Trucks Pooling and Allocation in TSE Concept Using GIS Spatial and Novel FFOA

Authors

  • Batara Parada Siahaan Del Institute of Technology
  • Togar Mangihut Simatupang Bandung Institue of Technology
  • Liane Okdinawati Bandung Institue of Technology
  • Chuan-kai Yang National Taiwan University of Science and Technology
  • Dinar Nugroho National Taiwan University of Science and Technology

DOI:

https://doi.org/10.52728/ijjm.v3i4.571

Keywords:

Trucks Pooling, Trucks Allocation, FFOA, GIS, Optimization, Spatial

Abstract

Strategic system logistics business entails the importance of regulating truck pooling facilities and allocating the trucks for cost optimization goals. Regulators and investors must consider spatial constraints such as the supply-demand gap and service distance. Little attention has been paid to developing decision logistics models, particularly truck pooling and allocation decisions. In this study, the FFOA and GIS were used to determine the spatial component of truck pooling decisions, providing a scenario for origin pooling and delivery distance. The model evaluates truck allocation to each city, a distance vector, a spatial factor, and city demand are used for the cost optimization goal. The results show that the FFOA model successfully defines the optimal truck allocation for each truck pooling site with a cost. The managerial implication in developing a sharing economy concept for truck logistics is to use the study's framework model result to solve challenges in truck logistics.

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References

Abdelazim, S., El-Hakim, M., Hossain, K., & Volovski, M. (2022). Pavement damage costs by truck class and economy of scale relative to increased loading. International Journal of Pavement Engineering, 23(6), 1970–1980. https://doi.org/10.1080/10298436.2020.1832221

Abdelmagid, A. M., Gheith, M. S., & Eltawil, A. B. (2022). A comprehensive review of the truck appointment scheduling models and directions for future research. Transport Reviews, 42(1), 102–126. https://doi.org/10.1080/01441647.2021.1955034

Akter, T., & Hernandez, S. (2022). Representative truck activity patterns from anonymous mobile sensor data. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2022.05.002

Bouchery, Y., Hezarkhani, B., & Stauffer, G. (2022). Coalition formation and cost sharing for truck platooning. Transportation Research Part B: Methodological, 165, 15–34. https://doi.org/10.1016/j.trb.2022.08.007

Boyko, C., Clune, S., Cooper, R., Coulton, C., Dunn, N., Pollastri, S., Leach, J., Bouch, C., Cavada, M., De Laurentiis, V., Goodfellow-Smith, M., Hale, J., Hunt, D., Lee, S., Locret-Collet, M., Sadler, J., Ward, J., Rogers, C., Popan, C., … Tyler, N. (2017). How Sharing Can Contribute to More Sustainable Cities. Sustainability, 9(5), 701. https://doi.org/10.3390/su9050701

Cheng, J., & Shi, T. (2022). Structural optimization of transmission line tower based on improved fruit fly optimization algorithm. Computers and Electrical Engineering, 103, 108320. https://doi.org/10.1016/j.compeleceng.2022.108320

Dai, H., Zhao, G., Lu, J., & Dai, S. (2014). Comment and improvement on “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example.” Knowledge-Based Systems, 59, 159–160. https://doi.org/10.1016/j.knosys.2014.01.010

Eckhardt, G. M., Houston, M. B., Jiang, B., Lamberton, C., Rindfleisch, A., & Zervas, G. (2019). Marketing in the Sharing Economy. Journal of Marketing, 83(5), 5–27. https://doi.org/10.1177/0022242919861929

Geruna, H. A., Abdullah, N. R. H., Asril, M. Z., Mustafa, M., Samad, R., & Pebrianti, D. (2017). Fruit fly optimization (FFO) for solving economic dispatch problem in power system. 2017 7th IEEE International Conference on System Engineering and Technology (ICSET), 106–110. https://doi.org/10.1109/ICSEngT.2017.8123429

Iscan, H., & Gunduz, M. (2014). Parameter Analysis on Fruit Fly Optimization Algorithm. Journal of Computer and Communications, 02(04), 137–141. https://doi.org/10.4236/jcc.2014.24018

Islam, S. (2018). Simulation of truck arrival process at a seaport: evaluating truck-sharing benefits for empty trips reduction. International Journal of Logistics Research and Applications, 21(1), 94–112. https://doi.org/10.1080/13675567.2017.1353067

Islam, S., & Olsen, T. (2014). Truck-sharing challenges for hinterland trucking companies. Business Process Management Journal, 20(2), 290–334. https://doi.org/10.1108/BPMJ-03-2013-0042

Islam, S., Shi, Y., Ahmed, J. U., & Uddin, M. J. (2019). Minimization of empty container truck trips: insights into truck-sharing constraints. The International Journal of Logistics Management, 30(2), 641–662. https://doi.org/10.1108/IJLM-08-2018-0191

Karkalos, N. E., Markopoulos, A. P., & Davim, J. P. (2019). Swarm Intelligence-Based Methods. In Computational Methods for Application in Industry 4.0 (1st ed., pp. 33–55). Springer Publishing Company. https://doi.org/10.1007/978-3-319-92393-2_3

Küffner, C. (2022). Multi-level perspective for the development and diffusion of fuel cell heavy-duty trucks. Transportation Research Part D: Transport and Environment, 111, 103460. https://doi.org/10.1016/j.trd.2022.103460

Li, Yancang, & Lian, S. (2018). Improved Fruit Fly Optimization Algorithm Incorporating Tabu Search for Optimizing the Selection of Elements in Trusses. KSCE Journal of Civil Engineering, 22(12), 4940–4954. https://doi.org/10.1007/s12205-017-2000-0

Li, Yang, & Xu, F. (2022). Acoustic emission sources localization of laser cladding metallic panels using improved fruit fly optimization algorithm-based independent variational mode decomposition. Mechanical Systems and Signal Processing, 166, 108514. https://doi.org/10.1016/j.ymssp.2021.108514

Mahmoodabadi, M. J., Rasekh, M., & Zohari, T. (2018). TGA: Team game algorithm. Future Computing and Informatics Journal, 3(2), 191–199. https://doi.org/10.1016/j.fcij.2018.03.002

Marukawa, T. (2017). Sharing economy in China and Japan. The Japanese Political Economy, 43(1–4), 61–78. https://doi.org/10.1080/2329194X.2018.1555666

Marzano, V., Tinessa, F., Fiori, C., Tocchi, D., Papola, A., Aponte, D., Cascetta, E., & Simonelli, F. (2022). Impacts of truck platooning on the multimodal freight transport market: An exploratory assessment on a case study in Italy. Transportation Research Part A: Policy and Practice, 163, 100–125. https://doi.org/10.1016/j.tra.2022.07.001

Miller, S. R. (2015). First Principles for Regulating the Sharing Economy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2568016

Mitić, M., Vuković, N., Petrović, M., & Miljković, Z. (2015). Chaotic fruit fly optimization algorithm. Knowledge-Based Systems, 89, 446–458. https://doi.org/10.1016/j.knosys.2015.08.010

Moeckel, R., & Donnelly, R. (2016). A model for national freight flows, distribution centers, empty trucks and urban truck movements. Transportation Planning and Technology, 39(7), 693–711. https://doi.org/10.1080/03081060.2016.1204091

Mousavi, S. M., Alikar, N., Ghazilla, R. A. R., Tavana, M., & Olugu, E. U. (2017). A bi-objective multi-period series-parallel inventory-redundancy allocation problem with time value of money and inflation considerations. Computers & Industrial Engineering, 104, 51–67. https://doi.org/10.1016/j.cie.2016.12.006

Osieczko, K., Zimon, D., Płaczek, E., & Prokopiuk, I. (2021). Factors that influence the expansion of electric delivery vehicles and trucks in EU countries. Journal of Environmental Management, 296, 113177. https://doi.org/10.1016/j.jenvman.2021.113177

