Ranking of Logistics System Scenarios for Central Business District

  • Snežana Radoman Tadić Faculty of Transport and Traffic Engineering, University of Belgrade
  • Slobodan Marko Zečević Faculty of Transport and Traffic Engineering, University of Belgrade
  • Mladen Dragan Krstić Faculty of Transport and Traffic Engineering, University of Belgrade
Keywords: city logistics, central business district, logistics system scenario, MCDM,

Abstract

This paper presents the procedure for logistics system scenario selection for the central business district (CBD) of the city in the phase of significant urban changes. Scenarios are defined in accordance with the overall logistics concept of the city. Conflicting goals of stakeholders (residents, shippers and receivers, logistics providers and city government) generate a vast number of criteria that need to be included when selecting the scenario for the city area logistics system. Due to limited resources and linguistic assessment of criteria, fuzzy extensions of conventional multi-criteria decision-making (MCDM) methods were used. Fuzzy 'analytical hierarchy process' (FAHP) is applied to determine the relative weights of evaluation criteria, and fuzzy 'technique for order preference by similarity to ideal solution' (FTOPSIS) is applied to rank the logistics systems scenarios. This paper contributes to the literature in the field of city logistics (CL), as it applies the integrated FAHP-FTOPSIS method for the evaluation of scenarios, which are also integrated combinations of different CL initiatives. The integrated combined approach proved to be accurate, effective and a systematic tool for the decision support in the process of selecting CBD logistics scenarios.

Author Biographies

Snežana Radoman Tadić, Faculty of Transport and Traffic Engineering, University of Belgrade
Department of Logistics, Research and Teaching Assistant
Slobodan Marko Zečević, Faculty of Transport and Traffic Engineering, University of Belgrade
Department of Logistics, Professor
Mladen Dragan Krstić, Faculty of Transport and Traffic Engineering, University of Belgrade
Department of Logistics, Research and Teaching Assistant

References

Dablanc L. Goods transport in large European cities: Difficult to organize, difficult to modernize. Transportation Research Part A. 2007;41:280-285.

Master Plan of Belgrade 2021 [Internet]. Belgrade: Urban Planning Institute of Belgrade; 2003 [cited 2013 Jul 22]. Available from: http://www.urbel.com/documents/planovi/4231(sl%20l%2027-03)

Zečević S. Razvojni koncept logističkog sistema na području Ada Huje. Expert Research. Belgrade: Urban Planning Institute of Belgrade; 2006.

Dablanc L, Rakotonarivo D. The impacts of logistic sprawl: How does the location of parcel transport terminals affect the energy efficiency of goods’ movements in Paris and what can we do about it? Procedia, Social and Behavioral Sciences. 2010;2(3):6087-6096.

Zadeh LA. Fuzzy set. Information and Control. 1965;8(3):338-353.

Sheu JB. A hybrid fuzzy-based approach for identifying global logistics strategies. Transportation Research Part E. 2004;40:39–61.

Onut S, Soner S. Transshipment site selection using AHP and TOPSIS approaches under fuzzy environment. Waste Management. 2008;28:1552-1559.

Tilahun SL, Ong HC. Bus Timetabling as a Fuzzy Multiobjective optimization problem using preference-based Genetic Algorithm. Promet - Traffic&Transportation. 2012;24(3):183-191.

Saaty TL. The Analytic Hierarchy Process. New York, NY: McGraw-Hill International; 1980.

Van Laarhoven PJM, Pedrycz W. A fuzzy extension of saaty’s priority theory. Fuzzy Set and Systems. 1983;11:229–41.

Wang YM, Chin KS. Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. International Journal of Approximate Reasoning. 2011;52:541–553.

Mikhailov L. Deriving priorities from fuzzy pairwise comparison judgments. Fuzzy Sets and Systems. 2003;134:365-385.

Hwang CL, Yoon K. Multiple attributes decision making methods and applications. Berlin: Springer; 1981.

Chen C. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems. 2000;114:1–9.

Lo CC, Cheng DY, Tsai CG, Chao KM. Service Selection Based on Fuzzy TOPSIS Method. Proceedings of 24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010, Perth, Australia; 20-13 April 2010.

Tran L, Duckstein L. Comparison of fuzzy numbers using a fuzzy distance measure. Fuzzy Sets and Systems. 2002;130:331–341.

Zhou HC, Wang GL, Yang Q. A multi-objective fuzzy recognition model for assessing groundwater vulnerability based on the DRASTIC system. Hydrological Sciences Journal. 1999;44:611–618.

Published
2014-04-27
How to Cite
1.
Tadić SR, Zečević SM, Krstić MD. Ranking of Logistics System Scenarios for Central Business District. Promet - Traffic&Transportation. 2014;26(2):159-67. DOI: 10.7307/ptt.v26i2.1349
Section
Articles