Open Access
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
Article Number 01026
Number of page(s) 9
Section Sustainable Infrastucture, Industry, Architecture, and Food Technology
Published online 17 May 2023
  1. ILFA, “Indonesian Logistics Overview,” 2017. [Google Scholar]
  2. K. Braekers, K. Ramaekers, and I. Van Nieuwenhuyse, “The vehicle routing problem: State of the art classification and review,” Comput. Ind. Eng., vol. 99, no. September 2018, pp. 300–313, 2016. [CrossRef] [Google Scholar]
  3. W. Yi and A. P. C. Chan, “Critical Review of Labor Productivity Research in Construction Journals,” J. Manag. Eng., vol. 30, no. 2, pp. 214–225, 2014. [CrossRef] [Google Scholar]
  4. C. H. Caldas, J.-Y. Kim, C. T. Haas, P. M. Goodrum, and D. Zhang, “Method to Assess the Level of Implementation of Productivity Practices on Industrial Projects,” J. Constr. Eng. Manag., vol. 141, no. 1, p. 04014061, 2015. [CrossRef] [Google Scholar]
  5. N. Wassan and G. Nagy, “Vehicle Routing Problem with Deliveries and Pickups: Modelling Issues and Meta-heuristics Solution Approaches,” Int. J. Transp., vol. 2, no. 1, pp. 95–110, 2014. [CrossRef] [Google Scholar]
  6. K. Jitt-aer, A. Group, and D. Jones, “The Integration of Geographic Information Systems and Capacitated Vehicle Routing Problem for Humanitarian Logistics : A Case Study of Preparedness for a Tsunami in,” no. September, 2018. [Google Scholar]
  7. T. Saeheaw and N. Charoenchai, “Integration of geographical information systems, meta-heuristics and optimization models for the employee transportation problem,” J. Spat. Sci., vol. 62, no. 2, pp. 281–306, 2017. [CrossRef] [Google Scholar]
  8. H. Woo, M. Acuna, M. Moroni, M. S. Taskhiri, and P. Turner, “Optimizing the location of biomass energy facilities by integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems (GIS),” Forests, vol. 9, no. 10, pp. 1–15, 2018. [Google Scholar]
  9. G. Zhou, H. Min, and M. Gen, “The balanced allocation of customers to multiple distribution centers in the supply chain network: A genetic algorithm approach,” Comput. Ind. Eng., vol. 43, no. 1–2, pp. 251–261, 2002. [CrossRef] [Google Scholar]
  10. V. Pillac, C. Guéret, and A. L. Medaglia, “An event-driven optimization framework for dynamic vehicle routing,” Decis. Support Syst., vol. 54, no. 1, pp. 414–423, 2012. [CrossRef] [Google Scholar]
  11. S. Y. Cho, Y. H. Lee, D. W. Cho, and M. Gen, “Logistics network optimization considering balanced allocation and vehicle routing,” Marit. Econ. Logist., vol. 18, no. 1, pp. 41–60, 2016. [CrossRef] [Google Scholar]
  12. J. Brandão, “A memory-based iterated local search algorithm for the multi-depot open vehicle routing problem,” Eur. J. Oper. Res., vol. 284, pp. 559–571, 2020. [CrossRef] [Google Scholar]
  13. A. Jungwirth, M. Frey, and R. Kolisch, “The vehicle routing problem with time windows , flexible service locations and time-dependent location capacity,” 2020. [Google Scholar]
  14. E. Ruiz, V. Soto-Mendoza, A. E. Ruiz Barbosa, and R. Reyes, “Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm,” Comput. Ind. Eng., vol. 133, no. March, pp. 207–219, 2019. [CrossRef] [Google Scholar]
  15. J. Li et al., “Discrete firefly algorithm with compound neighborhoods for asymmetric multi-depot vehicle routing problem in the maintenance of farm machinery,” Appl. Soft Comput. J., vol. 81, p. 105460, 2019. [CrossRef] [Google Scholar]
  16. M. E. H. Sadati, D. Aksen, and N. Aras, “The r-interdiction selective multi-depot vehicle routing problem,” Int. Trans. Oper. Res., vol. 27, no. 2, pp. 835–866, 2020. [CrossRef] [Google Scholar]
  17. P. Stodola, “Hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem,” Nat. Comput., vol. 19, no. 2, pp. 463–475, 2020. [CrossRef] [Google Scholar]
  18. S. B. Sarathi Barma, J. Dutta, and A. Mukherjee, “A 2-opt guided discrete antlion optimization algorithm for multi-depot vehicle routing problem,” Decis. Mak. Appl. Manag. Eng., vol. 2, no. 2, pp. 112–125, 2019. [CrossRef] [Google Scholar]
  19. T. R. P. Ramos, M. I. Gomes, and A. P. B. Póvoa, “Multi-depot vehicle routing problem: a comparative study of alternative formulations,” Int. J. Logist. Res. Appl., vol. 23, no. 2, pp. 103–120, 2020. [CrossRef] [Google Scholar]
  20. A. Soeanu, S. Ray, J. Berger, A. Boukhtouta, and M. Debbabi, “Multi-depot vehicle routing problem with risk mitigation: Model and solution algorithm,” Expert Syst. Appl., vol. 145, p. 113099, 2020. [CrossRef] [Google Scholar]
  21. A. Azadeh and H. Farrokhi-Asl, “The close– open mixed multi depot vehicle routing problem considering internal and external fleet of vehicles,” Transp. Lett., vol. 11, no. 2, pp. 78–92, 2019. [CrossRef] [Google Scholar]
