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Sustainability-based review of urban freight models

  • Maria Elena NenniEmail author
  • Antonio Sforza
  • Claudio Sterle
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Abstract

This paper provides a review of models and decision support systems for urban freight transport (UFT). The originality of this study is that the analysis framework has been developed to outline the progress on UFT specifically related to the sustainability issue. Accordingly, contributions regarding UFT and addressing at least one factor of sustainability have been analysed by cross-referencing categories of models with impacts on sustainability. Results from this work are supposed to help enhance research concerning operations research (OR) for sustainable UFT by pointing out gaps that need to be closed and opportunities for future research. There is also an attempt to understand what is stopping researchers from including selected sustainability factors in the optimisation models. This paper also finally proposes some developments, not only in the OR field, that are preparatory for elaborating new models or improving the existing ones.

Keywords

Urban freight transport Sustainability Last-mile delivery Literature review 
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Notes

Acknowledgements

This study was supported by the MOSTLOG—A multi-objective approach for a SusTainable LOGistic system—Project funded by the University Federico II of Naples, D.R. 341, 8 February 2016.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Allen J, Thorne G, Browne M (2007) BESTUFS good practice guide on urban freight transport. In: Good practice guide on urban freight transport. BESTUFS EU Thematic Network, BrusselsGoogle Scholar
  2. Allen J, Browne M, Holguin-Veras J (2010) Sustainability strategies for city logistics. Improving the environmental sustainability of logistics, Green Logist, pp 282–305Google Scholar
  3. Alumur SA, Kara BY, Karasan OE (2012) Multimodal hub location and hub network design. Omega 40(6):927–939CrossRefGoogle Scholar
  4. Ambrosini C, Routhier JL (2004) Objectives, methods and results of surveys carried out in the field of urban freight transport: an international comparison. Transport Rev 24(1):57–77CrossRefGoogle Scholar
  5. Ambrosini C, Routhier JL, Sonntag H, Meimbresse B (2008) Urban freight modelling: a review. In: Taniguchi E, Thompson RG (eds) Innovations in City Logistics, Nova Science Publishers, New York, pp 197–211Google Scholar
  6. Ameknassi L, Aït-Kadi D, Rezg N (2016) Integration of logistics outsourcing decisions in a green supply chain design: a stochastic multi-objective multi-period multi-product programming model. Int J Prod Econ 182:165–184CrossRefGoogle Scholar
  7. Andersen J, Crainic TG, Christiansen M (2009) Service network design with management and coordination of multiple fleets. Eur J Oper Res 193(2):377–389MathSciNetzbMATHCrossRefGoogle Scholar
  8. Arampantzi C, Minis I (2017) A new model for designing sustainable supply chain networks and its application to a global manufacturer. J Clean Prod 156:276–292CrossRefGoogle Scholar
  9. AustriaTech (2014) Annex: electric fleets in urban logistics—overview of current low emission vehicles. Published as part of the ENCLOSE project. http://www.austriatech.at/files/get/9e26eb124ad90ffa93067085721d4942/austriatech_electricfleets_annex.pdf. Last accessed 22 May 2014
  10. Awasthi A, Chauhan SS, Goyal SK (2011) A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Math Comput Model 53(1–2):98–109MathSciNetzbMATHCrossRefGoogle Scholar
