Skip to main content

Advertisement

Log in

An interactive possibilistic programming approach for green capacitated vehicle routing problem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The green capacitated vehicle routing problem (GCVRP) has attracted the attention of many researchers recently, due to the increasing global climate issues. This study presents an interactive fuzzy approach for solving green capacitated vehicle routing problem with imprecise travel time for each vehicle and supplier demands. Triangular fuzzy numbers are proposed for modeling uncertainty, and optimization problem is considered as a bi-objective possibilistic mixed-integer programming (PMIP) model. Possibilistic mixed-integer programming and a fuzzy analytical hierarchical process approach (FAHP) are combined to optimize two objective functions: (1) minimum total fuel consumption and (2) maximum total green score. In the first objective function, the fuel consumption ratio model is used. In this model, the fuel consumption is considered as function of travel time and total load of the vehicle. In the second objective function, suppliers are evaluated in terms of environmental factors with the fuzzy AHP method. The normalized weights are assigned to suppliers as a green score. A conciliating solution is obtained by solving this bi-objective mixed integer programming model. The proposed model and solution approach is applied for an automotive company in Turkey. According to the results obtained, a suggestion for a vehicle routing is proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Erdoğdu K, Karabulut K (2022) Bi-objective green vehicle routing problem. Int Trans Oper Res 29:1602–1626. https://doi.org/10.1111/itor.13044

    Article  MathSciNet  Google Scholar 

  2. McCollum D, Yang C (2009) Achieving deep reductions in US transport greenhouse gas emissions: scenario analysis and policy implications. Energy Policy 37(12):5580–5596. https://doi.org/10.1016/j.enpol.2009.08.038

    Article  Google Scholar 

  3. U.S. EPA (U.S. Environmental Protection Agency) (2021) Inventory of U.S. greenhouse gas emissions and sinks: 1990–2019. www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks

  4. 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(7):1419–1431. https://doi.org/10.1016/j.cor.2011.08.013

    Article  MathSciNet  MATH  Google Scholar 

  5. Bektaş D, Bektaş T, Laporte G (2014) A review of recent research on green road freight transportation. Eur J Oper Res 237(3):775–793. https://doi.org/10.1016/j.ejor.2013.12.033

    Article  MATH  Google Scholar 

  6. Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Trans Res Part E: Logist Trans Rev 48(1):100–114. https://doi.org/10.1016/j.tre.2011.08.001

    Article  Google Scholar 

  7. Koç Ç, 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.2015.10.064

    Article  Google Scholar 

  8. Poonthalir G, Nadarajan R (2018) A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications 100(131–144) https://doi.org/10.1016/j.eswa.2018.01.052

  9. Kazemian I, Aref S (2017) A green perspective on capacitated time-dependent vehicle routing problem with time windows. Int J Supply Chain Inventory Manag 2(1):20–38. https://doi.org/10.1504/IJSCIM.2017.10007322

    Article  Google Scholar 

  10. Werners B, Kondratenko Y (2017) Alternative fuzzy approaches for efficiently solving the capacitated vehicle routing problem in conditions of uncertain demands. Complex Systems: Solutions and Challenges in Economics Management and Engineering. Splinger. pp. 521–543. https://doi.org/10.1007/978-3-319-69989-9_31

  11. Wang R, Zhou J, Yi X et al (2019) Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm. J Ambient Intell Human Comput 10:321–332. https://doi.org/10.1007/s12652-018-0703-9

    Article  Google Scholar 

  12. Gupta P, Govindan K, Kumar M, Khaitan M, Khaitan A (2021) Multiobjective capacitated green vehicle routing problem with fuzzy time distances and demands split into bags. Int J Prod Res. https://doi.org/10.1080/00207543.2021.1888392

    Article  Google Scholar 

  13. Singh VP, Sharma K (2021) Capacitated vehicle routing problem with interval type-2 fuzzy demands. In Advances in Mechanical Engineering. Springer Singapore 83–89. https://doi.org/10.1007/978-981-15-3639-7_11

  14. DincYalçın G, Erginel N (2022) An Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing. Univ J Operations Manag 1(1):56–74. https://doi.org/10.37256/ujom.1120221144

