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Erschienen in:
Maximum Likelihood Estimation Based on Random Subspace EDA: Application to Extrasolar Planet Detection Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Multi-objective Memetic Algorithm Based on Three-Dimensional Request Prediction for Dynamic Pickup-and-Delivery Problem with Time Windows

verfasst von : Yanming Yang, Xiaoliang Ma, Yiwen Sun, Zexuan Zhu

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

A multi-objective memetic algorithm based on three-dimensional request prediction is proposed in this paper to solve dynamic pickup-and-delivery route problems with time windows. Dynamic requests are predicted in three dimensions including two space coordinates and time based on the statistical distribution of historical data. The predictive routes are planned firstly and tuned subsequently when the real requests occur. Tri-objective route planning problem based on route length, served time and workload is optimized by the proposed multi-objective memetic algorithm based on prediction, which combines multi-objective genetic algorithm with a locality-sensitive hashing based local search. The proposed algorithm is compared with the other two popular algorithms on two test problems and the experimental results show the efficiency of the proposed algorithm.

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Vorheriges Kapitel Parallel Particle Swarm Optimization for Community Detection in Large-Scale Networks
Nächstes Kapitel Optimization of Spectrum-Energy Efficiency in Heterogeneous Communication Network
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Literatur
1.
Cherkesly, M., Desaulniers, G., Laporte, G.: A population-based metaheuristic for the pickup and delivery problem with time windows and LIFO loading. Comput. Oper. Res. 62, 23–35 (2015)MathSciNetCrossRefMATH
2.
Deb, K., Jain, S.: Running performance metrics for evolutionary multi-objective optimization. In: Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), pp. 13–20 (2002)
3.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
4.
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRef
5.
Kachitvichyanukul, V., Sombuntham, P., Kunnapapdeelert, S.: Two solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSO. Comput. Ind. Eng. 89, 125–136 (2015)CrossRef
6.
Lenstra, J.K., Kan, A.: Complexity of vehicle routing and scheduling problems. Networks 11(2), 221–227 (1981)CrossRef
7.
Ma, X., Liu, F., Qi, Y., Wang, X., Li, L., Jiao, L., Yin, M., Gong, M.: A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Trans. Evol. Comput. 20(2), 275–298 (2016)CrossRef
8.
Ma, X., Zhang, Q., Tian, G., Yang, J., Zhu, Z.: On Tchebycheff decomposition approaches for multi-objective evolutionary optimization. IEEE Trans. Evol. Comput. (2017, in press). doi:10.​1109/​TEVC.​2017.​2704118
9.
Savelsbergh, M.W., Sol, M.: The general pickup and delivery problem. Transp. Sci. 29(1), 17–29 (1995)CrossRefMATH
10.
Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Cell. Immunol. 37(1), 1–13 (1995)MathSciNet
11.
Tan, K.C., Lee, L.H., Zhu, Q.L., Ou, K.: Heuristic methods for vehicle routing problem with time windows. Artif. Intell. Eng. 15(3), 281–295 (2001)CrossRef
12.
Wang, J., Zhou, Y., Wang, Y., Zhang, J., Chen, C.P., Zheng, Z.: Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms. IEEE Trans. Cybern. 46(3), 582–594 (2016)CrossRef
13.
Zhang, Y., Gong, D.W., Cheng, J.: Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinf. 22(99), 64–75 (2015)
14.
Zhang, Y., Gong, D., Hu, Y., Zhang, W.: Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148, 150–157 (2015)CrossRef
15.
Zhu, Z., Jia, S., He, S., Sun, Y., Ji, Z., Shen, L.: Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework. Inf. Sci. 298, 274–287 (2015)CrossRef
16.
Zhu, Z., Ong, Y.S., Dash, M.: Wrapper–filter feature selection algorithm using a memetic framework. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(1), 70–76 (2007). (A Publication of the IEEE Systems Man and Cybernetics Society)CrossRef
17.
Zhu, Z., Wang, F., He, S., Sun, Y.: Global path planning of mobile robots using a memetic algorithm. Int. J. Syst. Sci. 46(11), 1982–1993 (2015)MathSciNetCrossRefMATH
18.
Zhu, Z., Xiao, J., He, S., Ji, Z., Sun, Y.: A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem. Inf. Sci. 329, 73–89 (2016)CrossRef
19.
Zhu, Z., Xiao, J., Li, J.Q., Wang, F., Zhang, Q.: Global path planning of wheeled robots using multi-objective memetic algorithms. Integr. Comput.-Aided Eng. 22(4), 387–404 (2015)CrossRef
20.
Zhu, Z., Zhou, J., Ji, Z., Shi, Y.H.: DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm. IEEE Trans. Evol. Comput. 15(5), 643–658 (2011)CrossRef
21.
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms a comparative case study. In: International Conference on Parallel Problem Solving from Nature, pp. 292–301 (1998)
Metadaten
Titel
Multi-objective Memetic Algorithm Based on Three-Dimensional Request Prediction for Dynamic Pickup-and-Delivery Problem with Time Windows
verfasst von
Yanming Yang
Xiaoliang Ma
Yiwen Sun
Zexuan Zhu
Copyright-Jahr
2017
Buch
Simulated Evolution and Learning

Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68759-9_66