  11. Ayar B, Yaman H (2012) An intermodal multicommodity routing problem with scheduled services. Comput Optim Appl 53(1):131–153MathSciNetzbMATHCrossRefGoogle Scholar
  12. Babazadeh R, Razmi J, Pishvaee MS, Rabbani M (2017) A sustainable second-generation biodiesel supply chain network design problem under risk. Omega 66:258–277CrossRefGoogle Scholar
  13. Bandeira DL, Becker JL, Borenstein D (2009) A DSS for integrated distribution of empty and full containers. Decis Support Syst 47(4):383–397CrossRefGoogle Scholar
  14. Barceló J, Grzybowska H, Pardo S (2007) Vehicle routing and scheduling models, simulation and city logistics. In: Zeimpekis V, Tarantilis CD, Giaglis GM, Minis I (eds) Dynamic fleet management. Springer, Boston, MA, pp 163–195CrossRefGoogle Scholar
  15. Behrends S (2011) Urban freight transport sustainability-the interaction of urban freight and intermodal transport. Chalmers University of Technology, GothenburgGoogle Scholar
  16. Behrends S, Lindholm M, Woxenius J (2008) The impact of urban freight transport: a definition of sustainability from an actor’s perspective. Transp Plan Technol 31(6):693–713CrossRefGoogle Scholar
  17. Bektaş T, Laporte G (2011) The pollution-routing problem. Transp Res B Methodol 45(8):1232–1250CrossRefGoogle Scholar
  18. Bielli M, Bielli A, Rossi R (2011) Trends in models and algorithms for fleet management. Procedia Soc Behav Sci 20:4–18CrossRefGoogle Scholar
  19. Bock S (2010) Real-time control of freight forwarder transportation networks by integrating multimodal transport chains. Eur J Oper Res 200(3):733–746MathSciNetzbMATHCrossRefGoogle Scholar
  20. Bontekoning YM, Macharis C, Trip JJ (2004) Is a new applied transportation research field emerging? A review of intermodal rail–truck freight transport literature. Transp Res A Policy Pract 38(1):1–34CrossRefGoogle Scholar
  21. Boonsothonsatit K, Kara S, Ibbotson S, Kayis B (2015) Development of a generic decision support system based on multi-objective optimization for green supply chain network design (GOOG). J Manuf Technol Manag 26(7):1069–1084CrossRefGoogle Scholar
  22. Boschian V, Paganelli P, Pondrelli L (2013) A global freight business ecosystem based on low carbon end-to-end transport and logistic services. Int J Adv Logist 2(1):1–14CrossRefGoogle Scholar
  23. Brandenburg M (2015) Low carbon supply chain configuration for a new product—a goal programming approach. Int J Prod Res 53(21):6588–6610CrossRefGoogle Scholar
  24. Bravo JJ, Vidal CJ (2013) Freight transportation function in supply chain optimization models: a critical review of recent trends. Expert Syst Appl 40(17):6742–6757CrossRefGoogle Scholar
  25. Brown JR, Guiffrida AL (2014) Carbon emissions comparison of last mile delivery versus customer pickup. Int J Logist Res Appl 17(6):503–521CrossRefGoogle Scholar
  26. Browne M, Piotrowska M, Woodburn A, Allen J (2007) Literature review WM9: part I-urban freight transport. Green logistics project. University of Westminster, LondonGoogle Scholar
  27. Browne M, Allen J, Nemoto T, Patier D, Visser J (2012) Reducing social and environmental impacts of urban freight transport: a review of some major cities. Procedia Soc Behav Sci 39:19–33CrossRefGoogle Scholar
  28. Brundtland GH (1985) World commission on environment and development. Environ Policy Law 14(1):26–30Google Scholar
  29. Brundtland GH (1987) What is sustainable development. In: Our common future. Report of the 1987 World Commission on Environment and Development, pp 8–9Google Scholar
