SOLVING THE PROBLEM OF VEHICLE ROUTING BY EVOLUTIONARY ALGORITHM
1
Faculty of Maritime Technology and Transport, West Pomeranian University of Technology, Al. Piastów 41, 70-065 Szczecin, Poland
Publication date: 2016-03-01
Adv. Sci. Technol. Res. J. 2016; 10(29):97-108
KEYWORDS
ABSTRACT
In the presented work the vehicle routing problem is formulated, which concerns planning the collection of wastes by one garbage truck from a certain number of collection points. The garbage truck begins its route in the base point, collects the load in subsequent collection points, then drives the wastes to the disposal site (landfill or sorting plant) and returns to the another visited collection points. The filled garbage truck each time goes to the disposal site. It returns to the base after driving wastes from all collection points. Optimization model is based on genetic algorithm where individual is the whole garbage collection plan. Permutation is proposed as the code of the individual.
REFERENCES (27)
1.
Abdoun O., Abouchabaka J. 2011. A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. International Journal of Computer Applications, 31(11), 49–57.
2.
Ai T.J., Kachitvichyanukul V. 2009. A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693–1702.
3.
Baldacci R., Mingozzi A., Roberti R. 2012. Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research, 218(1), 1–6.
4.
Balinski M., Quandt R. 1964. On an integer program for a delivery problem. Operations Research, 12, 300–304.
5.
Cao E., Lai M. 2010. The open vehicle routing problem with fuzzy demands. Expert Systems with Applications, 37(3), 2405–2411.
6.
Chen P., Huang H., Dong X.-Y. 2010. Iterated variable neighborhood descent algorithm for the capacitated vehicle routing problem. Expert Systems with Applications, 37(2), 1620–1627.
7.
Dantzig G.B., Ramser J.H. 1956. The truck dispatching problem. Management Science, 6(1), 80–91.
8.
De Jaegere N, Defraeye M, Van Nieuwenhuyse I. 2014. The vehicle routing problem: state of the art classification and review. Research Report KBI_1415, Faculty of Economics and Business, Leuven, België.
9.
Ferrucci F., Bock S., Gendreau M. 2013. A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods. European Journal of Operational Research, 225(1), 130–141.
10.
Fisher M. 1995. Vehicle routing. In: Handbooks of Operations Research and Management Science, Vol 8: Network Routing, Ball M.O., Magnanti T. L., Monoma C.L. and Nemhauser G.L. (Eds.), North-Holland, Amsterdam, 1–31.
11.
Gendreau M., Potvin J.Y. Braysy O. Hasle G., Lokketangen A. 2008. Metaheuristics for the vehicle routing problem and its extensions: a categorized bibliography. In: B. Golden, S. Raghavan and E. Wasil (Eds.), The Vehicle Routing Problem – Latest Advances and New Challenges, Springer, Heidelberg, 143–169.
12.
Jin J., Crainic T.G., Løkketangen A. 2012. A parallel multi-neighborhood cooperative tabu search for capacitated vehicle routing problems. European Journal of Operational Research, 222(3), 441–451.
13.
Ke L., Feng Z. 2013. A two-phase metaheuristic for the cumulative capacitated vehicle routing problem. Computers & Operations Research, 40(2), 633–638.
14.
Laporte G. 1992. The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(3), 345–358.
15.
Li X., Leung S.C.H., Tian P. 2012. A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365–374.
16.
Lin S, Kernighan B.W. 1973. An effective heuristic for the traveling salesman problem, Operations Research, 21, 498–516.
17.
Liu R., Jiang Z. 2012. The close–open mixed vehicle routing problem. European Journal of Operational Research, 220(2), 349–360.
18.
Liu R., Jiang, Z., Fung R.Y.K., Chen F., Liu X. 2010. Two-phase heuristic algorithms for full truckloads multi-depot capacitated vehicle routing problem in carrier collaboration. Computers and Operations Research, 37(5), 950–959.
19.
Lin S.-W., Lee Z.-J., Ying K.-C., and Lee C.-Y. 2009. Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Systems with Applications, 36(2), 1505–1512.
20.
Lysgaard J. 2010. The pyramidal capacitated vehicle routing problem. European Journal of Operational Research, 205(1), 59–64.
21.
Marinakis Y. (2012. Multiple Phase Neighborhood Search-GRASP for the Capacitated Vehicle Routing Problem. Expert Systems with Applications, 39(8), 6807–6815.
22.
Mattos Ribeiro G., Laporte G. 2012. An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem. Computers and Operations Research, 39(3), 728–735.
23.
Ngueveu S. U., Prins C., Wolfler Calvo R. 2010. An effective memetic algorithm for the cumulative capacitated vehicle routing problem. Computers & Operations Research, 37(11), 1877–1885.
24.
Repoussis P.P., Tarantilis C.D. 2010. Solving the fleet size and mix vehicle routing problem with time windows via adaptive memory programming. Transportation Research Part C: Emerging Technologies, 18(5), 695–712.
25.
Salari M., Toth P., Tramontani A. 2010. An ILP improvement procedure for the Open Vehicle Routing Problem. Computers and Operations Research, 37(12), 2106–2120.
26.
Szeto W.Y., Wu Y., Ho S.C. 2011. An artificial bee colony algorithm for the capacitated vehicle routing problem. European Journal of Operational Research, 215(1), 126–135.
27.
Yu S., Ding C., Zhu K. 2011. A hybrid GA–TS algorithm for open vehicle routing optimization of coal mines material. Expert Systems with Applications, 38(8), 10568–10573.
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