Phân tích dự đoán rác thải điện tử cho các hoạt động logistics ngược: so sánh các mô hình xám đơn biến được cải tiến

Soft Computing - Tập 24 - Trang 15747-15762 - 2020
Gazi Murat Duman1, Elif Kongar2, Surendra M. Gupta3
1Trefz School of Business, University of Bridgeport, Bridgeport, USA
2Departments of Mechanical Engineering and Technology Management, University of Bridgeport, Bridgeport, USA
3Department of Mechanical and Industrial Engineering, Northeastern University, Boston, USA

Tóm tắt

Tốc độ đổi mới và nhu cầu tiêu dùng đang gia tăng dẫn đến việc tích lũy nhanh chóng chất thải thiết bị điện và điện tử hay còn gọi là rác thải điện tử (e-waste). Để xây dựng và duy trì các thành phố xanh, việc quản lý e-waste hiệu quả trở thành một phản ứng khả thi trước sự tích lũy này. Những dự đoán chính xác về e-waste mà các thành phố có thể sử dụng để xây dựng hạ tầng logistics ngược phù hợp trở nên ngày càng quan trọng khi việc thu gom, tái chế và xử lý e-waste trở nên phức tạp và khó lường hơn. Nguyên lý của nó cũng cho thấy rằng tài liệu liên quan trình bày nhiều phương pháp khác nhau tập trung vào dự báo phát sinh e-waste. Trong số các phương pháp này, phương pháp mô hình xám đã thu hút được sự quan tâm do khả năng trình bày kết quả có ý nghĩa với dữ liệu có kích thước nhỏ hoặc hạn chế. Để cải thiện tỷ lệ thành công tổng thể của phương pháp này, nhiều kỹ thuật dự báo dựa trên mô hình xám đã được đề xuất trong những năm qua. Tuy nhiên, hiệu suất của các mô hình này phụ thuộc sâu sắc vào các thông số được sử dụng mà không có sự đồng thuận established nào về tiêu chí phù hợp cho độ chính xác tốt hơn. Để giải quyết vấn đề này và cung cấp một hướng dẫn cho các học giả và thực hành, bài báo này trình bày một phân tích so sánh các phương pháp mô hình xám được sử dụng phổ biến nhất trong tài liệu được cải tiến bởi tối ưu hóa đàn đàn bướm. Một nghiên cứu trường hợp sử dụng dữ liệu e-waste từ Bang Washington được cung cấp để chứng minh phân tích so sánh được đề xuất trong nghiên cứu.

