pp 1–35 | Cite as

Reducing pollutant emissions in a waste collection vehicle routing problem using a variable neighborhood tabu search algorithm: a case study

  • Jose Carlos MolinaEmail author
  • Ignacio Eguia
  • Jesus Racero
Original Paper


This paper focuses on designing waste collection routes with a single landfill using eco-efficiency as a performance indicator. In this problem, there are a limited number of heterogeneous vehicles based at a single depot. Empty vehicles leave the depot, collect waste from a set of locations and drop off the collected waste at a specific landfill. Then, vehicles leave the landfill and may collect more waste from other locations or return empty to the depot. Traditional performance indicators in vehicle routing problems are mainly focused on economic objectives, not explicitly considering environmental issues. In this paper, a mathematical model is presented with an eco-efficient objective function that takes into account external costs (climate change and air pollution). The COPERT model is used for estimating fuel consumption, carbon dioxide and pollutant emissions. The problem is first heuristically solved using a semi-parallel construction algorithm. Then, solutions are improved by a variable neighborhood tabu search algorithm developed for this problem. The algorithm is validated for a real problem in the municipality of Alcalá de Guadaíra, within the metropolitan area of Seville (Spain). Results obtained on a set of case studies improve the solution that is currently implemented in the municipality, in terms of total distance traveled, carbon dioxide emissions and pollutant emissions.


Waste collection vehicle routing problem Variable neighborhood tabu search COPERT model equations Pollutant emissions 

Mathematics Subject Classification

90B06 (Transportation logistics) 90C11 (Mixed-integer programming) 90C59 (Approximation methods and heuristics) 



This research has been fully funded by the Andalusia Government through Grant P10-TEP-6332.