  22. B. A. Gusavac, M. Stanojevic, and M. Cangalovic, “Optimal treatment of agricultural land – special multi-depot vehicle routing problem,” Agric. Econ. (Czech Republic), vol. 65, no. 12, pp. 569–578, 2019. [CrossRef] [Google Scholar]
  23. H. Kumar and S. P. Yadav, Decision Science in Action. Springer Singapore, 2019. [Google Scholar]
  24. Y. Li, H. Soleimani, and M. Zohal, “An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives,” J. Clean. Prod., vol. 227, pp. 1161–1172, 2019. [CrossRef] [Google Scholar]
  25. Z. Gu, Y. Zhu, Y. Wang, M. Guizani, and Z. Tian, “Applying artificial bee colony algorithm to the multidepot vehicle routing problem,” no. November 2019, pp. 1–16, 2020. [Google Scholar]
  26. V. Singh, L. Ganapathy, and A. K. Pundir, “An improved genetic algorithm for solving multi depot Vehicle Routing Problems,” Int. J. Inf. Syst. Supply Chain Manag., vol. 12, no. 4, pp. 1–26, 2019. [CrossRef] [Google Scholar]
  27. L. Calvet, D. Wang, A. Juan, and L. Bové, “Solving the multidepot vehicle routing problem with limited depot capacity and stochastic demands,” Int. Trans. Oper. Res., vol. 26, no. 2, pp. 458–484, 2019. [CrossRef] [Google Scholar]
  28. M. Journal and O. F. Mathematical, “Harmony Search for Multi-depot Vehicle Routing Problem Laboratory of Computational Statistics and Operations Research , Department of Mathematics , Faculty of Science , Universiti Putra Faculty of Industrial Sciences & Technology , Universiti Malaysia,” vol. 13, no. 3, pp. 311–328, 2019. [Google Scholar]
  29. S. Rajak, P. Parthiban, and R. Dhanalakshmi, “Multi-depot vehicle routing problem based on customer satisfaction,” Int. J. Serv. Technol. Manag., vol. 26, no. 2–3, pp. 252–265, 2020. [CrossRef] [Google Scholar]
  30. S. Rajak, P. Parthiban, and R. Dhanalakshmi, “A hybrid metaheuristics approach for a multi-depot vehicle routing problem with simultaneous deliveries and pickups,” Int. J. Math. Oper. Res., vol. 15, no. 2, pp. 197–210, 2019. [CrossRef] [Google Scholar]
  31. E. Žunić, S. Delalić, and Ɖ. Donko, “Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data,” Transp. Lett., vol. 00, no. 00, pp. 1–14, 2020. [Google Scholar]
  32. B. Bilal and B. Benadda, “Vehicles Circuits Optimization by Combining GPS / GSM Information with Metaheuristic Algorithms Vehicles Circuits Optimization by Combining GPS / GSM Information with Metaheuristic Algorithms,” no. November, 2020. [Google Scholar]
  33. N. Mahmud and M. M. Haque, “Solving Multiple Depot Vehicle Routing Problem (MDVRP) using Genetic Algorithm,” 2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, pp. 1–6, 2019. [Google Scholar]
  34. O. Rizvanoğlu, S. Kaya, M. Ulukavak, and M.İ. Yeşilnacar, “Optimization of municipal solid waste collection and transportation routes, through linear programming and geographic information system: a case study from Sanl urfa, Turkey,” Environ. Monit. Assess., vol. 192, no. 1, pp. 1–12, 2020. [CrossRef] [Google Scholar]
  35. S. Niu, Y. Yang, and W. Pan, “Logistics Planning and Visualization of Modular Integrated Construction Projects Based on BIM-GIS Integration and Vehicle Routing Algorithm,” Modul. Offsite Constr. Summit Proc., no. 2018, pp. 579–586, 2019. [Google Scholar]
  36. E. Çakmak, İ. Önden, A. Z. Acar, and F. Eldemir, Analyzing the location of city logistics centers in Istanbul by integrating Geographic Information Systems with Binary Particle Swarm Optimization algorithm. 2020. [Google Scholar]
  37. T. Tlili, S. Faiz, and S. Krichen, “Integration of GIS and Optimization Routines for the Vehicle Routing Problem,” Int. J. Chaos, Control. Model. Simul., vol. 2, no. 2, pp. 9–17, 2013. [CrossRef] [Google Scholar]
  38. M. Bruglieri, S. Mancini, F. Pezzella, and O. Pisacane, “A Path-based solution approach for the Green Vehicle Routing Problem,” Comput. Oper. Res., vol. 103, pp. 109–122, 2019. [CrossRef] [Google Scholar]
  39. M. Ganzha and L. Maciaszek, Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS 2020, vol. 22. 2020. [Google Scholar]
  40. L. Amal, L. H. Son, H. Chabchoub, and H. Lahiani, “Analysis of municipal solid waste collection using GIS and multi-criteria decision aid,” Appl. Geomatics, vol. 12, no. 2, pp. 193–208, 2020. [CrossRef] [Google Scholar]
  41. S. M. Hina, J. Szmerekovsky, E. S. Lee, M. Amin, and S. Arooj, “Effective municipal solid waste collection using geospatial information systems for transportation: A case study of two metropolitan cities in Pakistan,” Res. Transp. Econ., no. June 2019, p. 100950, 2020. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.