Optimizing business location for small and medium enterprises considering travel time uncertainty, natural disasters, and density population: a study case in Jakarta

Authors

DOI:

https://doi.org/10.30656/jsmi.v8i1.8224

Keywords:

Coverage problem, Facility location, Metaheuristics, Optimization

Abstract

This study addresses the critical problem of identifying optimal business locations for small and medium enterprises (SMEs), a decision-making process by factors such as travel time uncertainty, natural disasters, and population density. Existing research in this area has not adequately addressed these complexities, leaving a knowledge gap that this study aims to fill. Our research employs two optimization methods, differential evolu­tion (DE) and mixed integer programming (MIP), to maximize customer coverage. We present a comprehensive model that not only determines optimum and near-optimum business locations but also investigates the scalability of the algorithms with increasing facilities and their adaptability to different traffic scenarios. Key findings indicate that the DE algorithm, in particular, demonstrates superior coverage performance. This study contributes to the field by providing a robust and adaptable model for facility location problem-solving. The insights gained have practical applications for both academia and industry, aiding SMEs in making informed, strategic decisions about business location placement.

References

S. M. T. Rahman and A. Kabir, ‘Factors influencing location choice and cluster pattern of manufacturing small and medium enterprises in cities: evidence from Khulna City of Bangladesh’, J. Glob. Entrep. Res., vol. 9, no. 1, p. 61, Dec. 2019, doi: https://doi.org/10.1186/s40497-019-0187-x.

A. Pagès-Bernaus, H. Ramalhinho, A. A. Juan, and L. Calvet, ‘Designing e-commerce supply chains: a stochastic facility–location approach’, Int. Trans. Oper. Res., vol. 26, no. 2, pp. 507–528, Mar. 2019, doi: https://doi.org/10.1111/itor.12433.

A. A. N. P. Redi, R. R. F. Flame, A. A. N. A. Redioka, W. Winarno, and A. C. Kurniawan, ‘Maximum covering location problem to select facility location for operation timbang in the City of Iloilo, Philippines’, J. Sist. dan Manaj. Ind., vol. 6, no. 2, pp. 135–142, Dec. 2022, doi: https://doi.org/10.30656/jsmi.v6i2.4599.

A. Ahmadi-Javid, P. Seyedi, and S. S. Syam, ‘A survey of healthcare facility location’, Comput. Oper. Res., vol. 79, pp. 223–263, Mar. 2017, doi: https://doi.org/10.1016/j.cor.2016.05.018.

C. Boonmee, M. Arimura, and T. Asada, ‘Facility location optimization model for emergency humanitarian logistics’, Int. J. Disaster Risk Reduct., vol. 24, pp. 485–498, 2017, doi: https://doi.org/10.1016/j.ijdrr.2017.01.017.

O. Berman, I. Hajizadeh, and D. Krass, ‘The maximum covering problem with travel time uncertainty’, IIE Trans., vol. 45, no. 1, pp. 81–96, Jan. 2013, doi: https://doi.org/10.1080/0740817X.2012.689121.

A. Johansson, E. Nekouei, K. H. Johansson, and J. Mårtensson, ‘Strategic Hub-Based Platoon Coordination Under Uncertain Travel Times’, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 8277–8287, 2022, doi: https://doi.org/10.1109/TITS.2021.3077467.

B. Y. Chen et al., ‘Measuring place-based accessibility under travel time uncertainty’, Int. J. Geogr. Inf. Sci., vol. 31, no. 4, pp. 783–804, Apr. 2017, doi: https://doi.org/10.1080/13658816.2016.1238919.

Y. Ma, W. Xu, L. Qin, and X. Zhao, ‘Site Selection Models in Natural Disaster Shelters: A Review’, Sustainability, vol. 11, no. 2. 2019, doi: https://doi.org/10.3390/su11020399.

P. Shi et al., ‘Disaster Risk Science: A Geographical Perspective and a Research Framework’, Int. J. Disaster Risk Sci., vol. 11, no. 4, pp. 426–440, 2020, doi: https://doi.org/10.1007/s13753-020-00296-5.

R. Sun, G. Gao, Z. Gong, and J. Wu, ‘A review of risk analysis methods for natural disasters’, Nat. Hazards, vol. 100, no. 2, pp. 571–593, 2020, doi: https://doi.org/10.1007/s11069-019-03826-7.

T. Sakai, A. Beziat, and A. Heitz, ‘Location factors for logistics facilities: Location choice modeling considering activity categories’, J. Transp. Geogr., vol. 85, p. 102710, 2020, doi: https://doi.org/10.1016/j.jtrangeo.2020.102710.

A. Dixit, C. Clouse, and N. Turken, ‘Strategic business location decisions: Importance of economic factors and place image’, Rutgers Bus. Rev., vol. 4, no. 1, pp. 73–91, 2019, [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3378663.

D. Kik, M. G. Wichmann, and T. S. Spengler, ‘Decision support framework for the regional facility location and development planning problem’, J. Bus. Econ., vol. 92, no. 1, pp. 115–157, 2022, doi: https://doi.org/10.1007/s11573-021-01050-z.

H. Etemad, ‘Managing uncertain consequences of a global crisis: SMEs encountering adversities, losses, and new opportunities’, J. Int. Entrep., vol. 18, no. 2, pp. 125–144, 2020, doi: https://doi.org/10.1007/s10843-020-00279-z.