  1. Angelelli E, Speranza MG (2002) The periodic vehicle routing problem with intermediate facilities. Eur J Oper Res 137(2):233–247Google Scholar
  2. Apaydin O, Gonullu MT (2008) Emission control with route optimization in solid waste collection process: a case study. Sadhana 33(2):71–82Google Scholar
  3. Bautista J, Fernández E, Pereira J (2008) Solving an urban waste collection problem using ants heuristics. Comput Oper Res 35(9):3020–3033Google Scholar
  4. Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res Part B Methodol 45(8):1232–1250Google Scholar
  5. Beliën J, De Boeck L, Van Ackere J (2012) Municipal solid waste collection and management problems: a literature review. Transp Sci 48(1):78–102Google Scholar
  6. Benjamin AM, Beasley JE (2013) Metaheuristics with disposal facility positioning for the waste collection VRP with time windows. Optim Lett 7(7):1433–1449Google Scholar
  7. Bing X, de Keizer M, Bloemhof-Ruwaard JM, van der Vorst JG (2014) Vehicle routing for the eco-efficient collection of household plastic waste. Waste Manag 34(4):719–729Google Scholar
  8. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308Google Scholar
  9. Boskovic G, Jovicic N, Milasinovic M, Jovicic G, Milovanovic D (2013) Methodology for reduction of GHG emission from municipal waste collection and transport. Int J Qual Res 7(4):641–652Google Scholar
  10. Brandão J (2011) A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem. Comput Oper Res 38(1):140–151Google Scholar
  11. CE Delft; Infras; Fraunhofer ISI (2011) External costs of transport in Europe—update study for 2008. CE Delft, DelftGoogle Scholar
  12. Croes GA (1958) A method for solving traveling-salesman problems. Oper Res 6(6):791–812Google Scholar
  13. Demir E, Bektaş T, Laporte G (2012) An adaptive large neighborhood search heuristic for the pollution-routing problem. Eur J Oper Res 223(2):346–359Google Scholar
  14. Demir E, Bektaş T, Laporte G (2014a) A review of recent research on green road freight transportation. Eur J Oper Res 237(3):775–793Google Scholar
  15. Demir E, Bektaş T, Laporte G (2014b) The bi-objective pollution-routing problem. Eur J Oper Res 232(3):464–478Google Scholar
  16. Dror M (ed) (2012) Arc routing: theory, solutions and applications. Springer, BerlinGoogle Scholar
  17. EEA (2012) Air quality in Europe-2012 report. EEA report no 4/2012. ISSN 1725-9177. Office for Official Publications of the European Union. European Environment Agency, Copenhagen, DKGoogle Scholar
  18. Eguia I, Racero J, Molina JC, Guerrero F (2013) Environmental issues in vehicle routing problems. In: Erechtchoukova MG, Khaiter PA, Golinska P (eds) Sustainability appraisal: quantitative methods and mathematical techniques for environmental performance evaluation. Springer, Berlin, Heidelberg, pp 215–241Google Scholar
  19. Ene S, Küçükoğlu İ, Aksoy A, Öztürk N (2016) A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet. Int J Veh Des 71(1–4):75–102Google Scholar
  20. Erdoğan S, Miller-Hooks E (2012) A green vehicle routing problem. Transp Res Part E Logist Transp Rev 48(1):100–114Google Scholar
  21. European Commission (2011) Directive 2011/76/EU of the European Parliament and of the Council of 27 September 2011 amending Directive 1999/62/EC on the charging of heavy goods vehicles for the use of certain infrastructures. Official Journal of the European Communities, EUGoogle Scholar
  22. Felipe Á, Ortuño MT, Righini G, Tirado G (2014) A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transp Res Part E Logist Transp Rev 71:111–128Google Scholar
  23. Franceschetti A, Honhon D, Van Woensel T, Bektaş T, Laporte G (2013) The time-dependent pollution-routing problem. Transp Res Part B Methodol 56:265–293Google Scholar
  24. Gendreau M, Hertz A, Laporte G, Stan M (1998) A generalized insertion heuristic for the traveling salesman problem with time windows. Oper Res 46(3):330–335Google Scholar
  25. Golden BL, Wong RT (1981) Capacitated arc routing problems. Networks 11(3):305–315Google Scholar
  26. Han H, Ponce Cueto E (2015) Waste collection vehicle routing problem: literature review. Promet-Traffic Transp 27(4):345–358Google Scholar
  27. Hansen P, Mladenović N, Pérez JAM (2010) Variable neighborhood search: methods and applications. Ann Oper Res 175(1):367–407Google Scholar
  28. Hoff A, Andersson H, Christiansen M, Hasle G, Løkketangen A (2010) Industrial aspects and literature survey: fleet composition and routing. Comput Oper Res 37(12):2041–2061Google Scholar
  29. INFRAS, CE Delft, ISI, University of Gdansk (2008) Handbook on estimation of external cost in the transport sector. produced within the study: internalisation measures and policies for all external cost of transport (IMPACT), Delft, CEGoogle Scholar
  30. Ioannou G, Kritikos M, Prastacos G (2001) A greedy look-ahead heuristic for the vehicle routing problem with time windows. J Oper Res Soc 52(5):523–537Google Scholar
  31. Jabali O, Woensel T, De Kok AG (2012) Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod Oper Manag 21(6):1060–1074Google Scholar
  32. Kara I, Kara B, Yetis M (2007) Energy minimizing vehicle routing problem. In: Dress A, Xu Y, Zhu B (eds) Combinatorial optimization and applications. Lecture Notes in Computer Science, vol 4616. Springer, Berlin, Heidelberg, pp 62–71Google Scholar
  33. Karadimas NV, Papatzelou K, Loumos VG (2007) Optimal solid waste collection routes identified by the ant colony system algorithm. Waste Manag Res 25(2):139–147Google Scholar
  34. Kim BI, Kim S, Sahoo S (2006) Waste collection vehicle routing problem with time windows. Comput Oper Res 33(12):3624–3642Google Scholar
  35. Kindervater GAP, Savelsbergh MWP (1998) Vehicle routing: handling edge exchanges. In: Aarts E, Lenstra JK (eds) Local search in combinatorial optimization. Wiley, London, pp 337–360Google Scholar
  36. Koç Ç, Karaoglan I (2016) The green vehicle routing problem: a heuristic based exact solution approach. Appl Soft Comput 39:154–164Google Scholar
  37. Koç Ç, Bektaş T, Jabali O, Laporte G (2014) The fleet size and mix pollution-routing problem. Transp Res Part B Methodol 70:239–254Google Scholar