  30. Buldeo Rai H, van Lier T, Meers D, Macharis C (2018) An indicator approach to sustainable urban freight transport. J Urban Int Res Placemak Urban Sustain 11(1):81–102CrossRefGoogle Scholar
  31. Cachon GP (2014) Retail store density and the cost of greenhouse gas emissions. Manag Sci 60(8):1907–1925CrossRefGoogle Scholar
  32. Chen X, Wang X (2016) Effects of carbon emission reduction policies on transportation mode selections with stochastic demand. Transp Res E Logist Transp Rev 90:196–205CrossRefGoogle Scholar
  33. Chen D, Ignatius J, Sun D, Goh M, Zhan S (2018) Impact of congestion pricing schemes on emissions and temporal shift of freight transport. Transp Res E Logist Transp Rev 118:77–105CrossRefGoogle Scholar
  34. Comi A, Donnelly R, Russo F (2014) Urban freight models. In: Tavasszy L, De Jong J (eds) Modelling freight transport, chap 8, Elsevier, pp 163–200.  https://doi.org/10.1016/B978-0-12-410400-6.00008-2
  35. Danloup N, Mirzabeiki V, Allaoui H, Goncalves G, Julien D, Mena C (2015) Reducing transportation greenhouse gas emissions with collaborative distribution a case study. Manag Res Rev 38(10):1049–1067CrossRefGoogle Scholar
  36. Dehghanian F, Mansour S (2009) Designing sustainable recovery network of end-of-life products using genetic algorithm. Resour Conserv Recycl 53(10):559–570CrossRefGoogle Scholar
  37. Demir E, Bektaş T, Laporte G (2012) An adaptive large neighborhood search heuristic for the pollution-routing problem. Eur J Oper Res 223(2):346–359MathSciNetzbMATHCrossRefGoogle Scholar
  38. Demir E, Bektaş T, Laporte G (2014) A review of recent research on green road freight transportation. Eur J Oper Res 237(3):775–793zbMATHCrossRefGoogle Scholar
  39. Devika K, Jafarian A, Nourbakhsh V (2014) Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. Eur J Oper Res 235(3):594–615MathSciNetzbMATHCrossRefGoogle Scholar
  40. E-commerce Statistics for Individuals (2018) Statistics Explained. Retrieved from http://ec.europa.eu/eurostat/statisticsexplained/. Accessed 14 May 2018
  41. Elhedhli S, Merrick R (2012) Green supply chain network design to reduce carbon emissions. Transp Res D Transport Environ 17(5):370–379CrossRefGoogle Scholar
  42. Elhedhli S, Wu H (2010) A Lagrangean heuristic for hub-and-spoke system design with capacity selection and congestion. INFORMS J Comput 22(2):282–296MathSciNetzbMATHCrossRefGoogle Scholar
  43. Elkington J (1997) Cannibals with forks: the triple bottom line of twentieth century business. Capstone, OxfordGoogle Scholar
  44. Farahani RZ, Hekmatfar M, Arabani AB, Nikbakhsh E (2013) Hub location problems: a review of models, classification, solution techniques, and applications. Comput Ind Eng 64(4):1096–1109CrossRefGoogle Scholar
  45. Fareeduddin M, Hassan A, Syed MN, Selim SZ (2015) The impact of carbon policies on closed-loop supply chain network design. Procedia CIRP 26:335–340CrossRefGoogle Scholar
  46. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Mirjalili S (2018) Multi-objective stochastic closed-loop supply chain network design with social considerations. Appl Soft Comput 71:505–525CrossRefGoogle Scholar
  47. Faulin J, Juan A, Lera F, Grasman S (2011) Solving the capacitated vehicle routing problem with environmental criteria based on real estimations in road transportation: a case study. Procedia Soc Behav Sci 20:323–334CrossRefGoogle Scholar
  48. Figliozzi MA (2011) The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels: results from a case study in Portland, Oregon. Transp Res C Emerging Technol 19(5):766–778CrossRefGoogle Scholar
  49. Franceschetti A, Honhon D, Van Woensel T, Bektaş T, Laporte G (2013) The time-dependent pollution-routing problem. Transp Res B Methodol 56:265–293CrossRefGoogle Scholar
