNAL TEKNIK INDUSTRI, 3(1), 17##[18] Cınar A. Study of process capability: An example and difficulties. Engineer and Machine 1987;28(329):31–2.##[19] Kapuria, T. K., Rahman, M., & Haldar, S. (2017). Root Cause Analysis and Productivity Improvement of An Apparel Industry In Bangladesh Through Kaizen Implementation. Journal of applied research on industrial engineering, 4(4), 227239.##[20] Shingo, S., & Dillon, A. P. (1989). A study of the Toyota production system: from an industrial engineering viewpoint. CRC Press.##]
1

Identification, mitigation of bottleneck by capacity addition and economic analysis for copper cable production process: a case study
http://www.journalaprie.com/article_89084.html
10.22105/jarie.2019.182889.1088
1
A bottleneck machine in a production line will reduce the productivity of the whole. The results of having a bottleneck are stalls in production, supply overstock, pressure from customers, and low employee morale. This paper focuses on the identification of the bottleneck in the production process of “Bangladesh cable silpa limited” and after the identification, tries to mitigate the bottleneck. A bottleneck machine or process causes starving or blocking of parts in the system, thus, therefore, increases the nonvalue added time and reduces the system performance. In this work, the bottleneck process is identified by using ARENA simulation based on the highest utilization rate and longest queue length matrices. Then, the bottleneck is reduced by increasing the capacity or the number of the machine in the bottleneck process. We can see the effect of changing the capacity of the process without changing the actual production line. Calculating it in a conventional way is very time consuming too. The last step of the thesis is to do an economic analysis. Because when the capacity of the process is increased, the production rate increases but the additional capacity increases the cost of the production.
0

97
107


Aurpon Tahsin
Shams
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna9203, Bangladesh.
Bangladesh
aurponshams@gmail.com


Kamrul
Hasan
Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna9203, Bangladesh.
Bangladesh
khasan@eee.buet.ac.bd
bottleneck
Capacity addition
Arena simulation
Costbenefit analysis
[[1] Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons.##[2] Lawrence, S. R., & Buss, A. H. (1994). Shifting production bottlenecks: causes, cures, and conundrums. Production and operations management, 3(1), 2137.##[3] Kuo, C. T., Lim, J. T., & Meerkov, S. M. (1996). Bottlenecks in serial production lines: a systemtheoretic approach. Mathematical problems in engineering, 2(3), 233276.##[4] Chiang, S. Y., Kuo, C. T., & Meerkov, S. M. (1998). Bottlenecks in Markovian production lines: a systems approach. IEEE transactions on robotics and automation, 14(2), 352359.##[5] Law, A. M. (1983). Statistical analysis of simulation output data. Operations research, 31(6), 9831029.##[6] Roser, C., & Nakano, M. (2015). A quantitative comparison of bottleneck detection methods in manufacturing systems with particular consideration for shifting bottlenecks. IFIP international conference on advances in production management systems (pp. 273281). Springer, Cham.##[7] Bernedixen, J., Pehrsson, L., Ng, A. H., & Antonsson, T. (2015). Simulationbased multiobjective bottleneck improvement: towards an automated toolset for industry. 2015 winter simulation conference (WSC) (pp. 21832194). IEEE.##[8] Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European journal of operational research, 183(2), 674693.##[9] Narayanasamy, P. (2010). Identification and mitigation of bottlenecks in complex production system (Doctoral dissertation, Wichita State University). Retrieved from https://soar.wichita.edu/handle/10057/3735##[10]Chiadamrong, N., & Limpasontipong, P. (2003). Using storage buffer to improve unbalanced asynchronous production flow line's performance. International journal of manufacturing technology and management, 5(12), 149161.##[11]Tamilselvan, P., Krishnan, K., & Cheraghi, H. (2010). Measurement of shifting bottlenecks in a nonbuffer production system. Proceedings of the 2010 industrial engineering research conference. Institute of Industrial and Systems Engineers (IISE).##[12]Papadopoulos, C. T., O'Kelly, M. E., Vidalis, M. J., & Spinellis, D. (2009). Analysis and design of discrete part production lines (p. 279). New York: Springer.##[13]Altuger, G., & Chassapis, C. (2009). Multi criteria preventive maintenance scheduling through arena based simulation modeling. Winter simulation conference (pp. 21232134). Austin, Texas: Winter Simulation Conference.##[14]Heshmat, M., ElSharief, M. A., & ElSebaie, M. G. (2013). Simulation Modeling of Automatic Production Lines with Intermediate Buffers. International journal of scientific & engineering research, 4(7), 25282535.##[15]Gopalakrishnan, M., Skoogh, A., & Laroque, C. (2016, December). Buffer utilization based scheduling of maintenance activities by a shifting priority approacha simulation study. 2016 winter simulation conference (WSC) (pp. 27972808). IEEE.##]
1

