Facility Location Optimization For Technical Inspection Centers Using Multi-Objective Mathematical Modeling Considering Uncertainty
DOI:
https://doi.org/10.31181/jscda11202314Keywords:
Facility Location, Multi-Objective Optimization, Uncertainty, Non-dominated Sorting (NSGA-II)Abstract
Encountering numerous vehicles on the roads can pose several risks, including a higher probability of accidents. To address these issues, a thorough examination of cars can significantly reduce these dangers. Technical inspection centers play a crucial role in this process and should be easily accessible. To provide the most customer service coverage at the lowest cost of transportation for technical inspection centers, facility location optimization is proposed in this paper. Specifically, we investigate the location of technical inspection centers (TICs) as a maximum coverage problem while minimizing the cost of TIC locations' construction and customers' transportation. To deal with this problem, we propose a robust programming considering our numeric data's uncertainty. Our research contributes to facility location optimization by providing a novel insight into solving the problem using a hybrid mathematical model. It presents a two-objective linear optimization model with binary variables to address this optimization problem. We used the Augmented Epsilon Constraint (AEC) method via the CPLEX solver and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) method for large-scale problems to solve the model. A case study was conducted to test the numerical analysis methodology and several practical problems of varying scales. The final results demonstrate the effectiveness of the proposed approach in meeting the optimality and feasibility robustness criteria. Identifying optimal TIC locations regarding the paper's main objective proves the advantage of using the mentioned innovative methodology.
Downloads
References
Aboolian, R., Berman, O., & Krass, D. (2007). Competitive facility location and design problem. European Journal of operational research, 182(1), 40-62. https://doi.org/10.1016/j.ejor.2006.07.021
Arabani, A. B., & Farahani, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408-420. https://doi.org/10.1016/j.cie.2011.09.018
Balinski, M. L. (1965). Integer programming: methods, uses, computations. Management science, 12(3), 253-313. https://doi.org/10.1287/mnsc.12.3.253
Berrocal-Plaza, V., Vega-Rodriguez, M. A., & Sanchez-Perez, J. M. (2014). On the use of multiobjective optimization for solving the location areas strategy with different paging procedures in a realistic mobile network. Applied Soft Computing, 18, 146-157. https://doi.org/10.1016/j.asoc.2014.01.008
Cappanera, P., Gallo, G., & Maffioli, F. (2003). Discrete facility location and routing of obnoxious activities. Discrete Applied Mathematics, 133(1-3), 3-28. https://doi.org/10.1016/S0166-218X(03)00431-1
Cho, H., Park, S., Choi, S., Choi, K. (2019). Optimizing locations for automated inspection facilities for heavy vehicles using GIS and multi-criteria decision-making method. Journal of Intelligent Transportation Systems, 23(4),366-381.
Cuerden, R. W., Edwards, M. J., & Pittman, M. B. (2011). Effects of vehicle defects in road accidents (No. 565).
Current, J., Min, H., & Schilling, D. (1990). Multiobjective analysis of facility location decisions. European journal of operational research, 49(3), 295-307. https://doi.org/10.1016/0377-2217(90)90401-V
Daskin, M. S., Snyder, L. V., & Berger, R. T. (2005). Facility location in supply chain design. In Logistics systems: Design and optimization (pp. 39-65). Boston, MA: Springer US.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017
Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European journal of operational research, 147(2), 231-252. https://doi.org/10.1016/S0377-2217(02)00558-1
Esfahani, M. M., & Sadati, H. (2022, March). Application of NSGA-II in channel selection of motor imagery EEG signals with common spatio-spectral patterns in BCI systems. In 2022 8th International Conference on Control, Instrumentation and Automation (ICCIA) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCIA54998.2022.9737199
Feng, X., Zhang, Y., Li, Y., & Wang, W. (2013). A location-allocation model for seaport-dry port system optimization. Discrete Dynamics in Nature and Society, 2013. https://doi.org/10.1155/2013/309585
Gao, F., Lee, A. H. (2018). A dynamic model for managing relief logistics response activities under uncertainty. International Journal of Production Economics, 203, 21-32.
Gao, Y. (2012). Uncertain models for single facility location problems on networks. Applied mathematical modelling, 36(6), 2592-2599. http://dx.doi.org/10.1016/j.apm.2011.09.042
Ge, J., Zhang, Y., & Park, S. J. (2019). Recent advances in carbonaceous photocatalysts with enhanced photocatalytic performances: a mini review. Materials, 12(12), 1916. https://doi.org/10.3390/ma12121916
Gharibi, K., & Abdollahzadeh, S. (2021). A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-11-2020-0472
Ghoushchi, S. J., Osgooei, E., Haseli, G., & Tomaskova, H. (2021). A novel approach to solve fully fuzzy linear programming problems with modified triangular fuzzy numbers. Mathematics, 9(22), 2937. https://doi.org/10.3390/math9222937
Goldberg, D. E., & Richardson, J. (1987, July). Genetic algorithms with sharing for multimodal function optimization. In Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms (Vol. 4149). Hillsdale, NJ: Lawrence Erlbaum.