  50. Friedrich M, Haupt T, Nökel K (2003, August) Freight modelling: data issues, survey methods, demand and network models. In: 10th international conference on travel behaviour research, Lucerne, Switzerland, AugustGoogle Scholar
  51. Glock CH, Kim T (2015) Coordinating a supply chain with a heterogeneous vehicle fleet under greenhouse gas emissions. Int J Logist Manag 26(3):494–516CrossRefGoogle Scholar
  52. Govindan K, Darbari JD, Agarwal V, Jha PC (2017) Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network. J Clean Prod 165:1598–1619CrossRefGoogle Scholar
  53. Hammad AW, Akbarnezhad A, Rey D (2017) Sustainable urban facility location: minimising noise pollution and network congestion. Transp Res E Logist Transp Rev 107:38–59CrossRefGoogle Scholar
  54. Hoen KMR, Tan T, Fransoo JC, Van Houtum GJ (2013) Switching transport modes to meet voluntary carbon emission targets. Transp Sci 48(4):592–608CrossRefGoogle Scholar
  55. Hoen KMR, Tan T, Fransoo JC, Van Houtum GJ (2014) Effect of carbon emission regulations on transport mode selection under stochastic demand. Flex Serv Manuf J 26(1–2):170–195CrossRefGoogle Scholar
  56. Hu J, Morais H, Sousa T, Lind M (2016) Electric vehicle fleet management in smart grids: a review of services, optimization and control aspects. Renew Sustain Energy Rev 56:1207–1226CrossRefGoogle Scholar
  57. Ishfaq R, Sox CR (2010) Intermodal logistics: the interplay of financial, operational and service issues. Transp Res E Logist Transp Rev 46(6):926–949CrossRefGoogle Scholar
  58. Ishfaq R, Sox CR (2011) Hub location–allocation in intermodal logistic networks. Eur J Oper Res 210(2):213–230MathSciNetzbMATHCrossRefGoogle Scholar
  59. Ishfaq R, Sox CR (2012) Design of intermodal logistics networks with hub delays. Eur J Oper Res 220(3):629–641MathSciNetzbMATHCrossRefGoogle Scholar
  60. Jabali O, Van Woensel T, De Kok AG (2012) Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod Oper Manag 21(6):1060–1074CrossRefGoogle Scholar
  61. Jakovljevic B, Paunovic K, Belojevic G (2009) Road-traffic noise and factors influencing noise annoyance in an urban population. Environ Int 35(3):552–556CrossRefGoogle Scholar
  62. Junior O, Duffrayer PA (2017) Evaluating the economical and environmental impact of a cargo cycle urban distribution: a case study. Doctoral dissertationGoogle Scholar
  63. Kang J (2006) Urban sound environment. CRC Press, Boca RatonCrossRefGoogle Scholar
  64. Kannan D, Diabat A, Alrefaei M, Govindan K, Yong G (2012) A carbon footprint based reverse logistics network design model. Resour Conserv Recycl 67:75–79CrossRefGoogle Scholar
  65. Kumar RS, Kondapaneni K, Dixit V, Goswami A, Thakur LS, Tiwari MK (2016) Multi-objective modeling of production and pollution routing problem with time window: a self-learning particle swarm optimization approach. Comput Ind Eng 99:29–40CrossRefGoogle Scholar
  66. Kwon YJ, Choi YJ, Lee DH (2013) Heterogeneous fixed fleet vehicle routing considering carbon emission. Transp Res D Transport Environ 23:81–89CrossRefGoogle Scholar
  67. Lam HL, Varbanov PS, Klemes JJ (2010) Optimization of regional energy supply chains utilizing renewables: P-graph approach. Comput Chem Eng 34(5):782–792CrossRefGoogle Scholar
  68. Li J, Su Q, Ma L (2017) Production and transportation outsourcing decisions in the supply chain under single and multiple carbon policies. J Clean Prod 141:1109–1122CrossRefGoogle Scholar
  69. Liljestrand K, Christopher M, Andersson D (2015) Using a transport portfolio framework to reduce carbon footprint. Int J Logist Manag 26(2):296–312CrossRefGoogle Scholar