Từ khóa

#rác thải điện tử #mô hình xám #logistics ngược #tối ưu hóa đàn bướm

Tài liệu tham khảo

Akay D, Atak M (2007) Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy 32:1670–1675. https://doi.org/10.1016/j.energy.2006.11.014 Albuquerque C, Mello C, Paes V, Balestrassi P, Souza L (2019) Electronic junk: best practice of recycling and production forecast case study in Brazil. In: Mula J, Barbastefano R, Díaz-Madroñero M, Poler R (eds) New global perspectives on industrial engineering and management. Springer, Cham, pp 127–134 Ayvaz B, Bolturk E, Kaçtıoğlu S (2014) A grey system for the forecasting of return product quantity in recycling network. Int J Supply Chain Manag 3:105–112 Brunner PH, Rechberger H (2016) Handbook of material flow analysis: For environmental, resource, and waste engineers. CRC Press, Boca Raton Chang S-C, Lai H-C, Yu H-C (2005) A variable P value rolling grey forecasting model for Taiwan semiconductor industry production. Technol Forecast Soc Change 72:623–640. https://doi.org/10.1016/j.techfore.2003.09.002 Chen CI (2008) Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate. Chaos Solitons Fractals 37:278–287. https://doi.org/10.1016/j.chaos.2006.08.024 Chen PY, Yu H-M (2014) Foundation settlement prediction based on a novel NGM model. Math Probl Eng 2014:1–8 Chen CI, Chen HL, Chen S-P (2008) Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1,1). Commun Nonlinear Sci Numer Simul 13:1194–1204. https://doi.org/10.1016/j.cnsns.2006.08.008 Chen CI, Hsin P-H, Wu C-S (2010) Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model. Expert Syst Appl 37:7557–7562. https://doi.org/10.1016/j.eswa.2010.04.088 Cui J, Dang Y, Liu S (2009) Novel grey forecasting model and its modeling mechanism. Control Decis 24:1702–1706 data.gov (2018) Electronics recycling. data.wa.gov. https://catalog.data.gov/dataset/electronics-recycling-2014. Accessed 9/26/2018 2018 Deng JL (1989) Introduction to grey system theory. J Grey Syst 1:1–24 Duan H, Lei GR, Shao K (2018) Forecasting crude oil consumption in china using a grey prediction model with an optimal fractional-order accumulating operator. Complexity 2018:1–12 Duman GM, Kongar E, Gupta SM (2019) Estimation of electronic waste using optimized multivariate grey models. Waste Manag 95:241–249 Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, 4–6 Oct 1995, pp 39–43. https://doi.org/10.1109/mhs.1995.494215 ecology.wa.gov (2018) Electronics recycling. ecology.wa.gov. https://ecology.wa.gov/Regulations-Permits/Plans-policies/Washington-state-waste-plan/Progress-report/Electronics-recycling. Accessed 09.26.2018 Elsayed A, Kongar E, Gupta SM (2012) An evolutionary algorithm for selective disassembly of end-of-life products. Int J Swarm Intell Evol Comput 1:1–7 Ene S, Öztürk N (2017) Grey modelling based forecasting system for return flow of end-of-life vehicles. Technol Forecast Soc Change 115:155–166. https://doi.org/10.1016/j.techfore.2016.09.030 EPA (2007) Management of electronic waste in the United States approach two. U.S. Environmental Protection Agency. https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100BC9O.TXT. Accessed 9.18.2018 EPA (2008) Electronics waste management in the United States: approach I. U.S. Environmental Protection Agency. https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P1001FPK.TXT. Accessed 9.18.2018 Gupta SM (2016) Reverse supply chains: issues and analysis. CRC Press, Boca Raton Hsu L-C (2009) Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert Syst Appl 36:7898–7903. https://doi.org/10.1016/j.eswa.2008.11.004 Hsu L-C (2010) A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry. Expert Syst Appl 37:4318–4323. https://doi.org/10.1016/j.eswa.2009.11.068 Hu Y-C (2019) A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems. Soft Comput. https://doi.org/10.1007/s00500-019-04191-0 Intharathirat R, Salam PA, Kumar S, Untong A (2015) Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Manag 39:3–14 Jain A, Sareen R (2006) E-waste assessment methodology and validation in India. J Mater Cycles Waste Manag 8:40–45. https://doi.org/10.1007/s10163-005-0145-2 Kayacan E, Ulutas B, Kaynak O (2010) Grey system theory-based models in time series