Gu, Q., Huang, S., Wang, Q., Li, X., & Liu, D. (2023). A chaotic differential evolution and symmetric direction sampling for large-scale multiobjective optimization. Information Sciences, 639, 119003. https://doi.org/10.1016/j.ins.2023.119003
Gülpınar, N., Pachamanova, D., & Çanakoğlu, E. (2013). Robust strategies for facility location under uncertainty. European Journal of Operational Research, 225(1), 21-35. https://doi.org/10.1016/j.ejor.2012.08.004
Hajipour, V., Farahani, R. Z., & Fattahi, P. (2016). Bi-objective vibration damping optimization for congested location–pricing problem. Computers & Operations Research, 70, 87-100. https://doi.org/10.1016/j.cor.2016.01.001
Harris, I., Mumford, C., & Naim, M. (2009, May). The multi-objective uncapacitated facility location problem for green logistics. In 2009 IEEE Congress on Evolutionary Computation (pp. 2732-2739). IEEE. https://doi.org/10.1109/CEC.2009.4983285
Haseli, G., & Sheikh, R. (2022). Base criterion method (BCM). In Multiple Criteria Decision Making: Techniques, Analysis and Applications (pp. 17-38). Singapore: Springer Nature Singapore. 10.1007/978-981-16-7414-3_2
Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International journal of management science and engineering management, 15(2), 79-88. https://doi.org/10.1080/17509653.2019.1633964
Haseli, G., Torkayesh, A. E., Hajiaghaei-Keshteli, M., & Venghaus, S. (2023). Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts. Applied Soft Computing, 137, 110120. https://doi.org/10.1016/j.asoc.2023.110120
Ho, C. J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The international journal of production research, 27(7), 1115-1135. https://doi.org/10.1080/00207548908942611
Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation research part E: logistics and transportation review, 70, 225-244. https://doi.org/10.1016/j.tre.2014.06.003
Jalalabadi, E., Paylakhi, S. Z., Rahimi‐kian, A., & Moshiri, B. (2022). Integral backstepping Lyapunov redesign control of uncertain nonlinear systems. IET Control Theory & Applications, 16(3), 330-339.https://doi.org/10.1049/cth2.12229
Karatas, M. (2017). A multi-objective facility location problem in the presence of variable gradual coverage performance and cooperative cover. European Journal of Operational Research, 262(3), 1040-1051. https://doi.org/10.1016/j.ejor.2017.04.001
Karatas, M., & Yakıcı, E. (2018). An iterative solution approach to a multi-objective facility location problem. Applied Soft Computing, 62, 272-287. https://doi.org/10.1016/j.asoc.2017.10.035
Kashgarani, H, Miller, C , Suresh, S ,Zacharias, A. (2022). Exploring Performance of GeoCAT data analysis routines on GPUs.
Lan, B., Peng, J. and Chen, L. (2015). An Uncertain Programming Model for Competitive Logistics Distribution Center Location Problem. American Journal of Operations Research, 5, 536-547. doi: 10.4236/ajor.2015.56042.
Lee, J. Y., Aviso, K. B., & Tan, R. R. (2019). Multi-objective optimisation of hybrid power systems under uncertainties. Energy, 175, 1271-1282. https://doi.org/10.1016/j.energy.2019.03.141
Li, Y., Ni, Z., Zhao, T., Yu, M., Liu, Y., Wu, L., & Zhao, Y. (2020). Coordinated scheduling for improving uncertain wind power adsorption in electric vehicles—Wind integrated power systems by multiobjective optimization approach. IEEE Transactions on Industry Applications, 56(3), 2238-2250. https://doi.org/10.1109/TIA.2020.2976909
Liu, B., & Liu, B. (2010). Uncertainty theory (pp. 1-79). Springer Berlin Heidelberg.
Liu, L., Gao, W., Li, H., Xie, J., & Gong, M. (2023). Property of decision variables-inspired location strategy for multiobjective optimization. Swarm and Evolutionary Computation, 77, 101226. https://doi.org/10.1016/j.swevo.2022.101226
Mohammadi, M., Samizadeh, M. ., Pouya, S., & Arabahmadi, R. (2023). Examining the Mediating Effect of Knowledge Management on the Relationship Between Organizational Culture and Organizational Performance. Journal of Soft Computing and Decision Analytics, 1(1), 63-79. https://doi.org/10.31181/jscda1120235
Moradi, H., Beh Aein, R., & Youssef, G. (2021). Multi‐objective design optimization of dental implant geometrical parameters. International Journal for Numerical Methods in Biomedical Engineering, 37(9), e3511. https://doi.org/10.1002/cnm.3511
Nair, V. G., & Guruprasad, K. R. (2020). MR-SimExCoverage: Multi-robot simultaneous exploration and coverage. Computers & Electrical Engineering, 85, 106680. https://doi.org/10.1016/j.compeleceng.2020.106680
Noura, H., Yamanaka, H., Zhang, X. (2021). Optimal Location Planning of Vehicle Inspection Centers: A Comparative Study of MADM Approaches. Sustainability, 13(4), 2344
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683. https://doi.org/10.1016/j.fss.2010.04.010
Qi, S., Zou, J., Yang, S., Jin, Y., Zheng, J., & Yang, X. (2022). A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization. Information sciences, 609, 1601-1620. https://doi.org/10.1016/j.ins.2022.07.110
Quiles, S. G., & Marin, A. (2015). Covering location problems. Location Science, 93-114.
Rababah, M., Maydanchi, M., Pouya, S., Basiri, M., Azad, A. N., Haji, F., & Aminjarahi, M. (2022). Data Visualization of Traffic Violations in Maryland, US. arXiv preprint arXiv:2208.10543. https://doi.org/10.48550/arXiv.2208.10543
Rechnitzer, G., Haworth, N., & Kowadlo, N. (2000). The effect of vehicle roadworthiness on crash incidence and severity (No. 164). Clayton, Australia: Monash University, Accident Research Centre.
Revelle, C. S., Eiselt, H. A., & Daskin, M. S. (2008). A bibliography for some fundamental problem categories in discrete location science. European journal of operational research, 184(3), 817-848. https://doi.org/10.1016/j.ejor.2006.12.044
Rodríguez-Molina, A., Villarreal-Cervantes, M. G., Mezura-Montes, E., & Aldape-Pérez, M. (2019). Adaptive controller tuning method based on online multiobjective optimization: A case study of the four-bar mechanism. IEEE transactions on cybernetics, 51(3), 1272-1285. https://doi.org/10.1109/TCYB.2019.2903491
Saeedian, S., Firoozabadi, M. P., Khabbazan, M. M. (2020). Vehicle estimation in Iran: An analysis of per capita ownership rates and population data. Journal of Transportation Research, Part A: Policy and Practice, 134, 30-40.
Sarbazi-Azad, H., Zarandi, H. R., & Fazeli, M. (2013). A parallel clustering algorithm on the star graph and its performance. Mathematical and Computer Modelling, 58(3-4), 886-897. https://doi.org/10.1016/j.mcm.2013.03.011
Schilling, D. A. (1993). A review of covering problems in facility location. Location Science, 1, 25-55.
Soltani, M. (2018). Joint optimization of opportunistic predictive maintenance and multi-location spare part inventories for a deteriorating system considering imperfect actions. arXiv preprint arXiv:1810.06315. https://doi.org/10.48550/arXiv.1810.06315
Tian, G., Zhou, M., Li, P., Zhang, C., & Jia, H. (2016). Multiobjective optimization models for locating vehicle inspection stations subject to stochastic demand, varying velocity and regional constraints. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1978-1987. DOI: 10.1109/TITS.2016.2514277
Toragay, O., & Silva, D. F. (2021). Fast heuristic approach for control of complex authentication systems. Applied Stochastic Models in Business and Industry, 37(4), 744-766. https://doi.org/10.1002/asmb.2619
Toragay, O., Silva, D. F., Vinel, A., & Shamsaei, N. (2022). Exact global optimization of frame structures for additive manufacturing. Structural and Multidisciplinary Optimization, 65(3), 97. https://doi.org/10.1007/s00158-022-03178-0
Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations research, 19(6), 1363-1373. https://doi.org/10.1287/opre.19.6.1363
Wang, C., He, D. (2020). Optimizing location and capacity of electric vehicle charging stations in urban areas. Sustainability, 12(21), 8862. https://doi.org/10.1109/TITS.2016.2514277
Wang, K. E., & Yang, Q. (2014). Hierarchical facility location for the reverse logistics network design under uncertainty. Journal of Uncertain Systems, 8(4), 255-270.
Wang, S., Zhou, A., Li, B., & Yang, P. (2023). Differential evolution guided by approximated Pareto set for multiobjective optimization. Information Sciences, 630, 669-687. https://doi.org/10.1016/j.ins.2023.02.043
Wen, M., Qin, Z., & Kang, R. (2014). The α-cost minimization model for capacitated facility location-allocation problem with uncertain demands. Fuzzy Optimization and Decision Making, 13(3), 345-356. http://dx.doi.org/10.1007/s10700-014-9179-z
Wu, Z., & Peng, J. (2014). A chance-constrained model of logistics distribution center location under uncertain environment. Advances in Information Sciences and Service Sciences, 6(3), 33.
Yan, X., Liu, C., Wang, Y., Ma, Y. (2020). Research on technical inspection method of vehicle body based on 3D measurement. Measurement, 166, 108011.
Yavary, A., & Sajedi, H. (2018, June). Solving dynamic vehicle routing problem with pickup and delivery by CLARITY method. In 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) (pp. 000207-000212). IEEE. https://doi.org/10.1109/INES.2018.8523908
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28. https://doi.org/10.1016/0165-0114(78)90029-5
Zhang, W., Cao, K., Liu, S., & Huang, B. (2016). A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Computers, Environment and Urban Systems, 59, 220-230. https://doi.org/10.1016/j.compenvurbsys.2016.07.001
Downloads
Published
Issue
Section
License
Copyright (c) 2023 CC Attribution-NonCommercial-NoDerivatives 4.0
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