  70. Limbourg S, Jourquin B (2009) Optimal rail-road container terminal locations on the European network. Transp Res E Logist Transp Rev 45(4):551–563CrossRefGoogle Scholar
  71. Lindholm M, Behrends S (2012) Challenges in urban freight transport planning—a review in the Baltic Sea Region. J Transp Geogr 22:129–136CrossRefGoogle Scholar
  72. Loni P, Khamseh AA, Pasandideh SHR (2018) A new multi-objective/product green supply chain considering quality level reprocessing cost. Int J Serv Oper Manag 30(1):1–22Google Scholar
  73. Macharis C, Bontekoning YM (2004) Opportunities for OR in intermodal freight transport research: a review. Eur J Oper Res 153(2):400–416zbMATHCrossRefGoogle Scholar
  74. Maden W, Eglese R, Black D (2010) Vehicle routing and scheduling with time-varying data: a case study. J Oper Res Soc 61(3):515–522zbMATHCrossRefGoogle Scholar
  75. Manzini R, Bindi F (2009) Strategic design and operational management optimization of a multi stage physical distribution system. Transp Res E Logist Transp Rev 45(6):915–936CrossRefGoogle Scholar
  76. MDS Transmodal (2012) Study on urban freight transport, for DG MOVE, European Commission. Retrieved from http://ec.europa.eu/transport/themes/urban/studies/doc/2012-04-urban-freight-transport.pdf. Accessed 9 May 2018
  77. Mohajeri A, Fallah M (2014) Closed-loop supply chain models with considering the environmental impact. Sci World J 2014:852529.  https://doi.org/10.1155/2014/852529 CrossRefGoogle Scholar
  78. Mohajeri A, Fallah M (2014b) Cost minimization model for reducing carbon footprints from different transportation modes. Int J Appl 4(4):49–76Google Scholar
  79. Mohajeri A, Fallah M (2016) A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: a case study. Transp Res D Transport Environ 48:425–450CrossRefGoogle Scholar
  80. Mohammed F, Selim SZ, Hassan A, Syed MN (2017) Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transp Res D Transport Environ 51:146–172CrossRefGoogle Scholar
  81. Monteiro MM, Leal JE, Raupp FMP (2010) A four-type decision-variable MINLP model for a supply chain network design. Math Probl Eng 2010:1–16zbMATHCrossRefGoogle Scholar
  82. Musavi MM, Bozorgi-Amiri A (2017) A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Comput Ind Eng 113:766–778CrossRefGoogle Scholar
  83. Nightingale A (2009) A guide to systematic literature reviews. Surgery (Oxford) 27(9):381–384CrossRefGoogle Scholar
  84. Nouira I, Hammami R, Frein Y, Temponi C (2016) Design of forward supply chains: impact of a carbon emissions-sensitive demand. Int J Prod Econ 173:80–98CrossRefGoogle Scholar
  85. 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):493–506CrossRefGoogle Scholar
  86. Ogden KW (1992) Urban goods movement: a guide to policy and planning. Ashgate, Hants, EnglandGoogle Scholar
  87. Paksoy T, Bektaş T, Özceylan E (2011) Operational and environmental performance measures in a multi-product closed-loop supply chain. Transp Res E Logist Transp Rev 47(4):532–546CrossRefGoogle Scholar
  88. Passchier-Vermeer W, Passchier WF (2000) Noise exposure and public health. Environ Health Perspect 108(suppl 1):123–131CrossRefGoogle Scholar
  89. Pishvaee MS, Razmi J, Torabi SA (2012a) Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Syst 206:1–20MathSciNetzbMATHCrossRefGoogle Scholar
  90. Pishvaee MS, Torabi SA, Razmi J (2012b) Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Comput Ind Eng 62:624–632CrossRefGoogle Scholar
  91. Pradenas L, Oportus B, Parada V (2013) Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Syst Appl 40(8):2985–2991CrossRefGoogle Scholar