The extension analysis of natural gas network locationrouting design through the feasibility study
http://www.journalaprie.com/article_88471.html
10.22105/jarie.2019.174164.1082
1
From 2001 to the present, natural gas production in Indonesia dominates petroleum production. Most of the natural gas is used for export until 2013. From 2014 until now, most of the natural gas production is being utilized for domestic with an increasing trend. Domestic gas usage for households is still far below industry and commercial sectors. Domestic gas usage in the household can be done in two ways, namely city gas and Liquefied Petroleum Gas cylinder. The use of city gas is better in terms of price, mitigation, and gas emission. The government plans to build a new city gas network for 4,000 households. This study aim is to propose the design of city gas network, so the construction and operational costs become minimal. This research uses three stages, namely division of region by using the clustering algorithm, the gas network route determination using heuristics algorithm, and determination of the feasibility using the BenefitCost Ratio. We have successfully calculated the maximum region’s capacity by making use of Weymouth Formula. The iterative clustering algorithm is done to make sure the location of Distribution Centers is welldefined. We modified the distance measurement by preferring driving distance rather than Euclidean in the interest of precision. In the end, we also discuss the feasibility study of the project. Based on the calculation, we have obtained that the gas network development project is feasible to run.
0

108
124


Filscha
Nurprihatin
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
fnurprihatin@bundamulia.ac.id


Agnes
Octa
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
agnsoct9@yahoo.com


Tasya
Regina
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
tasyaregina441@gmail.com


Tony
Wijaya
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
tonywj99@gmail.com


Julliete
Luin
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
jullieteluin@gmail.com


