Solving Disassembly Line Balancing Problems with Fuzzy Parameters Using an Artificial Intelligence Technique
Article Sidebar
Main Article Content
บทคัดย่อ
End-of-life (EOL) product recovery has become a critical issue driven by economic, social, and environmental concerns, along with stricter environmental regulations that emphasize disassembly and product recovery. Disassembly lines are used to dismantle EOL products into reusable components, but their efficiency depends heavily on accurately estimating task times, which are often uncertain. Since average task times cannot fully represent this uncertainty, task time can instead be modeled as a fuzzy number, allowing fuzzy logic to quantify representative values. This study introduces a disassembly line balancing problem where processing times are expressed as fuzzy numbers and solved using particle swarm optimization (PSO). The optimization aims to minimize the number of workstations, total idle time, maximum disassembly cost, and direction changes. The proposed method was benchmarked against LINGO and GUROBI solvers. Computational experiments, using the number of non-inferior solutions as a stopping criterion, demonstrated that the PSO-based approach achieved superior results. The findings indicate that the proposed method effectively outperforms existing algorithms, providing efficient and promising solutions for EOL product recovery and disassembly optimization.
Article Details
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เอกสารอ้างอิง
Dani ATR, Ratnasari V, Budiantara IN. Optimal Knots Point and Bandwidth Selection in Modeling Mixed Estimator Nonparametric Regression. IOP Conf Ser Mater Sci Eng. 2021;1115(1):012020.
Nurcahyani H, Budiantara IN, Zain I. Nonparametric Truncated Spline Regression on Modelling. AIP Conf Proc. 2023;020073(May).
Sriliana I, Budiantara IN, Ratnasari V. A Truncated Spline and Local Linear Mixed Estimator in Nonparametric Regression for Longitudinal Data and Its Application. Symmetry. 2022;14(12).
Dani ATR, Putra FB, Ni’matuzzaroh L, Ratnasari V, Budiantara IN. BTS Buku Tentang Statistika Regresi parametrik dan non Parametrik Teori dan Aplikasi dengan Software R. Jawa Barat: Perkumpulan Rumah Cemerlang Indonesia; 2024.
Chu CK, Marron JS. Choosing a Kernel Regression Estimator. Statistical Science. 1991;6(4):404–436.
Mariati NPAM, Budiantara IN, Ratnasari V. Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression. J Math. 2020;2020.
Cattaneo MD, Jansson M, Ma X. Simple Local Polynomial Density Estimators. J Am Stat Assoc. 2020;115(531):1449–55.
Bilodeau M. Fourier smoother and additive models. Can J Stat. 1992;20(3):257–69.
Amato U, Antoniadis A, De Feis I, Gijbels I. Wavelet-based robust estimation and variable selection in nonparametric additive models. Stat Comput. 2022;32(1).
Ampulembang AP, Otok BW, Rumiati AT, Budiasih. Bi-responses nonparametric regression model using MARS and its properties. Appl Math Sci. 2015;9(29–32):1417–27.
Tripena A, Prabowo A, Lianawati Y, Bon AT. Estimated spline in nonparametric regression with a generalized cross validation and unbiased risk approach. Proc Int Conf Ind Eng Oper Manag. 2021:3788–98.
Chamidah N, Zaman B, Muniroh L, Lestari B. Designing local standard growth charts of children in East Java province using a local linear estimator. Int J Innov Creat Change. 2020;13(1):45–67.
Eslami MM. Spline and Wavelet Smoothing Techniques for Functional Data. 2023:36–41.
Adrianingsih NY, Budiantara IN, Purnomo JDT. Modeling with Mixed Kernel, Spline Truncated and Fourier Series on Human Development Index in East Java. IOP Conf Ser Mater Sci Eng. 2021;1115(1):012024.
Widyastuti DA, Fernandes AAR, Pramoedyo H. Spline estimation method in nonparametric regression using truncated spline approach. J Phys Conf Ser. 2021;1872(1):012027.
Devi AR, Pratama RFW, Suparti. Comparison of generalized cross validation and unbiased risk method for selecting optimal knot in spline truncated. J Phys Conf Ser. 2019;1217(1):012094.