  92. Puettmann C, Stadtler H (2010) A collaborative planning approach for intermodal freight transportation. OR Spectrum 32(3):809–830zbMATHCrossRefGoogle Scholar
  93. Qiu Y, Qiao J, Pardalos PM (2017) A branch-and-price algorithm for production routing problems with carbon cap-and-trade. Omega 68:49–61CrossRefGoogle Scholar
  94. Quak H (2008) Sustainability of urban freight transport: retail distribution and local regulations in cities (No. EPS-2008-124-LIS)Google Scholar
  95. Quak H (2011) Urban freight transport: the challenge of sustainability. In: Macharis C, Melo S (eds) City distribution and urban freight transport: multiple perspectives. Edward Elgar Publishing, pp 37–55Google Scholar
  96. Ranieri L, Digiesi S, Silvestri B, Roccotelli M (2018) A review of last mile logistics innovations in an externalities cost reduction vision. Sustainability 10(3):782CrossRefGoogle Scholar
  97. Rao C, Goh M, Zhao Y, Zheng J (2015) Location selection of city logistics centers under sustainability. Transp Res D 36:29–44CrossRefGoogle Scholar
  98. Richardson BC (2005) Sustainable transport: analysis frameworks. J Transp Geogr 13(1):29–39CrossRefGoogle Scholar
  99. Rudi A, Frohling M, Zimmer K, Schultmann F (2016) Freight transportation planning considering carbon emissions and in-transit holding costs: a capacitated multi-commodity network flow model. EURO J Transp Logist 5(2):123–160CrossRefGoogle Scholar
  100. Saberi M, Verbas İÖ (2012) Continuous approximation model for the vehicle routing problem for emissions minimization at the strategic level. J Transp Eng 138(11):1368–1376CrossRefGoogle Scholar
  101. Sadegheih A, Drake PR, Li D, Sribenjachot S (2011) Global supply chain management under the carbon emission trading program using mixed integer programming and genetic algorithm. Int J Eng Trans B 24(1):37–53Google Scholar
  102. Sadjady H, Davoudpour H (2012) Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs. Comput Oper Res 39(7):1345–1354MathSciNetzbMATHCrossRefGoogle Scholar
  103. Sahebjamnia N, Fard AMF, Hajiaghaei-Keshteli M (2018) Sustainable tire closed-loop supply chain network design: hybrid metaheuristic algorithms for large-scale networks. J Clean Prod 196:273–296CrossRefGoogle Scholar
  104. Seuring S, Sarkis J, Müller M, Rao P (2008) Sustainability and supply chain management—an introduction to the special issue. J Clean Prod 16:1545–1551CrossRefGoogle Scholar
  105. Shefer D, Rietveld P (1997) Congestion and safety on highways: towards an analytical model. Urban Stud 34(4):679–692CrossRefGoogle Scholar
  106. Soleimani H, Govindan K, Saghafi H, Jafari H (2017) Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput Ind Eng 109:191–203CrossRefGoogle Scholar
  107. SteadieSeifi M, Dellaert NP, Nuijten W, Van Woensel T, Raoufi R (2014) Multimodal freight transportation planning: a literature review. Eur J Oper Res 233(1):1–15zbMATHCrossRefGoogle Scholar
  108. Suzuki Y (2011) A new truck-routing approach for reducing fuel consumption and pollutants emission. Transp Res D Transport Environ 16(1):73–77CrossRefGoogle Scholar
  109. Suzuki Y (2016) A dual-objective metaheuristic approach to solve practical pollution routing problem. Int J Prod Econ 176:143–153CrossRefGoogle Scholar
  110. Szeto WY, Jiang Y, Wang DZW, Sumalee A (2015) A sustainable road network design problem with land use transportation interaction over time. Netw Spat Econ 15(3):791–822MathSciNetzbMATHCrossRefGoogle Scholar
  111. Taniguchi E, Thompson RG, Yamada T (2014) Recent trends and innovations in modelling city logistics. Procedia Soc Behav Sci 125:4–14CrossRefGoogle Scholar