Hendy
Tannady
Department of Industrial Engineering, Faculty of Design and Technology, Universitas Bunda Mulia, Jakarta, Indonesia.
Indonesia
htannady@bundamulia.ac.id
City gas network
Kmeans Clustering
Heuristics Algorithm
Feasibility study
Benefitcost Ratio
[[1] Ministry of Energy and Mineral Resources (2015). Strategic plan of the ministry of energy and mineral resources 20152019. Retrieved July 06, 2019 from http://www.apbiicma.org//uploads/files/old/2016/01/RenstraKESDM20152019.pdf##[2] Ministry of Energy and Mineral Resources. (2016). Handbook of energy and economic statistics of Indonesia 2016. Retrieved July 06, 2019 from https://www.esdm.go.id/assets/media/content/contenthandbookofenergyeconomicstatisticsofindonesia2016lvekpnc.pdf##[3] Ahmad, H. (2017). Impact of slashing oil prices on the natural gas market. In S. K. Kar., & A. Gupta (Eds.), Natural gas markets in India (pp. 3342). Springer, Singapore.##[4] Paliwal, P. (2017). Natural gas pricing. In S. K. Kar., & A. Gupta (Eds.), Natural gas markets in india: opportunities and challenges (pp. 7593). Springer, Singapore.##[5] Kar, S. K., Sinha, P. K., & Dholakia, B. (2017). Building and sustaining natural gas business in India. In S. K. Kar., & A. Gupta (Eds.), Natural gas markets in India: opportunities and challenges (pp.167196). Singapore: Springer Nature Singapore.##[6] Shanker, R., & Bhattacharya, M. (2017). Brain tumor segmentation of normal and pathological tissues using Kmean clustering with fuzzy Cmean clustering. In J. M. R. S. Tavares., & R. M. N. Jorge (Eds.), European congress on computational methods in applied sciences and engineering (pp. 286296). Springer, Cham.##[7] Jodas, D. S., Pereira, A. S., & Tavares, J. M. R. (2017). Automatic segmentation of the lumen in magnetic resonance images of the carotid artery. In J. M. R. S. Tavares., & R. M. N. Jorge (Eds.), European congress on computational methods in applied sciences and engineering (pp. 92101). Springer, Cham.##[8] Herrera, W. G., Cover, G. S., & Rittner, L. (2017). Pixelbased classification method for corpus callosum segmentation on diffusionMRI. In J. M. R. S. Tavares., & R. M. N. Jorge (Eds.), European congress on computational methods in applied sciences and engineering (pp. 217224). Springer, Cham.##[9] Li, C., Sun, L., Jia, J., Cai, Y., & Wang, X. (2016). Risk assessment of water pollution sources based on an integrated kmeans clustering and set pair analysis method in the region of Shiyan, China. Science of the total environment, 557, 307316.##[10]Tartavulea, R. I. (2015). Model for determining the optimum location for performance improvement in supplychain strategies. European journal of interdisciplinary studies, 7(1), 39.##[11]Liu, D., Hou, M., & Qu, H. (2013). A simple model for the multiple traveling salesmen problem with single depot and multiple sink. COMPELThe international journal for computation and mathematics in electrical and electronic engineering, 32(2), 556574.##[12]Lam, C. H., Choy, K. L., Ho, G. T., & Lee, C. K. M. (2014). An orderpicking operations system for managing the batching activities in a warehouse. International journal of systems science, 45(6), 12831295.##[13]Jeřábek, K., Majercak, P., Kliestik, T., & Valaskova, K. (2016). Application of clark and wright´ s savings algorithm model to solve routing problem in supply logistics [Special issue]. NAŠE MORE: znanstvenostručni časopis za more i pomorstvo, 63(3), 115119.##[14]Sathyanarayanan, S., Joseph, K. S., & Jayakumar, S. K. V. (2015). A hybrid population seeding technique based genetic algorithm for stochastic multiple depot vehicle routing problem. 2015 international conference on computing and communications technologies (ICCCT) (pp. 119127). IEEE.##[15]Pichpibul, T., & Kawtummachai, R. (2013). A heuristic approach based on clarkewright algorithm for open vehicle routing problem. The scientific world journal. http://dx.doi.org/10.1155/2013/874349##[16]Hashi, E. K., Hasan, M. R., & Zaman, M. S. U. (2015, November). A heuristic solution of the Vehicle Routing Problem to optimize the office bus routing and scheduling using Clarke & Wright's savings algorithm. 2015 international conference on computer and information engineering (ICCIE) (pp. 1316). IEEE.##[17]Charoenwong, C., & Pathomsiri, S. (2015, May). Vehicle routing for improving financial performance: A case study of a freight transportation service provider in Thailand. 4th international conference on advanced logistics and transport (ICALT) (pp. 263268). IEEE.##[18]Young, J. R., Suon, S., Rast, L., Nampanya, S., Windsor, P. A., & Bush, R. D. (2016). Benefit‐cost analysis of foot and mouth disease control in large ruminants in cambodia. Transboundary and emerging diseases, 63(5), 508522.##[19]Hausken, K. (2016). Cost benefit analysis of war. International journal of conflict management, 27(4), 454469.##[20]Rushton, A., Croucher, P., & Baker, P. (2014). The handbook of logistics and distribution management: Understanding the supply chain. Kogan Page Publishers.##[21]Meindl, S. C. P. (2016). Supply chain managementstrategy, planning and operation. Tsinghua University Press. wheat soybean others land for no use.##[22]Panagakos, G., Psaraftis, H. N., & Holte, E. A. (2015). Green corridors and their possible impact on the European supply chain. In C.Y. Lee., & Q. Meng (Eds). Handbook of ocean container transport logistics (pp. 521550). Springer, Cham.##[23]Amani, H., Kariminezhad, H., & Kazemzadeh, H. (2016). Development of natural gas flow rate in pipeline networks based on unsteady state Weymouth equation. Journal of natural gas science and engineering, 33, 427437.##[24]Aggarwal, C. C. (2015). Data mining: the textbook. Springer.##[25]Badiru, A. B. (2005). Cluster analysis: a tool for industrial engineers. In Handbook of industrial and systems engineering (pp. 393402). CRC Press.##[26]Andritsos, P., & Tsaparas, P. (2017). Categorical data clustering. In C. Sammut., & G. I. Webb (Eds.), Encyclopedia of machine learning and data mining (pp. 188193). Springer, Boston, MA##[27]Zafar, M. H., & Ilyas, M. (2015). A clustering based study of classification algorithms. International journal of database theory and application, 8(1), 1122.##[28]Jin, X., & Han, J. (2017). KMeans clustering. In C. Sammut., G. I. Webb (Eds.), Encyclopedia of machine learning and data mining (pp. 695697). Springer, Boston, MA.##[29]Diaby, M., & Karwan, M. H. (2016). Advances in combinatorial optimization. Singapore: World Scientific Publishing, 2016.##[30]Bramel, J., & SimchiLevi, D. (1997). The logic of logistics: theory, algorithms, and applications for logistics management (pp. 175240). New York: Springer.##[31]Nahmias, S., & Cheng, Y. (2005). Production and operations analysis (Vol. 6). New York: McGrawhill.##]
1