Maharani M, Saputro DRS. Generalized Cross Validation (GCV) in Smoothing Spline Nonparametric Regression Models. IOP Conf Ser Earth Environ Sci. 2021;1808(1):012053.
Saputro DRS, Demu KR, Widyaningsih P. Nonparametric truncated spline regression model on the data of human development index (HDI) in Indonesia. J Phys Conf Ser. 2018;1028(1):012219.
Chamidah N, Lestari B, Massaid A, Saifudin T. Estimating mean arterial pressure affected by stress scores using spline nonparametric regression model approach. Commun Math Biol Neurosci. 2020;2020:1–12.
Sriliana I, Budiantara IN, Ratnasari V. The performance of mixed truncated spline-local linear nonparametric regression model for longitudinal data. MethodsX. 2024;12(March).
Budiantara IN, Ratna M, Zain I, Wibowo W. Modeling the Percentage of Poor People in Indonesia Using Spline Nonparametric Regression Approach. Int J Basic Appl Sci IJBAS-IJENS. 2012;12(6):119–24.
Setiawan RNS, Budiantara IN, Ratnasari V. Application of Confidence Intervals for Parameters of Nonparametric Spline Truncated Regression on Index Development Gender in East Java. IPTEK J Sci. 2017;2(3):49–55.
Nidhomuddin, Chamidah N, Kurniawan A. Confidence Interval of the Parameter on Multipredictor Biresponse Longitudinal Data Analysis Using Local Linear Estimator for Modeling of Case Increase and Case Fatality Rates Covid-19 in Indonesia: a Theoretical Discussion. Commun Math Biol Neurosci. 2022;2022:1–12.
Lestari B, Chamidah N, Budiantara IN, Aydin D. Determining confidence interval and asymptotic distribution for parameters of multiresponse semiparametric regression model using smoothing spline estimator. J King Saud Univ Sci. 2023;35(5):1–7.
Budiantara IN, Ratnasari V, Permatasari EO, Prawanti DD. Shortest confidence interval of parameter semi parametric regression model using spline truncated for longitudinal data. AIP Conf Proc. 2019;2194.
Mao W, Zhao LH. Free-knot polynomial splines with confidence intervals. J R Stat Soc B Stat Methodol. 2003;65(4):901–19.
Suprapto. Interval Estimation for Indonesia Democracy Index (IDI) Model Using Multivariables Spline Truncated. Int J Sci Eng Res. 2018;9(9):1740–50.
Setyawati M, Chamidah N, Kurniawan A. Confidence Interval of Parameters in Multiresponse Multipredictor Semiparametric Regression Model for Longitudinal Data Based on Truncated Spline Estimator. Commun Math Biol Neurosci. 2022;2022:1–18.
Lestari B, Chamidah N, Budiantara IN, Aydin D. Determining confidence interval and asymptotic distribution for parameters of multiresponse semiparametric regression model using smoothing spline estimator. J King Saud Univ Sci. 2023;35(5):102664.
Eubank RL. Nonparametric Regression and Spline Smoothing. New York: CRC Press; 1999.
Hardle W. Applied Nonparametric Regression. 1994.
Wahba G. Spline Models for Observational Data. Pennsylvania: SIAM; 1990.
Ratnasari V, Budiantara IN, Andrea TRD. Nonparametric Regression Mixed Estimators of Truncated Spline and Gaussian Kernel based on Cross-Validation (CV), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) Methods. Int J Adv Sci Eng Inf Technol. 2021;11(6):2400–6.
Nidhomuddin, Chamidah N, Kurniawan A. Confidence Interval Of The Parameter On Multipredictor Biresponse Longitudinal
Data Analysis Using Local Linear Estimator For Modeling Of Case Increase And Case Fatality Rates Covid-19 In Indonesia: A Theoretical Discussion. Commun Math Biol Neurosci. 2022;2022(23):1–12.
Wang L, Politis DN. Asymptotic validity of bootstrap confidence intervals in nonparametric regression without an additive model. Electron J Stat. 2021;15(1):392–426.