M. V. Ciasullo, R. Montera, and A. Douglas, ‘Building SMEs’ resilience in times of uncertainty: the role of big data analytics capability and co-innovation’, Transform. Gov. People, Process Policy, vol. 16, no. 2, pp. 203–217, Jan. 2022, doi: https://doi.org/10.1108/TG-07-2021-0120.

A. Silalahi, R. Sukwadi, D. A. H. Pramesjwari, C. W. Oktavia, and C. Natalia, ‘Integrated analytic hierarchy process and mixed integer programming for supplier selection in mold and dies industry’, J. Sist. dan Manaj. Ind., vol. 3, no. 2, pp. 124–133, Dec. 2019, doi: https://doi.org/10.30656/jsmi.v3i2.1765.

A. E. Ezugwu et al., ‘Metaheuristics: a comprehensive overview and classification along with bibliometric analysis’, Artif. Intell. Rev., vol. 54, no. 6, pp. 4237–4316, 2021, doi: https://doi.org/10.1007/s10462-020-09952-0.

E. Cuevas, E. Barocio Espejo, and A. Conde Enríquez, ‘Introduction to Metaheuristics Methods’, in Metaheuristics Algorithms in Power Systems, E. Cuevas, E. Barocio Espejo, and A. Conde Enríquez, Eds. Cham: Springer International Publishing, 2019, pp. 1–8, doi: https://doi.org/10.1007/978-3-030-11593-7_1.

R. Storn and K. Price, ‘Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces’, J. Glob. Optim., vol. 11, no. 4, pp. 341–359, 1997, doi: https://doi.org/10.1023/A:1008202821328.

T. Back, U. Hammel, and H.-P. Schwefel, ‘Evolutionary computation: comments on the history and current state’, IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 3–17, 1997, doi: https://doi.org/10.1109/4235.585888.

F. Lezama, L. E. Sucar, E. M. de Cote, J. Soares, and Z. Vale, ‘Differential evolution strategies for large-scale energy resource management in smart grids’, in Proceedings of the Genetic and Evolutionary Computation Conference Companion, Jul. 2017, pp. 1279–1286, doi: https://doi.org/10.1145/3067695.3082478.

A. Yaman, G. Iacca, and F. Caraffini, ‘A comparison of three differential evolution strategies in terms of early convergence with different population sizes’, in AIP conference proceedings, 2019, vol. 2070, no. 1, p. 020002, doi: https://doi.org/10.1063/1.5089969.

S. Das and P. N. Suganthan, ‘Differential Evolution: A Survey of the State-of-the-Art’, IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 4–31, 2011, doi: https://doi.org/10.1109/TEVC.2010.2059031.

M. R. Islam, H. H. Lu, M. J. Hossain, and L. Li, ‘A comparison of performance of GA, PSO and differential evolution algorithms for dynamic phase reconfiguration technology of a smart grid’, in 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 858–865, doi: https://doi.org/10.1109/CEC.2019.8790357.

Y. Fong, Y. Huang, P. B. Gilbert, and S. R. Permar, ‘chngpt: threshold regression model estimation and inference’, BMC Bioinformatics, vol. 18, no. 1, p. 454, 2017, doi: https://doi.org/10.1186/s12859-017-1863-x.

N. Senavirathne and V. Torra, ‘Rounding based continuous data discretization for statistical disclosure control’, J. Ambient Intell. Humaniz. Comput., vol. 14, no. 11, pp. 15139–15157, 2023, doi: https://doi.org/10.1007/s12652-019-01489-7.

J. Ohlberger, L. A. Rogers, and N. C. Stenseth, ‘Stochasticity and Determinism: How Density-Independent and Density-Dependent Processes Affect Population Variability’, PLoS One, vol. 9, no. 6, p. e98940, Jun. 2014, doi: https://doi.org/10.1371/journal.pone.0098940.

W. Anderson, S. Guikema, B. Zaitchik, and W. Pan, ‘Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru’, PLoS One, vol. 9, no. 7, p. e100037, Jul. 2014, doi: https://doi.org/10.1371/journal.pone.0100037.

Pemerintah Provinsi DKI Jakarta, ‘Daerah rawan banjir DKI Jakarta’, Pemerintah Provinsi DKI Jakarta, 2023. [Online]. Available: https://satudata.jakarta.go.id/open-data.

N. Koohathongsumrit and W. Chankham, ‘Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework’, Appl. Soft Comput., vol. 137, p. 110167, 2023, doi: https://doi.org/10.1016/j.asoc.2023.110167.

T. E. Saputro, G. Figueira, and B. Almada-Lobo, ‘Hybrid MCDM and simulation-optimization for strategic supplier selection’, Expert Syst. Appl., vol. 219, p. 119624, 2023, doi: https://doi.org/10.1016/j.eswa.2023.119624.

Downloads

Published

2024-06-04

Issue

Vol. 8 No. 1 (2024): June

Section

Research Article

License

All articles in Jurnal Sistem dan Manajemen Industri can be disseminated provided they include the identity of the article and the source of the article Jurnal Sistem dan Manajemen Industri. The publisher is not responsible for the contents of the article. The content of the article is the sole responsibility of the author

Creative Commons License

Jurnal Sistem dan Manajemen Industri is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
“Optimizing business location for small and medium enterprises considering travel time uncertainty, natural disasters, and density population: a study case in Jakarta”, j. sist. manaj. ind., vol. 8, no. 1, pp. 47–60, Jun. 2024, doi: 10.30656/jsmi.v8i1.8224.
Crossref
Scopus
Google Scholar
Europe PMC

Similar Articles

1-10 of 33

You may also start an advanced similarity search for this article.