Contribution of quality tools for reducing food waste in university canteen
http://www.journalaprie.com/article_88473.html
10.22105/jarie.2019.177566.1086
1
The aim of this paper is to implement the quality tools for reducing food waste in Woldia University (WU) canteen (Ethiopia). Universities are one of the main sources of food waste among food catering industries. In WU canteen, it is observed that most of the generated food wastes are goes to landfill. This has a direct impact on the environment. This paper proposes a methodology to tackle this problem. First, food items which contribute by 80% of food waste in the university are identified with the help of Pareto chart. Then the cause and effect diagram is used to identify the main causes or reason for these food items to be wasted. Finally, Quality Function Deployment (QFD) is implemented for reducing food waste through customer satisfaction. This reduces significant portion of food waste as the food waste in the canteen is directly related to customer satisfaction.
0

125
130


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


Andarge
Ayele
School of Mechanical and Automotive Engineering, College of Engineering, Dilla University, Dilla, Ethiopia.
Ethiopia
ayeleandarge1@gmail.com
University canteen
Quality tools
Food waste minimization
[[1] Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R., & Meybeck, A. (2011). Global food losses and food waste (pp. 138). Rome: FAO.##[2] Falasconi, L., Vittuari, M., Politano, A., & Segrè, A. (2015). Food waste in school catering: an Italian case study. Sustainability, 7(11), 1474514760.##[3] Modelling of milestones for achieving resource efficiency, turning milestones into quantified objectives: food waste. (2013). Final report for the European commission (DG Environment). Retrieved July 11, 2019 from http://ec.europa.eu/environment/enveco/resource_efficiency/pdf/Task%203%20report.pdf##[4] Environmental Impact of Products (EIPRO), Analysis of the life cycle environmental impacts related to the final consumption of the EU25. (2006). European commission. Retrieved July 11, 2019 from http://ec.europa.eu/environment/ipp/pdf/eipro_report.pdf##[5] Sutton, M. A., Bleeker, A., Howard, C. M., Erisman, J. W., Abrol, Y. P., Bekunda, M., ... & Zhang, F. S. (2013). Our nutrient world. The challenge to produce more food & energy with less pollution. Centre for Ecology & Hydrology.##[6] Quested, T., Ingle, R., & Parry, A. (2013). Household food and drink waste in the United Kingdom 2012. WRAP, London.##[7] Gustavsson, J., Cederberg, C., Sonesson, U., van Otterdijk, R., & Meybeck, A. (2011). Global food losses and food waste: extent, causes and prevention. FAO, Rome.##[8] Pinto, R. S., dos Santos Pinto, R. M., Melo, F. F. S., Campos, S. S., & Cordovil, C. M. D. S. (2018). A simple awareness campaign to promote food waste reduction in a University canteen. Waste management, 76, 2838.##[9] Saputri, E. M., Rojroongwasinkul, N., & Tangsuphoom, N. (2018). Effect of food serving style on quantity and composition of food waste generated from university canteens: a study at Mulawarman University, Indonesia. 3rd international conference of integrated intellectual community. Hanover.##[10]Lagorio, A., Pinto, R., & Golini, R. (2018). Food waste reduction in school canteens: Evidence from an Italian case. Journal of cleaner production, 199, 7784.##[11]Derqui, B., Fernandez, V., & Fayos, T. (2018). Towards more sustainable food systems. Addressing food waste at school canteens. Appetite, 129, 111.##[12]Juran, J., & Godfrey, A. B. (1999). Quality handbook. Republished McGrawHill, 173178.##[13]Cetinkaya, C., Kenger, O. N., Kenger, Z. D., & Ozceylan, E. (2019). Quality function deployment implementation on educational curriculum of industrial engineering in university of gaziantep. Industrial Engineering in the big data era (pp. 6778). Springer, Cham.##]
1

