nach oben

Neural Computing and Applications

Erschienen in:

05.02.2022 | Review

Multi-objective optimization of electric vehicle routing problem with battery swap and mixed time windows

verfasst von: Binghai Zhou, Zhe Zhao

Erschienen in: Neural Computing and Applications | Ausgabe 10/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

With the growing interest in green logistics, the electric vehicles have been widely used as an important distribution means. In this paper, the electric vehicle routing problem with battery swap consideration and mixed time windows constraints (EVRP-BS-MTW) is proposed. The problem aims to minimize the total distribution costs and maximize the average utilization of batteries simultaneously, meeting both the environmental and economic interests. To solve this problem, a multi-objective whale optimization algorithm enhanced by particle filter and Levy flights (MWOA-PFLF) is developed. The introduction of particle filter makes it possible to predict the near optimal solutions at each iteration, meanwhile, the combination of Levy flights contributes to escape from local optimum and accelerate convergence. Experimental results have verified the efficiency of the neighborhood search strategies. The results also indicate that the proposed MWOA-PFLF outperforms the comparison algorithms both in solution quality and convergence rate.

Nächster Artikel A survey of deep learning methods for multiple sclerosis identification using brain MRI images

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Dekker R, Bloemhof J, Mallidis I (2012) Operations research for green logistics—an overview of aspects, issues, contributions and challenges. Eur J Oper Res 219:671–679. https://doi.org/10.1016/j.ejor.2011.11.010CrossRef

Davis BA, Figliozzi MA (2013) A methodology to evaluate the competitiveness of electric delivery trucks. Transp Res Part E 49:8–23. https://doi.org/10.1016/j.tre.2012.07.003CrossRef

Keskin M, Çatay B, Laporte G (2021) A simulation-based heuristic for the electric vehicle routing problem with time windows and stochastic waiting times at recharging stations. Comput Oper Res. https://doi.org/10.1016/j.cor.2020.105060MathSciNetCrossRefMATH

Schneider F, Thonemann UW, Klabjan D (2017) Optimization of battery charging and purchasing at electric vehicle battery swap stations. Transp Sci 52:1211–1234. https://doi.org/10.1109/VPPC.2011.6043182CrossRef

Mak HY, Ying R, Shen ZJM (2013) Infrastructure planning for electric vehicles with battery swapping. Manag Sci. https://doi.org/10.2139/ssrn.2022651CrossRef

Avci B, Girotra K, Netessine S (2015) Electric vehicles with a battery switching station: adoption and environmental impact. Soc Sci Electron Publ. https://doi.org/10.2139/ssrn.2005092CrossRef

Wei F, Figliozzi M (2013) An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: a case study from the USA market. Transp Res Part C Emerg Technol 26:135–145. https://doi.org/10.1016/j.trc.2012.06.007CrossRef

Tredeau FP, Salameh ZM (2009) Evaluation of Lithium iron phosphate batteries for electric vehicles application. In: IEEE vehicle power & propulsion conference, 2009

Schneider M, Stenger A, Goeke D (2014) The electric vehicle-routing problem with time windows and recharging stations. Transp Sci 48:500–520. https://doi.org/10.1287/trsc.2013.0490CrossRef

10.

Briand C, He Y, Ngueveu SU (2018) Energy-efficient planning for supplying assembly lines with vehicles. Euro J Transp Log 7:387–414. https://doi.org/10.1007/s13676-018-0129-8CrossRef

11.

Reddy SS, Bijwe PR (2017) Differential evolution-based efficient multi-objective optimal power flow. Neural Comput Appl 31:509–522. https://doi.org/10.1007/s00521-017-3009-5CrossRef

12.

Gong G, Deng Q, Chiong R et al (2019) An effective memetic algorithm for multi-objective job-shop scheduling. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2019.07.011CrossRef

13.

Zhou B, Li X, Zhang Y (2020) Improved multi-objective cuckoo search algorithm with novel search strategies for point-to-point part feeding scheduling problems of automotive assembly lines. Assem Autom 41:24–44. https://doi.org/10.1108/AA-06-2020-0081CrossRef

14.

Nazari-Heris M, Mehdinejad M, Mohammadi-Ivatloo B et al (2017) Combined heat and power economic dispatch problem solution by implementation of whale optimization method. Neural Comput Appl 31:421–436. https://doi.org/10.1007/s00521-017-3074-9CrossRef

15.

Tahani M, Yousefi H, Noorollahi Y et al (2018) Application of nature inspired optimization algorithms in optimum positioning of pump-as-turbines in water distribution networks. Neural Comput Appl 31:7489–7499. https://doi.org/10.1007/s00521-018-3566-2CrossRef

16.

Khalilpourazari S, Pasandideh SHR, Ghodratnama A (2018) Robust possibilistic programming for multi-item EOQ model with defective supply batches: whale optimization and water cycle algorithms. Neural Comput Appl 31:6587–6614. https://doi.org/10.1007/s00521-018-3492-3CrossRef

17.

