Context-Aware Customizable Routing Solution for Fleet Management

Janis Grabis, Žanis Bondars, Jānis Kampars, Ēriks Dobelis, Andrejs Zaharčukovs

Abstract

Vehicle routing solutions delivered to companies as packaged applications combine vehicle routing decision-making models and supporting services for data integration, presentation and other functionality. The packaged applications often are tailored to specific needs of their users thought customization methods and mainly focus on the supporting services rather than on modification of the routing models. This paper proposes a method for customization of the routing model as a part of the routing application. The customization method enables companies to incorporate their specific decision-making goals and context into the routing model without redesigning the model itself. The routing model is also capable of adapting its behaviour according to observed interdependencies among decision-making goals and routing context. An illustrative example is provided to demonstrate customization of the routing solution and to highlight multi-objective and context-dependent characteristics of the vehicle routing problem.

References

  1. Abowd, G.D., 1999. Software engineering issues for ubiquitous computing. Proceedings - International Conference on Software Engineering, pp.75-84 Berziša, S., Bravos, G., Cardona Gonzalez, T., Czubayko, U., Espana, S., Grabis, J., et al., 2015. Capability Driven Development: An Approach to Designing Digital Enterprises. Business & Information Systems Engineering, 57 (1), pp. 15-25.
  2. Cardoso, P.J.S., Schütz, G., Semião, J., Monteiro, J., Rodrigues, J., Mazayev, A., Ey, E., Viegas, M. 2016. Integration of a real-time stochastic routing optimization software with an enterprise resource planner. Advances in Intelligent Systems and Computing, 582, 124-141.
  3. Carton, F., Hynes, T., Adam, F., 2016. A business value oriented approach to decision support systems. Journal of Decision Systems, 25, pp. 85-95.
  4. Cattaruzza, D., Absi, N., Feillet, D., Vidal, T., 2014. A memetic algorithm for the Multi Trip Vehicle Routing Problem. European Journal of Operational Research, 236 (3), pp. 833-848.
  5. Chandra, C., Grabis, J., 2009. A Goal Model Driven Supply Chain Design. International Journal of Data Analysis Techniques and Strategies, 1, (3), 224-241.
  6. Eksioglu, B., Vural, A. V., Reisman, A., 2009. The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57, pp. 1472-1483.
  7. Ghannadpour, S. F., Noori, S., Tavakkoli-Moghaddam, R., 2013. Multiobjective Dynamic Vehicle Routing Problem with Fuzzy Travel Times and Customers' Satisfaction in Supply Chain Management. IEEE Transactions on Engineering Management, 60, 4, 777-790.
  8. Giaglis, G.M., Minis, I., Tatarakis, A., Zeimpekis, V., 2004. Minimizing logistics risk through real-time vehicle routing and mobile technologies. International Journal of Physical Distribution & Logistics Management, 34, 9, 749-764.
  9. Haghani, A., Jung, S, 2005. A dynamic vehicle routing problem with time-dependent travel times. Computers & Operations Research, 32 (11), pp. 2959-2986.
  10. Jozefowiez, N., Semet, F., Talbi, E.-G., 2008. Multiobjective vehicle routing problems. European Journal of Operational Research, 189 (2), pp. 293-309.
  11. Keming, C., 2015. Research on Distribution Vehicle Routing Optimization Based on Cloud Computing. The Open Automation and Control Systems Journal, 7, pp. 2184-2188.
  12. Laporte, G., 1992. The Vehicle Routing Problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59, 345-358.
  13. Madapusi, A., D'Souza, D., 2012. The influence of ERP system implementation on the operational performance of an organization. International Journal of Information Management, 32 (1), pp. 24-34.
  14. Parthasarathy, S., Sharma, S., 2016. Efficiency analysis of ERP packages-A customization perspective. Computers in Industry, 82, pp. 19-27.
  15. Prindezis, N., Kiranoudis, C. T., Marinos-Kouris, D., 2003. A business-to-business fleet management service provider for central food market enterprises. Journal of Food Engineering, 60 (2), pp. 203-210.
  16. Solomon, M. M., 1987. Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research, 35 (2), 254-265.
  17. Speranza, M. G., 2016. Trends in transportation and logistics. European Journal of Operational Research, in press.
  18. Wan, J., Liu, J., Shao, Z., Vasilakos, A. V., Imran, M., Zhou, K., 2016. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles. Sensors, 16, 88.
Download


Paper Citation


in Harvard Style

Grabis J., Bondars Ž., Kampars J., Dobelis Ē. and Zaharčukovs A. (2017). Context-Aware Customizable Routing Solution for Fleet Management . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 638-645. DOI: 10.5220/0006366006380645


in Bibtex Style

@conference{iceis17,
author={Janis Grabis and Žanis Bondars and Jānis Kampars and Ēriks Dobelis and Andrejs Zaharčukovs},
title={Context-Aware Customizable Routing Solution for Fleet Management},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={638-645},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006366006380645},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Context-Aware Customizable Routing Solution for Fleet Management
SN - 978-989-758-247-9
AU - Grabis J.
AU - Bondars Ž.
AU - Kampars J.
AU - Dobelis Ē.
AU - Zaharčukovs A.
PY - 2017
SP - 638
EP - 645
DO - 10.5220/0006366006380645