An efficient nonlinear programming method for eliciting preference weights of incomplete comparisons
http://www.journalaprie.com/article_88474.html
10.22105/jarie.2019.169901.1078
1
The Analytic Hierarchy Process (AHP) which was developed by Saaty is a decision analysis tool. It has been applied to many different decision fields. Acquiring Pairwise Comparison Matrices (PCM) is the main step in AHP and also is frequently used in other multicriteria decisionmaking methods. In a real problem when the number of alternatives/criteria to be compared is increased, the number of Pairwise Comparisons (PC) often becomes overwhelming. Since the Decision Maker’s (DM) performance in representing the relative preferences tends to deteriorate in such cases, it is preferred to gather fewer data from each individual DM in the form of pairwise comparisons. Missing values in Pairwise Comparison Matrices (PCM) in AHP is a spreading problem in areas dealing with great or dynamic data. The aim of this paper is to present an efficient mathematical programming model for estimating preference vector of pairwise comparison matrices with missing entries.
0

131
138


Mohamad
Movafaghpour
Department of Industrial Engineering, Facuty of Mechanical Engineering, JundiShapur University of Technology, Dezful, Iran.
Iran
movafaghpour@jsu.ac.ir
Incomplete Pairwise Comparisons
Analytic Hierarchy Process
Nonlinear Programming
Optimum Solution
Dynamic Data
[[1] Alonso, S., Chiclana, F., Herrera, F., Herrera‐Viedma, E., Alcalá‐Fdez, J., & Porcel, C. (2008). A consistency‐based procedure to estimate missing pairwise preference values. International journal of intelligent systems, 23(2), 155175.##[2] Benítez, J., Carrión, L., Izquierdo, J., & PérezGarcía, R. (2014). Characterization of consistent completion of reciprocal comparison matrices. Abstract and Applied Analysis. http://dx.doi.org/10.1155/2014/349729##[3] Bozóki, S., & Fülöp, J. (2018). Efficient weight vectors from pairwise comparison matrices. European journal of operational research, 264(2), 419427.##[4] Bozóki, S., Fülöp, J., & Koczkodaj, W. W. (2011). An LPbased inconsistency monitoring of pairwise comparison matrices. Mathematical and computer modelling, 54(12), 789793.##[5] Bozóki, S., FüLöP, J., & RóNyai, L. (2010). On optimal completion of incomplete pairwise comparison matrices. Mathematical and computer modelling, 52(12), 318333.##[6] Chen, K., Kou, G., Tarn, J. M., & Song, Y. (2015). Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices. Annals of operations research, 235(1), 155175.##[7] Chen, Q., & Triantaphyllou, E. (2001). Estimating data for multicriteria decision making problems: optimization techniques. Encyclopedia of optimization, 567576.##[8] Chiclana, F., HerreraViedma, E., Alonso, S., & Herrera, F. (2008). A note on the estimation of missing pairwise preference values: a uninorm consistency based method. International journal of uncertainty, fuzziness and knowledgebased systems, 16(supp02), 1932.##[9] Choo, E. U., & Wedley, W. C. (2004). A common framework for deriving preference values from pairwise comparison matrices. Computers & operations research, 31(6), 893908.##[10]Ergu, D., Kou, G., Peng, Y., & Zhang, M. (2016). Estimating the missing values for the incomplete decision matrix and consistency optimization in emergency management. Applied mathematical modelling, 40(1), 254267.##[11]Fedrizzi, M., & Giove, S. (2007). Incomplete pairwise comparison and consistency optimization. European journal of operational research, 183(1), 303313.##[12]Harker, P. T. (1987). Alternative modes of questioning in the analytic hierarchy process. Mathematical modelling, 9(35), 353360.##[13]Harker, P. T. (1987). Incomplete pairwise comparisons in the analytic hierarchy process. Mathematical modelling, 9(11), 837848.##[14]Kou, G., Ergu, D., Lin, C., & Chen, Y. (2016). Pairwise comparison matrix in multiple criteria decision making. Technological and economic development of economy, 22(5), 738765.##[15]Kwiesielewicz, M. (1996). The logarithmic least squares and the generalized pseudoinverse in estimating ratios. European journal of operational research, 93(3), 611619.##[16]Oliva, G., Setola, R., & Scala, A. (2017). Sparse and distributed analytic hierarchy process. Automatica, 85, 211220.##[17]Saaty, T. L., & Vargas, L. G. (2012). Models, methods, concepts & applications of the analytic hierarchy process (Vol. 175). Springer Science & Business Media.##[18]Shiraishi, S., Obata, T., & Daigo, M. (1998). Properties of a positive reciprocal matrix and their application to AHP. Journal of the operations research society of japan, 41(3), 404414.##[19]Siraj, S., Mikhailov, L., & Keane, J. A. (2012). Enumerating all spanning trees for pairwise comparisons. Computers & operations research, 39(2), 191199.##[20]Wedley, W. C. (1993). Consistency prediction for incomplete AHP matrices. Mathematical and computer modelling, 17(45), 151161.##[21]Xu, Y., Patnayakuni, R., & Wang, H. (2013). Logarithmic least squares method to priority for group decision making with incomplete fuzzy preference relations. Applied mathematical modelling, 37(4), 21392152.##]
1