  112. Tavasszy LA, Ruijgrok K, Davydenko I (2012) Incorporating logistics in freight transport demand models: state-of-the-art and research opportunities. Transport Rev 32(2):203–219CrossRefGoogle Scholar
  113. Teypaz N, Schrenk S, Cung VD (2010) A decomposition scheme for large-scale service network design with asset management. Transp Res E Logist Transp Rev 46(1):156–170CrossRefGoogle Scholar
  114. Thompson RG, Zhang L (2018) Optimising courier routes in central city areas. Transp Res C Emerging Technol 93:1–12CrossRefGoogle Scholar
  115. Ubeda S, Arcelus FJ, Faulin J (2011) Green logistics at Eroski: a case study. Int J Prod Econ 131(1):44–51CrossRefGoogle Scholar
  116. Validi S, Bhattacharya A, Byrne PJ (2014) Integrated low- carbon distribution system for the demand side of a product distribution supply chain: a DOE-guided MOPSO optimizer-based solution approach. Int J Prod Res 52(10):3074–3096CrossRefGoogle Scholar
  117. Verma M, Verter V (2010) A lead-time based approach for planning rail–truck intermodal transportation of dangerous goods. Eur J Oper Res 202(3):696–706zbMATHCrossRefGoogle Scholar
  118. Visser J, Van Binsbergen A, Nemoto T (1999) Urban freight transport policy and planning. In: Taniguchi E, Thompson RG (eds) First International Symposium on City Logistics. Institute of Systems Science Research, Kyoto, Japan, pp 39–69Google Scholar
  119. Visser J, Nemoto T, Browne M (2014) Home delivery and the impacts on urban freight transport: a review. Procedia Soc Behav Sci 125:15–27CrossRefGoogle Scholar
  120. Wadud Z, Aye L, Beer T, Watson H (2006) Modeling Australian road transport emissions till 2025. J Civ Eng 34(2):115–127Google Scholar
  121. Wang C, Quddus MA, Ison SG (2009) Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England. Accid Anal Prev 41(4):798–808CrossRefGoogle Scholar
  122. Wang F, Lai X, Shi N (2011) A multi-objective optimization for green supply chain network design. Decis Support Syst 51(2):262–269CrossRefGoogle Scholar
  123. Wanke P, Correa P, Jacob J, Santos T (2015) Including carbon emissions in the planning of logistic networks: a Brazilian case. Int J Shipp Transport Logist 7(6):655–675CrossRefGoogle Scholar
  124. Wong EY, Tai AH, Zhou E (2018) Optimising truckload operations in third-party logistics: a carbon footprint perspective in volatile supply chain. Transp Res D Transport Environ 63:649–661CrossRefGoogle Scholar
  125. World Business Council for Sustainable Development, World Resources Institute (2001) The greenhouse gas protocol: a corporate accounting and reporting standard. World Resources Institute, Washington, DCGoogle Scholar
  126. World Urbanization Prospects (2018) The 2018 revision. Retrieved from http://esa.un.org/unpd/wup/Publications/Files/WUP2018-KeyFacts.pdf. Accessed 10 Apr 2018
  127. Xu M, Lam WH, Gao Z, Grant-Muller S (2016) An activity-based approach for optimisation of land use and transportation network development. Transp B Transport Dyn 4(2):111–134Google Scholar
  128. Zhalechian M, Tavakkoli-Moghaddam R, Zahiri B, Mohammadi M (2016) Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transp Res E Logist Transp Rev 89:182–214CrossRefGoogle Scholar
  129. Zhu W, Erikstad SO, Nowark MP (2014) Emission allocation problems in the maritime logistics chain. EURO J Transp Logist 3(1):35–54CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity “Federico II” of NaplesNaplesItaly
  2. 2.Department of Electrical Engineering and Information TechnologyUniversity “Federico II” of NaplesNaplesItaly

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