DOI QR코드

DOI QR Code

Usefulness of Drones in the Urban Delivery System: Solving the Vehicle and Drone Routing Problem with Time Window

배송 네트워크에서 드론의 유용성 검증: 차량과 드론을 혼용한 배송 네트워크의 경로계획

  • Chung, Yerim (School of Business, Yonsei University) ;
  • Park, Taejoon (School of Business, Yonsei University) ;
  • Min, Yunhong (Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd.)
  • 정예림 (연세대학교 경영대학 경영학과) ;
  • 박태준 (연세대학교 경영대학 경영학과) ;
  • 민윤홍 (삼성전자 종합기술원)
  • Received : 2016.07.18
  • Accepted : 2016.08.24
  • Published : 2016.08.31

Abstract

This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon's instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.

Keywords

References

  1. 석상문, "네트워크 문제를 위한 새로운 진화 알고리즘에 대하여", 한국경영과학회지, 제32권, 제2호(2007), pp.109-121.
  2. 윤영수, "적응형 유전알고리즘의 실험적 비교", 한국경영과학회지, 제32권, 제4호(2007), pp.1-18.
  3. 이상헌, 이승원, "시간제약이 있는 차량경로문제에 대한 개미군집 시스템 해법", 한국경영과학회지, 제34권, 제1호(2009), pp.153-165.
  4. "택배 서비스의 핵심 지표: 고객은 '당일 배송'을 원한다(?)", CLO 2015년 4월호 통권 59호, pp.71-73.
  5. 서울시, 2014년 서울시 차량통행속도 보고서, pp.11-13, 2015.
  6. Agatz, N., P. Bouman, and M. Schmidt, "Optimization approaches for the traveling salesman problem with drone," ERIM Report Series Reference, No. ERS-2015-011-LIS., 2015.
  7. Anand, N., H. Quak, R. van Duin, and L. Tavasszy, "City logistics modeling efforts: Trends and gaps-A review," Procedia-Social and Behavioral Sciences, Vol.39(2012), pp.101- 115. https://doi.org/10.1016/j.sbspro.2012.03.094
  8. Berger, J. and M. Barkaoui, "A parallel hybrid genetic algorithm for the vehicle routing problem with time windows," Computers & Operations Research, Vol.31, No.12(2004), pp.2037-2053. https://doi.org/10.1016/S0305-0548(03)00163-1
  9. Blanton, Jr, J.L. and R.L. Wainwright, "Multiple vehicle routing with time and capacity constraints using genetic algorithms," Proceedings of the 5th International Conference on Genetic Algorithms, pp.452-459, Morgan Kaufmann Publishers Inc. (1993, June).
  10. Braysy, O. and M. Gendreau, "Tabu search heuristics for the vehicle routing problem with time windows," Vol.10, No.2(2002), pp.211-237. https://doi.org/10.1007/BF02579017
  11. Braysy, O., "A reactive variable neighborhood search for the vehicle-routing problem with time windows," INFORMS Journal on Computing, Vol.15, No.4(2003), pp.347-368. https://doi.org/10.1287/ijoc.15.4.347.24896
  12. Braysy, O., "Fast local searches for the vehicle routing problem with time windows," INFOR, Vol.40, No.4(2002), pp.319-330.
  13. Braysy, O., W. Dullaert, and M. Gendreau, "Evolutionary algorithms for the vehicle routing problem with time windows," Journal of Heuristics, Vol.10, No.6(2004), pp.587-611. https://doi.org/10.1007/s10732-005-5431-6
  14. Bryan, V., Drone delivery: DHL 'parcelcopter' flies to German isle http://www.reuters.com/article/us-deutsche-post-drones-idUSKCN0HJ1ED20140924, 2014.
  15. Burke, E.K. and J.P. Newall, "A multistage evolutionary algorithm for the timetable problem," Evolutionary Computation, IEEE Transactions on, Vol.3, No.1(1999), pp.63-74. https://doi.org/10.1109/4235.752921
  16. Carter, A.E. and C.T. Ragsdale, "A new approach to solving the multiple traveling salesperson problem using genetic algorithms," European Journal of Operational Research, Vol. 175, No.1(2006), pp.246-257. https://doi.org/10.1016/j.ejor.2005.04.027
  17. Chaovalitwongse, W., D. Kim, and P.M. Pardalos, "GRASP with a new local search scheme for vehicle routing problems with time windows," Journal of Combinatorial Optimization, Vol.7, No.2(2003), pp.179-207. https://doi.org/10.1023/A:1024427114516
  18. Chiang, W.C. and R.A. Russell, "A reactive tabu search metaheuristic for the vehicle routing problem with time windows," INFORMS Journal on Computing, Vol.9, No.4(1997), pp.417-430. https://doi.org/10.1287/ijoc.9.4.417
  19. Chiang, W.C. and R.A. Russell, "Simulated annealing metaheuristics for the vehicle routing problem with time windows," Annals of Operations Research, Vol.63, No.1(1996), pp.3- 27. https://doi.org/10.1007/BF02601637
  20. Colin Lewis, Is package delivery using drones feasible? RobotEcnomics, 2014.(Lewis, 2014b).
  21. Colin Lewis, The economics of Amazon's delivery drones, RobotEcnomics, 2014.(Lewis, 2014a).
  22. Cordeau, J.F., M.M. Gendreau, and G. Laporte, "A tabu search heuristic for periodic and multi ‐depot vehicle routing problems," Networks, Vol.30, No.2(1997), pp.105-119. https://doi.org/10.1002/(SICI)1097-0037(199709)30:2<105::AID-NET5>3.0.CO;2-G
  23. Cordeau, J.F., G. Laporte, and A. Mercier, "A unified tabu search heuristic for vehicle routing problems with time windows," Journal of the Operational Research Society, Vol.52, No.8(2001), pp.928-936. https://doi.org/10.1057/palgrave.jors.2601163
  24. Czech, Z.J. and P. Czarnas, "Parallel Simulated Annealing for the Vehicle Routing Problem with Time Windows," PDP, 2002, p.0376 IEEE.