    Article  Google Scholar 

  15. Yang T, Wang W, Wu Q (2022) Fuzzy demand vehicle routing problem with soft time windows. Sustainability 14:56–58

    Google Scholar 

  16. Azarkish M, Aghaeipour Y (2022) A fuzzy bi-objective mathematical model for multi-depot electric vehicle location routing problem with time windows and simultaneous delivery and pick-up. Asian J Basic Sci Res 4(2):01–03. https://doi.org/10.38177/AJBSR.2022.4201

    Article  Google Scholar 

  17. Eskandari MJ, Aliahmadi A, Khaleghi GHH (2010) A robust optimisation approach for the milk run problem with time windows with inventory uncertainty: an auto industry supply chain case study. Int J Rapid Manuf 1(2):334–347. https://doi.org/10.1504/IJRAPIDM.2010.034254

    Article  Google Scholar 

  18. Özgen D, Önüt S, Gülsün B, Tuzkaya UF, Tuzkaya G (2008) A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Inf Sci 178(2):485–500. https://doi.org/10.1016/j.ins.2007.08.002

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang RC, Liang TF (2005) Applying possibilistic linear programming to aggregate production planning. Int J Prod Econ 98(3):328–341. https://doi.org/10.1016/j.ijpe.2004.09.011

    Article  Google Scholar 

  20. Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159(2):193–214. https://doi.org/10.1016/j.fss.2007.08.010

    Article  MathSciNet  MATH  Google Scholar 

  21. Lai YJ, Hwang CL (1992) A new approach to some possibilistic linear programming problems. Fuzzy Sets Syst 49:121–133. https://doi.org/10.1016/0165-0114(92)90318-X

    Article  MathSciNet  Google Scholar 

  22. Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–55. https://doi.org/10.1016/0165-0114(78)90031-3

    Article  MathSciNet  MATH  Google Scholar 

  23. Amin SH, Zhang G (2013) An integrated model for closed-loop supply chain configuration and supplier selection: multi-objective approach. Expert Syst Appl 39(8):6782–6791. https://doi.org/10.1016/j.eswa.2011.12.056

    Article  Google Scholar 

  24. Gupta S, Soni U, Kumar G (2019) Green supplier selection using multi-criterion decision making under fuzzy environment: a case study in automotive industry. Comput Ind Eng 136:663–680. https://doi.org/10.1016/j.cie.2019.07.038

    Article  Google Scholar 

  25. Çalık A (2021) A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput 25:2253–2265. https://doi.org/10.1007/s00500-020-05294-9

    Article  Google Scholar 

  26. Amindoust A, Ahmed S, Saghafinia A, Bahreininejad A (2012) Sustainable supplier selection: a ranking model based on fuzzy inference system. Appl Soft Comput 12(6):1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023

    Article  Google Scholar 

  27. Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354. https://doi.org/10.1016/j.jclepro.2012.04.014

    Article  Google Scholar 

  28. Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124(1):252–264. https://doi.org/10.1016/j.ijpe.2009.11.023

    Article  Google Scholar 

  29. Hashemi SH, Karimi A, Tavana M (2015) An integrated green supplier selection approach with analytic network process and improved grey relational analysis. Int J Prod Econ 159:178–191. https://doi.org/10.1016/j.ijpe.2014.09.027

    Article  Google Scholar 

  30. Eren E, Tuzkaya UR (2021) Safe distance-based vehicle routing problem: Medical waste collection case study in COVID-19 pandemic. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107328

    Article  Google Scholar 

  31. Aydınalp Z, Özgen D (2022) Solving vehicle routing problem with time windows using meta heuristic approaches. Int J Intell Comput Cybern. https://doi.org/10.1108/IJICC-01-2022-0021

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. The manuscript was written by Zeynep Aydinalp Birecik. Doğan Özgen supervised the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zeynep Aydinalp Birecik.

Ethics declarations

Conflict of interest

The authors certify that there are no conflicts of interest regarding the publication of this manuscript. They declare that the content of the manuscript has not been published or submitted for publication anywhere.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aydinalp Birecik, Z., Özgen, D. An interactive possibilistic programming approach for green capacitated vehicle routing problem. Neural Comput & Applic 35, 9253–9265 (2023). https://doi.org/10.1007/s00521-022-08180-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08180-7

Keywords