A new approach to solve fully fuzzy linear programming problem
http://www.journalaprie.com/article_89684.html
10.22105/jarie.2019.183391.1090
1
Today, human decisions are more than ever based on information. But most of this information is not definitive, and in this situation, logical decision making is very difficult based on this uncertainty. Different methods are used to represent this uncertainty, including the fuzzy numbers. The fuzzy linear programming problem is one of the interesting concepts to be addressed in fuzzy optimization. Fully Fuzzy Linear Programming Problems (FFLP) are issues in which all parameters of the coefficients of the variables in the target functions, the coefficients of the variables in the constraints, the righthand side of the constraints, and the decision variables in them are fuzzy. In this paper, we show that Definition 2.6 which is used by Ezzati et al. [1], failed to compare any arbitrary triangular fuzzy numbers. We demonstrate that their presented method is not well in general, thus the proposed method finds the fuzzy optimal solution of fully fuzzy linear programming problems by Ezzati et al. [1]. Then a new approach is proposed for solving this FFLP problem. An example is also presented to demonstrate the new method.
0

139
149


Faranak
Mahmoudi
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Iran
m.mahmoudi8690@yahoo.com


Seyed Hadi
Nasseri
Department of Mathematics, University of Mazandaran, Babolsar, Iran.
Iran
nhadi57@gmail.com
fully fuzzy linear programming
triangular fuzzy numbers
ranking function
Fuzzy number
[[1] Ezzati, R., Khorram, E., & Enayati, R. (2015). A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem. Applied mathematical modelling, 39(12), 31833193.##[2] Tanaka, H., Okuda, T., & Asai, K. (1974). On fuzzy mathematical programming. Jornal of cybern. 3(4), 37–46.##[3] Bellman, R. E., & Zadeh, L. A. (1970). Decisionmaking in a fuzzy environment. Management science, 17(4), B141.##[4] Bector, C. R., & Chandra, S. (2005). Fuzzy mathematical programming and fuzzy matrix games (Vol. 169). Berlin: Springer.##[5] Lotfi, F. H., Allahviranloo, T., Jondabeh, M. A., & Alizadeh, L. (2009). Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution. Applied mathematical modelling, 33(7), 31513156.## [6] Kumar, A., Kaur, J., & Singh, P. (2011). A new method for solving fully fuzzy linear programming problems. Applied mathematical modelling, 35(2), 817823.##[7] Najafi, H. S., & Edalatpanah, S. A. (2013). A note on “A new method for solving fully fuzzy linear programming problems”. Applied mathematical modelling, 37(1415), 78657867.##[8] Bhardwaj, B., & Kumar, A. (2015). A note on “A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem”. Applied mathematical modelling, 39(19), 59825985.##[9] Nasseri, S. H., & MahdaviAmiri, N. (2009). Some duality results on linear programming problems with symmetric fuzzy numbers. Fuzzy information and engineering, 1(1), 5966.##]
1

Development of an automated obstacle detector for blind people
http://www.journalaprie.com/article_88472.html
10.22105/jarie.2019.176040.1084
1
Eyes play a vital role in our life. Usually, all of us have seen the visually impaired people and know the problems that they face in their daily life. In order to detect the obstacles, blind people use sticks when they are walking but this instrument just can help them find objects on the ground. Obstacle detection is a field of effort that has led to vast progress in primary safety systems and in the primarysecondary safety systems interaction. Obstacle detector for the blind is an automated device for blind people. The main objective of this device is to make easy the walking environment for blind people. In this paper, a device is made which help the blind people by assisting through an android application. First, the device is made and then its performance is tested in three demanding conditions, for instance, normal, windy, and rainy conditions. The device shows a satisfactory level of accuracy in the three conditions. It helps the user to hear the distance and the location in an automated human voice.
0

150
160


Chandan
Debnath
Department of Computer Science and Engineering, Daffodil Institute of IT, Dhaka, Bangladesh.
Bangladesh
chandandebnath523@gmail.com
Visually impaired people
obstacle detector
sonar sensor
Arduino
Android application
location tracking
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