prediction. Expert Syst Appl 37:1784–1789. https://doi.org/10.1016/j.eswa.2009.07.064 Kordnoori S, Mostafaei H, Kordnoori S (2014) The application of Fourier residual grey Verhulst and grey Markov model in analyzing the global ICT development. Hyperion Econ J 2:50–60 Lau WK-Y, Chung S-S, Zhang C (2013) A material flow analysis on current electrical and electronic waste disposal from Hong Kong households. Waste Manag 33:714–721. https://doi.org/10.1016/j.wasman.2012.09.007 Li K, Liu L, Zhai J, Khoshgoftaar TM, Li T (2016) The improved grey model based on particle swarm optimization algorithm for time series prediction. Eng Appl Artif Intell 55:285–291. https://doi.org/10.1016/j.engappai.2016.07.005 Li S, Meng W, Xie Y (2017) Forecasting the amount of waste-sewage water discharged into the Yangtze river basin based on the optimal fractional order grey model. Int J Environ Res Public Health 15:20 Liu G, Yu J (2007) Gray correlation analysis and prediction models of living refuse generation in Shanghai city. Waste Manag 27:345–351. https://doi.org/10.1016/j.wasman.2006.03.010 Liu L, Wang Q, Liu M, Li L (2014) An intelligence optimized rolling grey forecasting model fitting to small economic dataset. Abstract Appl Anal. https://doi.org/10.1155/2014/641514 Liu L, Wang Q, Wang J, Liu M (2016) A rolling grey model optimized by particle swarm optimization in economic prediction. Comput Intell 32:391–419 Ma X, Liu Z (2017a) Application of a novel time-delayed polynomial grey model to predict the natural gas consumption in China. J Comput Appl Math 324:17–24. https://doi.org/10.1016/j.cam.2017.04.020 Ma X, Liu Z (2017b) The GMC (1, n) model with optimized parameters and its application. J Grey Syst 29:122–138 Matthews HS, McMichael FC, Hendrickson CT, Hart DJ (1997) Disposition and end-of-life options for personal computers. Green design initiative technical report, Carnegie Mellon University Oguchi M, Kameya T, Yagi S, Urano K (2008) Product flow analysis of various consumer durables in Japan. Resour Conserv Recycl 52:463–480. https://doi.org/10.1016/j.resconrec.2007.06.001 Pao H-T, Fu H-C, Tseng C-L (2012) Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy 40:400–409. https://doi.org/10.1016/j.energy.2012.01.037 Petridis NE, Stiakakis E, Petridis K, Dey P (2016) Estimation of computer waste quantities using forecasting techniques. J Clean Prod 112:3072–3085. https://doi.org/10.1016/j.jclepro.2015.09.119 Shili F, Lifeng W, Liang Y, Zhigeng F (2013) Using fractional GM (1, 1) model to predict maintenance cost of weapon system. In: 2013 IEEE international conference on grey systems and intelligent services. IEEE, pp 177–181 Srivastava AK, Nema AK (2006) Grey modelling of solid waste volumes in developing countries. In: Proceedings of the institution of civil engineers-waste and resource management, 2006, vol 4. Thomas Telford Ltd, pp 145–150 Steubing B, Böni H, Schluep M, Silva U, Ludwig C (2010) Assessing computer waste generation in Chile using material flow analysis. Waste Manag 30:473–482. https://doi.org/10.1016/j.wasman.2009.09.007 Wang ZX (2013) An optimized Nash nonlinear grey Bernoulli model for forecasting the main economic indices of high technology enterprises in China. Comput Ind Eng 64:780–787. https://doi.org/10.1016/j.cie.2012.12.010 Wang CH, Hsu L-C (2008) Using genetic algorithms grey theory to forecast high technology industrial output. Appl Math Comput 195:256–263 Wang Z-X, Li Q (2019) Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model. J Clean Prod 207:214–224 Wang FX, Zhang L-s (2009) Combination gray forecast model based on the ant colony algorithm. Math Pract Theory 14:017 Wang ZX, Dang Y, Liu S (2009) Unbiased grey Verhulst model and its application. Syst Eng Theory Pract 29:138–144 Wang ZX, Hipel KW, Wang Q, He S-W (2011) An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China. Appl Math Model 35:5524–5532. https://doi.org/10.1016/j.apm.2011.05.022 Wang Z-X, Li D-D, Zheng H-H (2019) Model comparison of GM (1, 1) and DGM (1, 1) based on Monte-Carlo simulation. Phys A 542:123341 Wu L, Zhao H (2019) Discrete grey model with the weighted accumulation. Soft Comput. https://doi.org/10.1007/s00500-019-03845-3 Wu L, Liu