Erdogan S, Miller-Hooks E (2012) A green vehicle routing problem. Transp Res Part E Log Transp Rev 48:100–114. https://doi.org/10.1016/j.tre.2011.08.001CrossRef

18.

Artmeier A, Haselmayr J, Leucker M et al (2010) The shortest path problem revisited: optimal routing for electric vehicles. In: 33rd annual German conference on AI, 2010, pp 309–316

19.

Zhang S, Gajpal Y, Appadoo SS et al (2018) Electric vehicle routing problem with recharging stations for minimizing energy consumption. Int J Prod Econ 203:404–413. https://doi.org/10.1016/j.ijpe.2018.07.016CrossRef

20.

Goeke D, Schneider M (2015) Routing a mixed fleet of electric and conventional vehicles. Eur J Oper Res 245:81–99. https://doi.org/10.1016/j.ejor.2015.01.049MathSciNetCrossRefMATH

21.

Pelletier S, Jabali O, Laporte G (2016) 50th anniversary invited article—goods distribution with electric vehicles: review and research perspectives. Transp Sci 50:3–22. https://doi.org/10.1287/trsc.2015.0646CrossRef

22.

Barre A, Deguilhem B, Grolleau S et al (2013) A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J Power Sources 241:680–689. https://doi.org/10.1016/j.jpowsour.2013.05.040CrossRef

23.

Yang J, Sun H (2015) Battery swap station location-routing problem with capacitated electric vehicles. Comput Oper Res 55:217–232. https://doi.org/10.1016/j.cor.2014.07.003MathSciNetCrossRefMATH

24.

Zhou BH, Tan F (2018) Electric vehicle handling routing and battery swap station location optimisation for automotive assembly lines. Int J Comput Integr Manuf 31:978–991. https://doi.org/10.1080/0951192x.2018.1493229CrossRef

25.

Hof J, Schneider M, Goeke D (2017) Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops. Transp Res Part B Methodol 97:102–112. https://doi.org/10.1016/j.trb.2016.11.009CrossRef

26.

Chakraborty N, Mondal A, Mondal S (2018) Multi-objective heuristic charge scheduling and eco-routing mechanism for electric vehicles. In: Proceedings of the ninth international conference on future energy systems. https://doi.org/10.1145/3208903.3212065

27.

Rezgui D, Bouziri H, Aggoune-Mtalaa W et al (2018) A hybrid evolutionary algorithm for smart freight delivery with electric modular vehicles. In: 15th IEEE/ACS international conference on computer systems and applications, 2018.

28.

Desaulniers G, Errico F, Irnich S et al (2016) Exact algorithms for electric vehicle-routing problems with time windows. Oper Res 64:1388–1405. https://doi.org/10.1287/opre.2016.1535MathSciNetCrossRefMATH

29.

Andelmin J, Bartolini E (2017) An exact algorithm for the green vehicle routing problem. Transp Sci 51:1288–1303. https://doi.org/10.1287/trsc.2016.0734CrossRef

30.

Guo Z, Yang L, Jiang X, et al (2017) The electric vehicle routing problem with time windows using genetic algorithm. In: 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), 2017

31.

Wang Q, Peng S, Liu S (2020) Optimization of electric vehicle routing problem using Tabu search. In: 2020 Chinese control and decision conference (CCDC), 2020

32.

Koc C, Karaoglan I (2016) The green vehicle routing problem: a heuristic based exact solution approach. Appl Soft Comput 39:154–164. https://doi.org/10.1016/j.asoc.2015.10.064CrossRef

33.

Hiermann G, Puchinger J, Ropke S et al (2016) The electric fleet size and mix vehicle routing problem with time windows and recharging stations. Eur J Oper Res 252:995–1018. https://doi.org/10.1016/j.ejor.2016.01.038MathSciNetCrossRefMATH

34.

Florio AM, Hartl RF, Minner S (2020) New exact algorithm for the vehicle routing problem with stochastic demands. Transp Sci 54:1073–1090. https://doi.org/10.1287/trsc.2020.0976CrossRefMATH

35.

Wang L, Lu J (2019) A memetic algorithm with competition for the capacitated green vehicle routing problem. IEEE/CAA J Autom Sin 6:516–526. https://doi.org/10.1109/jas.2019.1911405CrossRef

36.

Abousleiman R, Rawashdeh O (2014) Tabu search based solution to the electric vehicle energy efficient routing problem. In: Transportation electrification conference & expo, 2014.

37.

Mavrovouniotis M, Li C, Ellinas G et al (2018) Ant colony optimization for the electric vehicle routing problem. In: 2019 IEEE symposium series on computational intelligence (SSCI)

38.

Abousleiman R, Rawashdeh O (2016) Electric vehicle modelling and energy-efficient routing using particle swarm optimisation. IET Intell Transp Syst 10:65–72. https://doi.org/10.1049/iet-its.2014.0177CrossRef

39.

Abousleiman R, Rawashdeh O (2014) Energy-efficient routing for electric vehicles using metaheuristic optimization frameworks. In: Mediterranean electrotechnical conference

40.

Barco J, Guerra A, Muñoz L et al (2017) Optimal routing and scheduling of charge for electric vehicles: a case study. Math Probl Eng 2017:1–16. https://doi.org/10.1155/2017/8509783CrossRef

41.

Ewbank H, Wanke P, Hadi-Vencheh A (2015) An unsupervised fuzzy clustering approach to the capacitated vehicle routing problem. Neural Comput Appl 27:857–867. https://doi.org/10.1007/s00521-015-1901-4CrossRef

42.

Hosseinabadi AAR, Vahidi J, Balas VE et al (2016) OVRP_GELS: solving open vehicle routing problem using the gravitational emulation local search algorithm. Neural Comput Appl 29:955–968. https://doi.org/10.1007/s00521-016-2608-xCrossRef

43.

Montoya A, Guéret C, Mendoza JE et al (2017) The electric vehicle routing problem with nonlinear charging function. Transp Res Part B Methodol 103:87–110. https://doi.org/10.1016/j.trb.2017.02.004CrossRef

44.

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

45.

Tong W (2020) A new whale optimisation algorithm based on self-adapting parameter adjustment and mix mutation strategy. Int J Comput Integr Manuf 33:949–961. https://doi.org/10.1080/0951192x.2020.1736717CrossRef

46.

Abed-alguni B, Klaib A (2020) Hybrid whale optimisation and β-hill climbing algorithm for continuous optimisation problems. Int J Comput Sci Math 12:350. https://doi.org/10.1504/IJCSM.2020.112650MathSciNetCrossRef

47.

Alguni BHA, Klaib AF, Nahar KMO (2019) Island-based whale optimisation algorithm for continuous optimisation problems. Int J Reason Based Intell Syst 11:319–329. https://doi.org/10.1504/ijris.2019.103525CrossRef

48.

Arulampalam MS, Maskell S, Gordon N et al (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50:174–188. https://doi.org/10.1109/78.978374CrossRef

49.

Zhou BH, Shen CY (2018) Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration. J Clean Prod 192:293–305. https://doi.org/10.1016/j.jclepro.2018.04.251CrossRef

50.

Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8:173–195. https://doi.org/10.1162/106365600568202CrossRef

51.

Piotr C et al (1998) Pareto simulated annealing—a metaheuristic technique for multiple-objective combinatorial optimization. J Multi Criteria Decis Anal 7:34–47. https://doi.org/10.1002/(SICI)1099-1360(199801)7:1%3c34::AID-MCDA161%3e3.0.CO;2-6CrossRefMATH

52.

Emde S, Boysen N (2012) Optimally routing and scheduling tow trains for JIT-supply of mixed-model assembly lines. Eur J Oper Res 217:287–299. https://doi.org/10.1016/j.ejor.2011.09.013MathSciNetCrossRefMATH

53.

Hafiz F, Swain A, Patel N et al (2018) A two-dimensional (2-D) learning framework for Particle Swarm based feature selection. Pattern Recognit J Pattern Recognit Soc 76:416–433. https://doi.org/10.1016/j.patcog.2017.11.027CrossRef

54.

Lin Y, Jiang YS, Gong YJ et al (2019) A discrete multiobjective particle swarm optimizer for automated assembly of parallel cognitive diagnosis tests. IEEE Trans Cybern 49:2792–2805. https://doi.org/10.1109/TCYB.2018.2836388CrossRef

55.

Li J, Chen W, Zhang J et al. (2015) A parallel implementation of multiobjective particle swarm optimization algorithm based on decomposition. In: 2015 IEEE symposium series on computational intelligence. https://doi.org/10.1109/ssci.2015.187

56.

Wei P, Zhang Q (2008) A decomposition-based multi-objective particle swarm optimization algorithm for continuous optimization problems. In: The 2008 IEEE international conference on granular computing

57.

Zhou BH, Liao X (2020) Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106217CrossRef

Titel: Multi-objective optimization of electric vehicle routing problem with battery swap and mixed time windows
verfasst von: Binghai Zhou
Zhe Zhao
Publikationsdatum: 05.02.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-06967-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 10/2022

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

Hybrid deep learning models for multivariate forecasting of global horizontal irradiation

Selection of suitable distance education platforms based on human–computer interaction criteria under fuzzy environment

Fake visual content detection using two-stream convolutional neural networks

Integrating label propagation with graph convolutional networks for recommendation

Interval-valued fermatean fuzzy sets with multi-criteria weighted aggregated sum product assessment-based decision analysis framework

Premium Partner