  38. Koç Ç, Bektaş T, Jabali O, Laporte G (2015) A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows. Comput Oper Res 64:11–27Google Scholar
  39. Kopfer HW, Schönberger J, Kopfer H (2014) Reducing greenhouse gas emissions of a heterogeneous vehicle fleet. Flex Serv Manuf J 26(1–2):221–248Google Scholar
  40. Kramer R, Subramanian A, Vidal T, Lucídio dos Anjos FC (2015a) A matheuristic approach for the pollution-routing problem. Eur J Oper Res 243(2):523–539Google Scholar
  41. Kramer R, Maculan N, Subramanian A, Vidal T (2015b) A speed and departure time optimization algorithm for the pollution-routing problem. Eur J Oper Res 247(3):782–787Google Scholar
  42. Kuo Y (2010) Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Comput Ind Eng 59(1):157–165Google Scholar
  43. Leggieri V, Haouari M (2017) A practical solution approach for the green vehicle routing problem. Transp Res Part E Logist Transp Rev 104:97–112Google Scholar
  44. Li F, Golden B, Wasil E (2007) A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Comput Oper Res 34(9):2734–2742Google Scholar
  45. Lin C, Choy KL, Ho GT, Chung SH, Lam HY (2014) Survey of green vehicle routing problem: past and future trends. Expert Syst Appl 41(4):1118–1138Google Scholar
  46. Liu FH, Shen SY (1999) The fleet size and mix vehicle routing problem with time windows. J Oper Res Soc 50(7):721–732Google Scholar
  47. Maden W, Eglese R, Black D (2010) Vehicle routing and scheduling with time-varying data: a case study. J Oper Res Soc 61(3):515–522Google Scholar
  48. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100Google Scholar
  49. Molina JC, Eguia I, Racero J (2018) An optimization approach for designing routes in metrological control services: a case study. Flex Serv Manuf J 30(4):924–952Google Scholar
  50. Montoya A, Guéret C, Mendoza JE, Villegas JG (2016) A multi-space sampling heuristic for the green vehicle routing problem. Transp Res Part C Emerg Technol 70:113–128Google Scholar
  51. Ntziachristos L, Samaras Z (2012) EMEP/EEA emission inventory guidebook 2009, updated May 2012Google Scholar
  52. Nuortio T, Kytöjoki J, Niska H, Bräysy O (2006) Improved route planning and scheduling of waste collection and transport. Expert Syst Appl 30(2):223–232Google Scholar
  53. Oberscheider M, Zazgornik J, Henriksen CB, Gronalt M, Hirsch P (2013) Minimizing driving times and greenhouse gas emissions in timber transport with a near-exact solution approach. Scand J For Res 28(5):493–506Google Scholar
  54. Paraskevopoulos DC, Repoussis PP, Tarantilis CD, Ioannou G, Prastacos GP (2008) A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. J Heuristics 14(5):425–455Google Scholar
  55. Pastorello C, Dilara P, Martini G (2011) Effect of a change towards compressed natural gas vehicles on the emissions of the Milan waste collection fleet. Transp Res Part D Transp Environ 16(2):121–128Google Scholar
  56. Penna PHV, Subramanian A, Ochi LS (2013) An iterated local search heuristic for the heterogeneous fleet vehicle routing problem. J Heuristics 19(2):201–232Google Scholar
  57. Pérez JAM, Moreno-Vega JM, Martın IR (2003) Variable neighborhood tabu search and its application to the median cycle problem. Eur J Oper Res 151(2):365–378Google Scholar
  58. Repoussis PP, Paraskevopoulos DC, Tarantilis CD, Ioannou G (2006) A reactive greedy randomized variable neighborhood tabu search for the vehicle routing problem with time windows. In: Almeida F, Blesa Aguilera MJ, Blum C, Moreno Vega JM, Pérez Pérez M, Roli A, Sampels M (eds) Hybrid metaheuristics 2006. Lecture Notes in Computer Science, vol 4030. Springer, Heidelberg, pp 124–138Google Scholar
  59. Savelsbergh MW (1992) The vehicle routing problem with time windows: minimizing route duration. ORSA J Comput 4(2):146–154Google Scholar
  60. Sbihi A, Eglese RW (2007) Combinatorial optimization and green logistics. 4OR 5(2):99–116Google Scholar
  61. Schneider M, Stenger A, Goeke D (2014) The electric vehicle-routing problem with time windows and recharging stations. Transp Sci 48(4):500–520Google Scholar
  62. Semet F (1995) A two-phase algorithm for the partial accessibility constrained vehicle routing problem. Ann Oper Res 61(1):45–65Google Scholar
  63. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res Int Journal 35(2):254–265Google Scholar
  64. Sonesson U (2000) Modelling of waste collection—a general approach to calculate fuel consumption and time. Waste Manag Res 18(2):115–123Google Scholar
  65. Subramanian A, Penna PHV, Uchoa E, Ochi LS (2012) A hybrid algorithm for the heterogeneous fleet vehicle routing problem. Eur J Oper Res 221(2):285–295Google Scholar
  66. Taillard ÉD (1999) A heuristic column generation method for the heterogeneous fleet VRP. RAIRO Oper Res 33(1):1–14Google Scholar
  67. Taillard É, Badeau P, Gendreau M, Guertin F, Potvin JY (1997) A tabu search heuristic for the vehicle routing problem with soft time windows. Transp Sci 31(2):170–186Google Scholar
  68. Tajik N, Tavakkoli-Moghaddam R, Vahdani B, Mousavi SM (2014) A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty. J Manuf Syst 33(2):277–286Google Scholar
  69. Tavares G, Zsigraiova Z, Semiao V, Carvalho MDG (2009) Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modelling. Waste Manag 29(3):1176–1185Google Scholar
  70. Teixeira J, Antunes AP, de Sousa JP (2004) Recyclable waste collection planning—a case study. Eur J Oper Res 158(3):543–554Google Scholar
  71. Xiao Y, Konak A (2016) The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transp Res Part E Logist Transp Rev 88:146–166Google Scholar
  72. 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):1419–1431Google Scholar
  73. Zsigraiova Z, Semiao V, Beijoco F (2013) Operation costs and pollutant emissions reduction by definition of new collection scheduling and optimization of MSW collection routes using GIS. The case study of Barreiro, Portugal. Waste Manag 33(4):793–806Google Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Escuela Superior de IngenieríaUniversidad de SevillaSevilleSpain

Personalised recommendations