Pan, Q. K., Sang, H. Y., Duan, J. H., & Gao, L. (2014). An improved fruit fly optimization algorithm for continuous function optimization problems. Knowledge-Based Systems, 62, 69–83. https://doi.org/10.1016/j.knosys.2014.02.021

Pan, W. T. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74. https://doi.org/10.1016/j.knosys.2011.07.001

Pan, W. T., Zhu, W. Z., Ma, F. X., Zhong, Z. C., & Yuan, X. F. (2017). Modified fruit fly optimization algorithm of logistics storage selection. The International Journal of Advanced Manufacturing Technology, 93(1–4), 547–558. https://doi.org/10.1007/s00170-017-0699-x

Polo-López, L., Córcoles, J., & Ruiz-Cruz, J. (2018). Antenna Design by Means of the Fruit Fly Optimization Algorithm. Electronics, 7(1), 3. https://doi.org/10.3390/electronics7010003

Poluru, R. K., & Kumar R, L. (2021). An Improved Fruit Fly Optimization (IFFOA) based Cluster Head Selection Algorithm for Internet of Things. International Journal of Computers and Applications, 43(7), 623–631. https://doi.org/10.1080/1206212X.2019.1600831

Shudapreyaa, R. S., & Subramanian, A. (2016). Parameter Selection using Fruit Fly Optimization. I-Manager’s Journal on Computer Science, 3(4), 29. https://doi.org/10.26634/jcom.3.4.4831

Siahaan, B. P., Simatupang, T. M., & Okdinawati, L. (2021). Modelling incentive policy on truck sharing economy concept: current state and literature review. International Journal of Logistics Systems and Management, 39(2), 185. https://doi.org/10.1504/IJLSM.2021.115484

Standing, C., Standing, S., & Biermann, S. (2019). The implications of the sharing economy for transport. Transport Reviews, 39(2), 226–242. https://doi.org/10.1080/01441647.2018.1450307

Sun, H., Li, W., Zheng, L., Ling, S., & Fu, W. (2022). Adaptive co-simulation method and platform application of drive mechanism based on Fruit Fly Optimization Algorithm. Progress in Nuclear Energy, 153, 104397. https://doi.org/10.1016/j.pnucene.2022.104397

Ta, C. H., Kresta, J. V., Forbes, J. F., & Marquez, H. J. (2005). A stochastic optimization approach to mine truck allocation. International Journal of Surface Mining, Reclamation and Environment, 19(3), 162–175. https://doi.org/10.1080/13895260500128914

Wang, C. L., & Li, S. W. (2018). Hybrid fruit fly optimization algorithm for solving multi-compartment vehicle routing problem in intelligent logistics. Advances in Production Engineering & Management, 13(4), 466–478. https://doi.org/10.14743/apem2018.4.304

Wang, R. Y., Hu, P., Hu, C. H., & Pan, J. S. (2022). A novel Fruit Fly Optimization Algorithm with quasi-affine transformation evolutionary for numerical optimization and application. International Journal of Distributed Sensor Networks, 18(2), 155014772110730. https://doi.org/10.1177/15501477211073037

Wu, L., Zuo, C., & Zhang, H. (2015). A cloud model based fruit fly optimization algorithm. Knowledge-Based Systems, 89, 603–617. https://doi.org/10.1016/j.knosys.2015.09.006

Xing, B., & Gao, W. J. (2014). Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms (1st ed., Vol. 62). Springer International Publishing. https://doi.org/10.1007/978-3-319-03404-1

Zhao, F., Ding, R., Wang, L., Cao, J., & Tang, J. (2021). A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism. Expert Systems with Applications, 183, 115342. https://doi.org/10.1016/j.eswa.2021.115342

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Published

2022-10-31

How to Cite

Siahaan, B. P., Simatupang, T. M., Okdinawati, L., Yang, C.- kai, & Nugroho, D. (2022). Trucks Pooling and Allocation in TSE Concept Using GIS Spatial and Novel FFOA. Ilomata International Journal of Management, 3(4), 486–500. https://doi.org/10.52728/ijjm.v3i4.571