The process of urbanization of coastal tourist destinations (CTDs) is taking place at high speed and at the same time creating a lot of complex problems. The positive trend of urbanization has resulted in increased volume of freight transport which leads to burdening the traffic network, time losses and causes traffic congestion problems on the streets with increased environmental pollution due to emissions, noise and vibration. These findings brought to some research being started on the EU level, aiming to develop new logistic solutions, so these areas could be developed on a sustainable basis. With this in mind, the paper proposes a method of developing a novel model of logistics (MoL) for CTDs through several stages. The point of proposed MoL lies in achieving optimal connectivity of transportation, warehousing and physical distribution of goods, and making it a single functional model, so as to allow simultaneous optimization of logistic processes in a CTD, and to incorporate logistics in tourist offer.
Krugman P. Development, Geography and Economic Theory. Cambridge Mass and London: The MIT Press; 1995.
Ivanović Ž. One approach to the development of models of logistics of tourist coastal regions. The 1st Logistics International Conference, LOGIC. Belgrade, Serbia; 2013.
Likas A, Vlassis N, Verbeek J. The global k-means clustering algorithm. Pattern Recognition. 2003;36:451-461.
Duda R.O. Feature selection and clustering for HCI [Internet, cited 2014 February 1]; 1996-2007. Available from: https://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/k_means.htm
Husein N. A fast greedy k-means algorithm [Master’s thesis]. Amsterdam, Netherlands: University of Amsterdam; 2002 [cited 2014 February 1]. Available from: www.science.uva.nl/.../NoahLaith.doc
Faber V. Clustering and the continuous k-means algorithm. Los Alamos Science. 1994;22:138-144.
Racunica I, Wynter L. Optimal location of intermodal freight hubs. Rapport de recherché n 4088. Unit´e de recherche INRIA Rocquencourt; December 2000 [cited 2014 February 8]. Available from: http://hal.archives-ouvertes.fr/docs/00/07/25/45/PDF/RR-4088.pdf
Mehrjendi YZ, Nadizadeh A. Using Greedy Clustering Method to Solve Capacitated Location Routing problem with Fuzzy Demands. European Journal of Operational Research. 2013;299(1):75-84.
Najaf P, Famili S. Application of an Intelligent Fuzzy Regression Algorithm in Road Freight Transportation Modelling. Promet – Traffic&Transportation. 2013;25(4):311-322.
Kratica J, Milanović M, Stanimirović Z, Tošić D. An evolutionary based approach for solving a capacitated hub location problem. Applied Soft Computing, ASC. 2011;11(2):1858-1866.
Crainic TG, Mancini S, Perboli G, Tadei R. Multistart heuristics for the two echelon vehicle routing problem. Lecture Notes in Computer Science. 2011;6622:179-190.
Crainic TG, Ricciardi N, Storchi G. Advanced freight transportation systems for congested urban areas. Transportation Research Part C. 2004;12:119-137.
Jacobsen S, Madsen O. A comparative study of heuristics for a two level routing location problem. European Journal of Operational Research. 1980;5:378-387.
Nagy G, Salhi S. Location routing: Issues, models and methods. European Journal of Operational Research. 2007;177:649–672.
Nakamura Y, Taniguchi E, Yamada T, Ando N. Selecting a dynamic and stochastic path method for vehicle routing and scheduling problems. Procedia - Social and Behavioral Sciences. 2010;2(3):6042-6052.
Perboli G, Tadei R, Vigo D. The two echelon capacitated vehicle routing problem: models and mathbased heuristics. Transportation Science. 2011;45:364-380.
Perboli G, Pezzella F, Tadei R. EVEOPT: a hybrid algorithm for the capability vehicle routing Problem. Mathematical Methods of Operations Research. 2008;68:361-382.
Quak H, De Koster R. The impacts of time access restrictions and vehicle weight restrictions on food retailers and the environment. European Journal of Transport and Infrastructure Research. 2005;6(2):131-150.
Quak H, De Koster R. Delivering goods in urban areas: How to deal with urban policy restrictions and the environment. Transportation Science. 2009;43(2):211-227.
Groer C, Golden B, Wasil E. A library of local search heuristics for the vehicle routing problem. Mathematical Programming Computation. 2010;2(2):79-101.
Homberger J, Gehring H. A two phase hybrid metaheuristic for the vehicle routing problem with time windows. European Journal of Operational Research. 2005;162:220-238.
Taniguchi E, Yamada T, Tamagawa D. Probabilistic vehicle routing and scheduling on variable travel times with dynamic traffic simulation. In: Taniguchi E, Thompson RG, editors. City Logistics I. Kyoto: Institute for City Logistics; 1999. p. 8599.
Qureshi AG, Taniguchi E, Yamada T. Exact solution for the vehicle routing problem with semi soft time windows and its application. Procedia - Social and Behavioral Sciences. 2010;2(3):5931-5943.
Gonzalez-Feliu J. Models and methods for the city logistics: the two echelon capacitated vehicle routing problem [dissertation]. Torino, Italy: Politecnico di Torino; 2008.
Gonzales-Feliu J. Freight distribution systems with cross docking. Journal of the Transportation Researc Forum, JTRF. 2012;51(1):93-109.
Gonzalez-Feliu J, Ambrosini C, Routhier JL. New trends on urban goods movement: Modelling and simulation of e-commerce distribution. European Transport. 2012;50:6-23.
Russo F, Comi A. A modelling system to simulate goods movements at an urban scale. Transportation. 2010;37(6):987-1009.
Ivanović Lj, Ivanović Ž. City logistics in the Montenegrin coastal region. The 1st Logistics International Conference, LOGIC. Belgrade, Serbia; 2013.
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal