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A memory structure adapted simulated annealing algorithm for a green vehicle routing problem

  • Environmental Science and Pollution Sensing, Monitoring, Modeling and Remediation
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Abstract

Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.

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References

  • Agra A, Christiansen M, Figueiredo R, Hvattum LM, Poss M, Requejo C (2013) The robust vehicle routing problem with time windows. Comput Oper Res 40:856–866

    Article  Google Scholar 

  • Alvarenga GG, Mateus GR, Tomi GD (2007) A genetic ans set partitioning two phase approach for the vehicle routing problem with time windows. Comput Oper Res 34:1561–1584

    Article  Google Scholar 

  • Amini S, Javanshir H, Moghaddam RT (2010) A PSO approach for solving VRPTW with real case study. Int J Res Rev App Sci August: 339–347

  • Apaydın Ö, Gönüllü MT (2008) Emission control with route optimization in solid waste collection process: a case study. Sadhana 33(2):71–82

    Article  Google Scholar 

  • Azi N, Gendreau M, Potvin JY (2010) An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles. Eur J Oper Res 202(3):756–763

    Article  Google Scholar 

  • Banos R, Ortega J, Gil C, Fernandez A, Toro F (2013) A simulated annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Syst Appl 40:1696–1707

    Article  Google Scholar 

  • Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res B-Meth 45:1232–1250

    Article  Google Scholar 

  • Bodin LD (1990) Twenty years of routing and scheduling. Oper Res 38(4):571–579

    Article  Google Scholar 

  • Braess HH, Seiffert U (2005) Handbook of automotive engineering. SAE International, Pennsylvania USA

    Google Scholar 

  • Bräysy O, Gendreau M (2005a) Vehicle routing problem with time windows, part I: route construction and local search algorithms. Transp Sci 39(1):104–118

    Article  Google Scholar 

  • Bräysy O, Gendreau M (2005b) Vehicle routing problem with time windows, Part II: metaheuristics. Transp Sci 39(1):119–139

    Article  Google Scholar 

  • Bühler G, Jochem P (2008) CO2 emission reduction in freight transports how to stimulate environmental friendly behaviour? Centre for European Economic Research, Discussion paper no. 08–066

  • Dantzig GB, Ramser TH (1959) The truck dispatching problem. Manag Sci 6(1):80–91

    Article  Google Scholar 

  • Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Transp Res E-Log 48:100–114

    Article  Google Scholar 

  • Figliozzi M (2010) Vehicle routing problem for emissions minimization. Transportation Research Record: Journal of the Transportation Research Board, 2197:1–7. Transportation Research Board of the National Academies, Washington, D.C.

  • Gehring H, Homberger J (2001) A parallel two-phase metaheuristic for routing problems with time windows. Asia Pac J Oper Res 18:35–47

    Google Scholar 

  • Hoff A, Andersson H, Christiansen M, Hasle G, Lokketangen A (2010) Industrial aspects and literature survey: fleet composition and routing. Comput Oper Res 37:2041–2061

    Article  Google Scholar 

  • Huang Y, Shi C, Zhao L, Van Woensel T (2012) A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries. IEEE International Conference on Service Operations and Logistics and Informatics, Suzhou, China, 8–10 July 2012

  • Jabali O, Van Woensel T, Kok AG (2012) Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod Oper Manag 21(6):1060–1074

    Article  Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  CAS  Google Scholar 

  • Küçükoğlu İ, Ene S, Aksoy A, Öztürk N (2013) A green capacitated vehicle routing problem with fuel consumption optimization model. Int J Comput Eng Res 3(7):16–23

    Google Scholar 

  • Kuo Y (2010) Using simulated annealing to minimize fuel consumption fort the time-dependent vehicle routing problem. Comput Ind Eng 59:157–165

    Article  Google Scholar 

  • Lin SW, Yu VF, Lu CC (2011) A simulated annealing heuristic for the truck and trailer routing problem with time windows. Expert Syst Appl 38:15244–15252

    Article  Google Scholar 

  • Macedo R, Alves C, Carvalho JMV, Clautiaux F, Hanafi S (2011) Solving the vehicle routing problem with time windows and multiple routes exactly using a pseudo-polynomial mode. Eur J Oper Res 214(3):536–545

    Article  Google Scholar 

  • Osman HO, Christofides N (1994) Capacitated clustring problems by hybrid simulated annealing and tabu search. Int Trans Oper Res 1(3):317–336

    Article  Google Scholar 

  • Otten RHJM, Ginneken LPPP (1988) Stop criteria in simulated annealing. In IEEE Computer Design: VLSI in Computers and Processors: 549 – 552

  • Pradenas L, Oportus B, Parada V (2013) Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Syst Appl 40:2985–2991

    Article  Google Scholar 

  • Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time windows constraints. Oper Res 35(2):254–265

    Article  Google Scholar 

  • Suzuki Y (2011) A new truck-routing approach for reducing fuel consumption and pollutants emission. Transp Res D-Tr E 16:73–77

    Article  Google Scholar 

  • Talbi EG (2009) Metaheuristics: from design to implementation. John Wiley and Sons

  • Tan KC, Lee LH, Zhu QL, Ou K (2001) Heuristic methods for vehicle routing problem with time windows. Artif Intell Eng 15:281–295

    Article  Google Scholar 

  • Tavares G, Zsigraiova Z, Semiao V, Carvalho MG (2009) Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modelling. Waste Manag 29:1176–1185

    Article  CAS  Google Scholar 

  • Toth P, Vigo D (2002) The vehicle routing problem. SIAM, Philadelphia: PA

    Book  Google Scholar 

  • Wu Y, Zhao P, Zhang H, Wang Y, Mao G (2012) Assessment for fuel consumption and exhaust emissions of China’s vehicles: future trends and policy implications. Sci World J, Article ID 591343

  • Wygonik E, Goodchild A (2011) Evaluating CO2 emissions, cost, and service quality trade-offs in an urban delivery system case study. IATTS Res 35:7–15

    Article  Google Scholar 

  • Xiao Y, Zhao Q, Kaku I, Xu Y (2012) Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput Oper Res 39:1419–1431

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by The Commission of Scientific Research Projects of Uludag University, Project number KUAP(M)-2012/53. The authors would like to sincerely thank the anonymous referees and editor for their valuable comments.

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Engineering, Uludag University, Gorukle Campus, 16059, Bursa, Turkey

    İlker Küçükoğlu, Seval Ene, Aslı Aksoy & Nursel Öztürk

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  1. İlker Küçükoğlu
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  2. Seval Ene
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  3. Aslı Aksoy
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  4. Nursel Öztürk
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Correspondence to Aslı Aksoy.

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Responsible editor: Philippe Garrigues

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Küçükoğlu, İ., Ene, S., Aksoy, A. et al. A memory structure adapted simulated annealing algorithm for a green vehicle routing problem. Environ Sci Pollut Res 22, 3279–3297 (2015). https://doi.org/10.1007/s11356-014-3253-5

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  • DOI: https://doi.org/10.1007/s11356-014-3253-5

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