S, Yao L, Yan S, Liu D (2013) Grey system model with the fractional order accumulation. Commun Nonlinear Sci Numer Simul 18:1775–1785 Wu L, Liu S, Fang Z, Xu H (2015a) Properties of the GM (1, 1) with fractional order accumulation. Appl Math Comput 252:287–293 Wu L, Liu S, Yao L, Xu R, Lei X (2015b) Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model. Soft Comput 19:483–488. https://doi.org/10.1007/s00500-014-1268-y Xia T, Jin X, Xi L, Zhang Y, Ni J (2015) Operating load based real-time rolling grey forecasting for machine health prognosis in dynamic maintenance schedule. J Intell Manuf 26:269–280. https://doi.org/10.1007/s10845-013-0780-8 Xie Y, Li M (2009) Research on gray prediction modeling optimized by genetic algorithm for energy consumption demand. In: International conference on industrial mechatronics and automation, 2009. ICIMA 2009. IEEE, pp 289–291 Xie N, Liu S (2005a) Discrete GM (1, 1) and mechanism of grey forecasting model. Syst Eng Theory Pract 1:93–99 Xie N, Liu S (2005b) Research on discrete grey model and its mechanism. In: 2005 IEEE international conference on systems, man and cybernetics, vol 601, 12–12 Oct 2005, pp 606–610. https://doi.org/10.1109/icsmc.2005.1571213 Xie N, Liu S (2009) Discrete grey forecasting model and its optimization. Appl Math Model 33:1173–1186. https://doi.org/10.1016/j.apm.2008.01.011 Xie Q, Xie Y (2009) forecast of regional gross national product based on grey modelling optimized by genetic algorithm. In: International conference on e-learning, e-business, enterprise information systems, and e-government, 2009. EEEE’09. IEEE, pp 3–5 Xu L, Gao P, Cui S, Liu C (2013) A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Manag 33:1324–1331. https://doi.org/10.1016/j.wasman.2013.02.012 Yang Y, Williams E (2009) Logistic model-based forecast of sales and generation of obsolete computers in the US. Technol Forecast Soc Change 76:1105–1114. https://doi.org/10.1016/j.techfore.2009.03.004 Yao T, Forrest J, Gong Z (2012) Generalized discrete GM (1, 1) model. Grey Syst Theory Appl 2:4–12 Ye J, Dang Y, Yang Y (2019) Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation. Soft Comput. https://doi.org/10.1007/s00500-019-04276-w Zeng B, Li C (2016) Forecasting the natural gas demand in China using a self-adapting intelligent grey model. Energy 112:810–825. https://doi.org/10.1016/j.energy.2016.06.090 Zeng B, Li C (2018) Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application. Comput Ind Eng 118:278–290 Zeng B, Liu S, Xie N (2010) Prediction model of interval grey number based on DGM (1, 1). J Syst Eng Electron 21:598–603. https://doi.org/10.3969/j.issn.1004-4132.2010.04.011 Zeng B, Luo C, Liu S, Li C (2016a) A novel multi-variable grey forecasting model and its application in forecasting the amount of motor vehicles in Beijing. Comput Ind Eng 101:479–489. https://doi.org/10.1016/j.cie.2016.10.009 Zeng B, Meng W, Tong M (2016b) A self-adaptive intelligence grey predictive model with alterable structure and its application. Eng Appl Artif Intell 50:236–244. https://doi.org/10.1016/j.engappai.2015.12.011 Zhang F, Liu F, Zhao W, Sun Z, Jiang G (2003) Application of grey Verhulst model in middle and long term load forecasting. Power Syst Technol 5:37–40 Zhang L, Zheng Y, Wang K, Zhang X, Zheng Y (2014) An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China. Comput Biol Med 49:67–73. https://doi.org/10.1016/j.compbiomed.2014.02.008 Zhao H, Guo S (2016) An optimized grey model for annual power load forecasting. Energy 107:272–286. https://doi.org/10.1016/j.energy.2016.04.009 Zhao H, Zhao H, Guo S (2016a) Using GM (1, 1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner mongolia. Appl Sci 6:20 Zhao M, Zhao C, Yu L, Li G, Huang J, Zhu H, He W (2016b) Prediction and analysis of WEEE in China based on the gray model. Procedia Environ Sci 31:925–934 Zhou W, Pei L (2019) The grey generalized Verhulst model and its application for forecasting Chinese pig price index. Soft Comput. https://doi.org/10.1007/s00500-019-04248-0 Zhou J, Fang R, Li Y, Zhang Y, Peng B (2009) Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization. Appl Math Comput 207:292–299. https://doi.org/10.1016/j.amc.2008.10.045