Annals of Computer Science and Information Systems, Volume 22

DOI: http://dx.doi.org/10.15439/2020F123

Citation: Position Papers of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 22, pages 45–49 (2020)

Full text

Abstract. The problem of transport optimization is of great importance for the successful operation of distribution companies. To successfully find routes, it is necessary to provide accurate input data on orders, customer location, vehicle fleet, depots, and delivery restrictions. Most of the input data can be provided through the order creation process or the use of various online services. One of the most important inputs is an estimate of the unloading time of the goods for each customer. The number of customers that the vehicle serves during the day directly depends on the time of unloading. This estimate depends on the number of items, weight and volume of orders, but also on the specifics of customers, such as the proximity of parking or crowds at the unloading location. Customers repeat over time, and unloading time can be calculated from GPS data history. The paper describes the innovative application of machine learning techniques and delivery history obtained through a GPS vehicle tracking system for a more accurate estimate of unloading time. The application of techniques gave quality results and significantly improved the accuracy of unloading time data by 83.27\% compared to previously used methods. The proposed method has been implemented for some of the largest distribution companies in Bosnia and Herzegovina.

References

1. Dantzig, G. B., and Ramser, J. H. (1959). The truck dispatching problem. Management science, Vol. 6(1), 80-91. http://dx.doi.org/10.1287/mnsc.6.1.80
2. Žunić, E., Ðonko, D., and Buza., E. (2020). An adaptive data-driven approach to solve real-world vehicle routing problems in logistics. Complexity. Hindawi. http://dx.doi.org/10.1155/2020/7386701
3. Žunić, E., and Ðonko, D. (2019, September). Parameter setting problem in the case of practical vehicle routing problems with realistic constraints. In 2019 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 755-759). IEEE. http://dx.doi.org/10.15439/2019F194
4. Laporte, G. (2009). Fifty years of vehicle routing. Transportation science, 43(4), 408-416. http://dx.doi.org/10.1287/trsc.1090.0301
5. Gendreau, M., Laporte, G., and Potvin, J. Y. (2002). Metaheuristics for the capacitated VRP. In The vehicle routing problem (pp. 129-154). Society for Industrial and Applied Mathematics. http://dx.doi.org/10.1137/1.9780898718515.ch6
6. Braekers, K., Ramaekers, K., and Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers Industrial Engineering, 99, 300-313. http://dx.doi.org/10.1016/j.cie.2015.12.007
7. Osaba, E., Yang, X. S., and Del Ser, J. (2020). Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities. In Nature-Inspired Computation in Navigation and Routing Problems (pp. 57-84). Springer, Singapore. http://dx.doi.org/10.1007/978-981-15-1842-3_3
8. Chiang, W. C., and Russell, R. A. (1996). Simulated annealing meta-heuristics for the vehicle routing problem with time windows. Annals of Operations Research, 63(1), 3-27. http://dx.doi.org/10.1007/BF02601637
9. Gendreau, M., Hertz, A., and Laporte, G. (1994). A tabu search heuristic for the vehicle routing problem. Management science, 40(10), 1276-1290. http://dx.doi.org/10.1287/mnsc.40.10.1276
10. Baker, B. M., and Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers Operations Research, 30(5), 787-800. http://dx.doi.org/10.1109/ICECTECH.2010.5479956
11. Osaba, E., Yang, X. S., Fister Jr, I., Del Ser, J., Lopez-Garcia, P., and Vazquez-Pardavila, A. J. (2019). A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm and evolutionary computation, 44, 273-286. http://dx.doi.org/10.1016/j.swevo.2018.04.001
12. Osaba, E., Yang, X. S., Diaz, F., Onieva, E., Masegosa, A. D., and Perallos, A. (2017). A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Computing, 21(18), 5295-5308. http://dx.doi.org/10.1007/s00500-016-2114-1
13. Žunić, E., Delalić, S., Hodžić, K., Beširević, A., and Hindija, H. (2018, November). Smart warehouse management system concept with implementation. In 2018 14th Symposium on Neural Networks and Applications (NEUREL) (pp. 1-5). IEEE. http://dx.doi.org/10.1109/NEUREL.2018.8587004
14. Delalić, S., Chahin, M., and Alihodžić, A. (2019, October). Optimal City Selection and Concert Tour Planning Based on Heuristic Optimization Methods and the Use of Social Media Analytics. In 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT) (pp. 1-6). IEEE. http://dx.doi.org/10.1109/I-CAT47117.2019.8939040
15. Delalić, S., Alihodžić, A., and Selmanović, E. (2019, October). Innovative Usage of Online Platforms Analytics on Event Planning based on the Genetic Algorithm with Greedy Approach. In 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT) (pp. 1-6). IEEE. http://dx.doi.org/10.1109/ICAT47117.2019.8938919
16. Zhou, X., and Chen, L. (2014). Event detection over twitter social media streams. The VLDB journal, 23(3), 381-400. http://dx.doi.org/10.1007/s00778-013-0320-3
17. Vaughan, A. (2020). Tracking down coronavirus. http://dx.doi.org/10.1016/S0262-4079(20)30834-4
18. Žunić, E., Hindija, H., Beširević, A., Hodžić, K., and Delalić, S. (2018, November). Improving Performance of Vehicle Routing Algorithms using GPS Data. In 2018 14th Symposium on Neural Networks and Applications (NEUREL) (pp. 1-4). IEEE. http://dx.doi.org/10.1109/NEUREL.2018.8586982
19. Žunić, E., Delalić, S., Hodžić, K., and Tucaković, Z. (2019, July). Innovative GPS Data Anomaly Detection Algorithm inspired by QRS Complex Detection Algorithms in ECG Signals. In IEEE EUROCON 2019-18th International Conference on Smart Technologies (pp. 1-6). IEEE. http://dx.doi.org/10.1109/EUROCON.2019.8861619
20. Žunić, E., Delalić, S., and Ðonko, Dž. (2020). Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data. Transportation Letters. http://dx.doi.org/10.1080/19427867.2020.1824311