  25. Davis, L., "Applying adaptive algorithms to epistatic domains," IJCAI, Vol.85(1985), pp.162- 164.
  26. Falkenauer, E., Genetic algorithms and grouping problems, John Wiley & Sons, New York, 1998.
  27. Glover, F., "Ejection chains, reference structures and alternating path methods for traveling salesman problems," Discrete Applied Mathematics, Vol.65, No.1(1996), pp.223-253. https://doi.org/10.1016/0166-218X(94)00037-E
  28. Ha, Q.M., Y. Deville, Q.D. Pham, and M.H. Ha, "Heuristic methods for the Traveling Salesman Problem with Drone," arXiv preprint arXiv:1509.08764., 2015.
  29. Ha, Q.M., Y. Deville, Q.D. Pham, and M.H. Ha, "On the Min-cost Traveling Salesman Problem with Drone," arXiv preprint arXiv: 1512.01503., 2015.
  30. Kilby, P., P. Prosser, and P. Shaw, "Guided local search for the vehicle routing problem with time windows," Meta-Heuristics, Springer, US, (1999), pp.473-486.
  31. Laporte, G., Y. Nobert, and D. Arpin, "Optimal solutions to capacitated multi-depot vehicle routing problems," Universite de Montreal, Centre de recherche sur les transports, 1984.
  32. Laporte, G., Y. Nobert, and S. Taillefer, "Solving a family of multi-depot vehicle routing and location-routing problems," Transportation Science, Vol.22, No.3(1988), pp.161-172. https://doi.org/10.1287/trsc.22.3.161
  33. Lin, S., "Computer solutions of the traveling salesman problem," Bell System Technical Journal, Vol.44(1965), pp.2245-2269. https://doi.org/10.1002/j.1538-7305.1965.tb04146.x
  34. Louis, S.J., X. Yin, and Z.Y. Yuan, "Multiple vehicle routing with time windows using genetic algorithms," Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on Vol.3(1999), IEEE.
  35. Malmborg, C., "A genetic algorithm for service level based vehicle scheduling," European Journal of Operational Research, Vol.93, No.1(1996), pp.121-134. https://doi.org/10.1016/0377-2217(95)00185-9
  36. Murray, C.C. and A.G. Chu, "The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery," Transportation Research Part C: Emerging Technologies, Vol.54(2015), pp.86-109. https://doi.org/10.1016/j.trc.2015.03.005
  37. Oliver, I.M., D.D. Smith, and J.R. Holland, "Study of permutation crossover operators on the traveling salesman problem," Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms: July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates, 1987.
  38. Park, Y.B., "A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines," International Journal of Productions Economics, Vol.73, No.2(2001), pp.175-188. https://doi.org/10.1016/S0925-5273(00)00174-2
  39. Renaud, J., F.F. Boctor, and G. Laporte, "An improved petal heuristic for the vehicle routing problem," Journal of the Operational Research Society, Vol.47, No.2(1996), pp.329- 336. https://doi.org/10.1057/jors.1996.29
  40. Renaud, J., G. Laporte, and F.F. Boctor, "A tabu search heuristic for the multi-depot vehicle routing problem," Computers & Operations Research, Vol.23, No.3(1996), pp.229- 235. https://doi.org/10.1016/0305-0548(95)O0026-P
  41. Rose, C., "Amazon's Jeff Bezos looks to the future," D. Mihailovich (Producer), (2013.), p.60.
  42. Solomon, M.M., "Algorithms for the vehicle routing and scheduling problems with time window constraints," Operations research, Vol.35, No.2(1987), pp.254-265. https://doi.org/10.1287/opre.35.2.254
  43. Starkweather, T., S. McDaniel, K.E. Mathias, L.D. Whitley, and C. Whitley, "A Comparison of Genetic Sequencing Operators," In ICGA (1991, July) pp.69-76.
  44. Syswerda, G., "Schedule optimization using genetic algorithms," Handbook of genetic algorithms, 1991.
  45. Taillard, E., P. Badeau, M. Gendreau, F. Guertin, and J.Y. Potvin, "A tabu search heuristic for the vehicle routing problem with soft time windows," Transportation science, Vol.31, No.2(1997), pp.170-186. https://doi.org/10.1287/trsc.31.2.170
  46. Tan, K.C., Y.H. Chew, and L.H. Lee, "A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows," Computational Optimization and Applications, Vol.34, No.1(2006), pp.115-151. https://doi.org/10.1007/s10589-005-3070-3
  47. Tang, L., J. Liu, A. Rong, and Z. Yang, "A multiple traveling salesman problem model for hot rolling schedule in Shanghai Baoshan Iron and Steel Complex," European Journal of Operational Research, Vol.124, No.2(2000), p.267. https://doi.org/10.1016/S0377-2217(99)00380-X
  48. Taniguchi, E. and R.G. Thompson, "City Logistics Network Modelling and Intelligent Transport Systems," Emerald, United Kingdom, 2011.
  49. Whitley, L.D., T. Starkweather, and D. Fuquay, "Scheduling problems and traveling salesmen: The genetic edge recombination operator," In ICGA, Vol.89(1989), pp.133-140.
  50. Zhang, M., D. Jiang, and X. Tang, "Full load vehicle routing with multiple depots: New network flow based algorithm," Journal of Systems Science and Systems Engineering, Vol.6(2002), pp.216-220.