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The efficient use of transportation resources is the foundation of the management concept behind sustainable supply chains. The complexity of distribution supply chains requires the implementation of appropriate decision-making steps during modeling. This is due to the number of supply chain participants, the diversity of processes, and their flow. Thus, an appropriate way to assess the variety of control parameters that define the functionality of participants within sustainable supply chains is required. An extended multi-criteria analysis provides an opportunity to support the decision process of selecting appropriate supply chain elements, based on a scoring system that defines the relevance of the parameters. The developed method of multi-criteria evaluation of the attractiveness of carrier offers can be applied to urban logistics conditions. Due to limited access to data on carrier offers and their range of services under urban conditions, it seems appropriate to translate the experiences and conclusions from the consolidation of deliveries and the sharing of long-distance routes into urban transportation logistics. The effectiveness of the selection of supply-chain road-transportation service providers has been the subject of a comparative analysis of the types and parameters of the process within the research. The principles of the multi-criteria decision-making (MCDM) approach are considered, ncluding identification of the process, determination of process requirements, establishment of objectives, consideration of alternative solutions, and identification of the operational framework. The variant approach proposed within this study allows us to verify the impact of road transportation conditions on overall efficiency. The performer analysis enables a choice between full truckload (FTL) and less-than-truckload (LTL) types of road transport. The results of this study support the decision-making process in the selection of road transport service providers. Conclusions are valuable also from the organization of city transportation models, as the logic behind efficiency assessment is comparable in both operational environments. A formulated set of recommendations can be implemented within the organization with a focus on optimizing the use of road transport solutions.
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Tom
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39--48
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
- Łukasiewicz Research Network, Poznań Institute of Technology 61-755 Poznań, Poland
autor
- Łukasiewicz Research Network, Poznań Institute of Technology 61-755 Poznań, Poland
- Poznan University of Technology, Faculty of Engineering Management 60-965 Poznań, Poland
autor
- Poznan School of Logistics
Bibliografia
- 1. Abdulla, M.F. & Musa, H. (2021) Mediation model of logistics service supply chain (LSSC) factors affecting organisational performance. International Journal of Sustainable Construction Engineering and Technology 12(5), doi: 10.30880/ijscet.2021.12.05.030.
- 2. Abdullah, M.I., Sarfraz, M., Qun, W. & Javaid, N. (2018) Drivers of green supply chain management. Logforum 14(4), pp. 437–447, doi: 10.17270/J.LOG.2018.297.
- 3. Boysen, N. & Fliedner, M. (2010) Cross dock scheduling: Classification, literature review and research agenda. Omega 38(6), pp. 413–422, doi: 10.1016/j.omega.2009.10.008.
- 4. Camargo Pérez, J., Carrillo, M.H. & Montoya-Torres, J.R. (2015) Multi-criteria approaches for urban passenger transport systems: A literature review. Annals of Operations Research 226 (1), pp. 69–87, doi: 10.1007/s10479-014-1681-8.
- 5. Caputo, A.C., Fratocchi, L. & Pelagagge, P.M. (2006) A genetic approach for freight transportation planning. Industrial Management & Data Systems 106(5), pp. 719–738, doi: 10.1108/02635570610666467.
- 6. Dubisz, D., Golinska-Dawson, P. & Zawodny, P. (2022) Measuring CO2 emissions in e-commerce deliveries: From empirical studies to a new calculation approach. Sustainability 14(23), 16085, doi: 10.3390/su142316085.
- 7. Ehsani, M., Ahmadi, A. & Fadai, D. (2016) Modeling of vehicle fuel consumption and carbon dioxide emission in road transport. Renewable and Sustainable Energy Reviews 53, pp. 1638–1648, doi: 10.1016/j.rser.2015.08.062.
- 8. Hagerer, I. (2019) Universities act differently: Identification of organizational effectiveness criteria for faculties. Tertiary Education and Management 25(3), pp. 273–287, doi: 10.1007/s11233-019-09031-2.
- 9. Khayyat, M. & Awasthi, A. (2016) An intelligent multiagent based model for collaborative logistics systems. Transportation Research Procedia 12, pp. 325–338, doi: 10.1016/j.trpro.2016.02.069.
- 10. Korpinen, O.-J., Aalto, M. & Ranta, T. (2019) Impacts of a high-capacity truck transportation system on the economy and traffic intensity of pulpwood supply in Southeast Finland. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering 40 (1), pp. 89–105.
- 11. Mouronte-López, M.L. (2021) Modeling the public transport networks: A study of their efficiency. Complexity 2021, pp. 1–19, doi: 10.1155/2021/3280777.
- 12. Nie, L., Xu, X. & Zhan, D. (2006) Incorporating transportation costs into JIT lot splitting decisions for coordinated supply chains. Journal of Advanced Manufacturing Systems 05 (01), pp. 111–121, doi: 10.1142/S0219686706000741.
- 13. Olsson, J., Hellström, D. & Pålsson, H. (2019) Framework of last mile logistics research: a systematic review of the literature. Sustainability 11(24), 7131, doi: 10.3390/su11247131.
- 14. Reggiani, A. (2013) Network resilience for transport security: Some methodological considerations. Transport Policy 28, pp. 63–68, doi: 10.1016/j.tranpol.2012.09.007.
- 15. Rudi, A., Fröhling, 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 Journal on Transportation and Logistics 5(2), pp. 123–160, doi: 10.1007/s13676-014-0062-4.
- 16. Spengler, T. (2016) Energy Consumption and Energy Efficiency Indicators in Container Terminalsa National Inventory. UN-ECLAC.
- 17. Vega, D.A.S.D.L., Lemos, P.H., Silva, J.E.A.R.D. & Vieira, J.G.V. (2021) Criteria analysis for deciding the LTL and FTL modes of transport. Gestão & Produção 28(2), e5065, doi: 10.1590/1806-9649-2020v28e5065.
- 18. Wang, Y. & Wang, S. (2021) Deploying, scheduling, and sequencing heterogeneous vessels in a liner container shipping route. Transportation Research Part E: Logistics and Transportation Review 151, 102365, doi: 10.1016/j. tre.2021.102365.
- 19. Yannis, G., Kopsacheili, A., Dragomanovits, A. & Petraki, V. (2020) State-of-the-art review on multicriteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering (English Edition) 7(4), pp. 413–431, doi: 10.1016/j.jtte.2020.05.005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7160cf7e-a5da-4c58-889d-520415fbb801