SELECTION OF THE INTERMODAL TRANSPORT CHAIN VARIANT USING AHP METHOD Slobodan Zežević1, Snežana Tadić2, Mladen Krstić3 1,2,3
University of Belgrade, Faculty of Transport and Traffic Engineering, Serbia
Abstract: In the increasingly competitive world market freight transport faces demands for overcoming large distances while improving reliability and sustainability. Most of this transport is carried by road which generates various negative effects. On the other hand, intermodal transport allows the combination of different modes of transport, using the advantages of each of them. One of the problems in this area of research is the definition of the adequate transport modes and technologies combination, as well as the transport routes. Accordingly, the aim of this paper is to select the most favorable variant for the realization of the intermodal transport chain. As the decision-making process includes numerous conflicting criteria this is a problem of multi-criteria decisionmaking (MCDM) and must be accordingly addressed by the appropriate methods. The analytical hierarchy process (AHP) method is used in this paper and the applicability of the method for solving the defined problem is demonstrated by solving the case study of selecting the appropriate variant for the realization of the part of the intermodal transport chain from the port of Hamburg to trimodal terminal in Belgrade. Keywords: intermodal transport chain, multi-criteria decision-making, AHP method.
1. Introduction Increasing specialization and internationalization of the world trade leads to the increasing distances between suppliers, manufacturers and end users and the increase in the volume of world transport operations (OECD, 2010, UNCTAD, 2013). In the European Union, about 1,700 tons-kilometers of goods is transported in 2014, with the share of road transport of about 75% (Eurostat, 2015). A high share of road transport can be explained by the relative dense transport network that provides door-to-door transport and the high flexibility of route planning (Kummer, 2006). But the growing volume of road transport contributes to increasing road congestions, delays and other negative impacts on the reliability of transport (European Commission, 2012). In addition, transport is one of the main causes of harmful gases emission, noise and other negative impacts on the environment (European Commission, 2014). Consequently, companies are looking for alternative transport options that will enable them to reduce negative impacts of road transport and improve economic and environmental performance of their distribution systems (e.g. Demir et al., 2015). As a result, more and more researches are analyzing the negative impact of transport along the supply chain, with the aim of determining the consequences and ways to reduce them. The research results lead to the creation of policies that promote more intensive use of other modes of transport (rail, waterway) as well as the more intensive application of intermodal transport, which makes it possible to exploit the advantages of each mode of transport but also to eliminate their negative sides. In the literature one can find different definitions or intermodal transport but the most widely accepted definition is that intermodal transport is movement of goods, in one and the same loading unit or a vehicle, by successive modes of transport without handling of the goods themselves when changing modes (ECMT, 1993). Various problems regarding intermodal transport are analyzed in the literature, such as the complementarities of different transport modes, changes in the price policy in intermodal systems, management of the flows between different modes of transport and their potential environmental impact, etc. (Sawadogo & Anciaux, 2010). One of the problems faced by planners is to select the optimal route of goods transportation from the starting point (e.g. place of manufacture or supplier warehouse) to the endpoint (e.g. distribution center or retail warehouse). This is exactly the problem analyzed in this paper but in such a way to optimize the cost, time and performance while reducing environmental and social impacts. The aim is to support decision-makers (e.g. logistics operators) when selecting an appropriate route in the intermodal network, taking into consideration not only the criteria of time and costs, but also environmental and social criteria, and thus enabling them to choose the route, among the set of possible variants, that achieves a compromise between the benefits and negative impacts. As the achievement of this compromise implies consideration of different, mutually conflicting criteria, the problem is defined as a multi-criteria decision-making problem, and accordingly requires the application of appropriate method for its solution. The paper is organized as follows. The second chapter describes in more detail the problem analyzed in this paper, i.e. the selection of the best variant for the realization of the part of the intermodal transport chain, as well as the criteria used in the decision making process. The third chapter describes the decision making method, AHP method, which is used for solving the case study. The fourth chapter describes the case study and presents the obtained results. Fifth chapter gives the concluding remarks. 2. Intermodal transport chain variant selection When talking about transport chains optimization, most talk about the problems of finding the shortest or most economical route between the initial and final terminal, reducing transport costs while performing the delivery within a 3
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reasonable period of time, reducing the cost of routing for each delivery in relation to the total cost of transport and inventories, finding the optimal mix of transport alternatives to reduce overall logistics costs, comparing different forms of transport and technologies in relation to the internal and external costs, etc. (Sawadogo & Anciaux, 2010). A special class of problems are the problems of selecting the routes in intermodal transport networks (e.g. Qu & Chen, 2008) and this is exactly the problem discussed in this paper. The aim is to choose the best variant of intermodal transport chain realization for a set of criteria. The problem is defined as the transport of intermodal transport units - Twenty-Feet Equivalent Units (TEUs), i.e. q pallets of goods (where qu is the number of pallets in one TEU), along the road r, from the starting point O to the end point D. Every route r is the possible variant V and can be composed of multiple sections i (i=1,...,n). Each section represents part of the route with the length d, while µ represents the number of vehicles of a certain transport mode m used for the transportation of goods. The selection of variant is based on the following criteria: transportation costs (C1), transportation time (C2), particle emissions (C3), noise emissions (C4), energy consumption (C5), traffic congestion (C6), transshipment time (C7 ), transshipment damages (C8) and accidents (C9), which are described in more detail below. C1: Transportation costs. Transportation costs include fixed costs, fleet personnel costs and variable costs that depend on the traveled distance (fuels, tolls, etc.), and can be expressed by the following equation:
C1 c1im ti c2im di c3im im n
(1)
i 1
where c1im denotes fixed costs of using transport mode m, c2im costs of hiring fleet personnel for the transport mode m, c3im costs of the distance traveled by the transport mode m, ti transportation time on the section i, di length of the section i, and µim number of transport means m used on the section i. C2: Transportation time. The criterion includes driving time and does not include time losses that may arise due to the border and customs control, traffic congestion, the regulations on the duration and the period of transport, etc. The transportation time is obtained by using the following equation: n
C 2 di / vim * im
(2)
i 1
where vim denotes the velocity of vehicles of the transport mode m on route section i. C3: Particle emissions. The emission of harmful particles into the air by means of transport can be calculated using the following equation: n
g
C 3 u di iem
(3)
i 1 e1
where ρiem denotes the quantity of emitted harmful particles e (e=1,...,g) on route section i by the transport mode m. C4: Noise emissions. Noise emission can be displayed using the following equation: n
C 4 u di im
(4)
i 1
where βim denotes average costs of the noise emission for the transport mode m. C5: Energy consumption. Energy consumption can be obtained by using the following equation: n
C 5 u di im
(5)
i 1
where εim denotes the energy consumption of the transport mode m. C6: Traffic congestion. Traffic congestion can be obtained by using the following equation: n
C 6 u di im
(6)
i 1
where χim denotes average costs of the traffic congestion for the transport mode m. C7: Transshipment time. The transshipment time includes all times necessary for loading and unloading pallets to/from the transport unit and the times of transshipment TEU in breaking points, and can be obtained using the following equation: (7) C7 u u 2 2br q q where Δu denotes average time for transshipment of one TEU, Δq average time for transshipment of one pallet, br number of breaking points on the route r. C8: Transshipment damages. The cost of damaging the goods during transshipment can be obtained using the following equation: (8) C8 u u pu 2 2br q q * pq where δu denotes the average costs of damaging the goods during the transshipment of one TEU, δu average costs of damaging the goods during the transshipment of one pallet, pu the probability of damaging the goods during the transshipment of one TEU, and pq the probability of damaging the goods during the transshipment of one pallet. C9: Accidents. Costs incurred as a result of an accident can be obtained using the following equation: n
C 9 u di im
(9)
i 1
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where αim denotes average costs arising from accidents during transportation by the transport mode m. For the calculation of the variants values in relation to the criteria, it is necessary to define the values of the parameters that appear in the previously defined equations (Table 1). Table 1 Values of the parameters for the calculation of the variant values in relation to criteria Parameter Units Road Rail Waterway Capacity TEU 2 120 180 c1 € 100 200 200 c2 €/h 20 26.27 26.27 c3 €/h 0.40 0.20 0.10 vm km/h 80 35-100 10 ρCO2 g/TEU.km 1,065 7.7 21.5 ρNox mg/TEU.km 8,130 0.8 24.8 ρNMHC mg/TEU.km 570 0.55 25.22 ρpart. mg/TEU.km 195 13 25.71 βm €/TEU.km 0.112 0.484 0 εm KJ/TEU.km 14,490 12.4 24.8 χm €/TEU.km 0.083 0.003 0 Δu h/TEU 0.3 0.3 0.3 Δq h/pal 0.01 0.01 / σu 1,000€/TEU 15 15 15 σq 1,000€/pal. 0.5 0.5 0.5 αm €/TEU.km 0.081 0.022 0 pu,q probability variable variable variable Source: Adapted from: European Commission – DG Mobility and Transport, 2014, Sawadogo & Anciaux, 2009. The ultimate goal is to find a compromise solution for all the above criteria, which is why there is a need for the use of multi-criteria decision-making methods. For this purpose, AHP method, described in more detail bellow, is used in this paper. 3. AHP method The multi-criteria decision-making problem described in this paper can be solved by using AHP method. AHP method developed by Saaty (1980) deals with the determination of the relative importance of criteria in multi-criteria decisionmaking problems. AHP method is based on three principles: the structure of the model, a comparative analysis of the structural elements (criteria and alternatives), and synthesis of priorities. The first step in applying the method is the formation of the hierarchical structure of the problem being solved. AHP first decompose a complex multi-criteria decision-making problem into a hierarchically arranged elements (objectives, criteria, alternatives). In general, according to the defined problem an analysis is performed to determine the relative weights of criteria at each hierarchical level and value of alternatives against the criteria. This analysis includes pair-wise comparison of all criteria in each hierarchical level as well as the pair-wise comparison of all alternatives against the criteria. The pairwise comparison is performed by applying the standardized nine-point scale (Saaty scale) (Table 2). Table 2 Saaty scale for criteria evaluation Numerical value Linguistic assessment 1 Equal importance 3 Moderate importance 5 Strong importance 7 Very strong importance 9 Extreme importance 2,4,6 i 8 Intermediate values Steps of applying the AHP method are described below. Step 1: Define the problem structure. First, it is necessary to define the elements of the structure, i.e. the objective, alternatives (variants) and the criteria for their prioritization. Step 2: Form a matrix for pair wise comparison of the elements:
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a11 a A 21 a o1
a1o a 2o , a kk 1, alk 1 / a kl , a kl 0 (10) a oo elements of which are akl (k,l=1,2,...,o) and denote the importance of element k in relation to element l. Step 3: Obtain the element weights based on the eigenvector. First, it is necessary to set up a matrix equation: AW max W (11) where W is the element weights matrix, and λmax is the eigenvalue of the matrix A. Equation (11) becomes equation: A max I W 0 (12) where I is the identity matrix (matrix whose elements on the main diagonal have a value of 1). λmax is obtained by solving the equation: det A max I 0 . (13) Based on the value λmax and by transforming the matrix equation (12), the system of linear equations is obtained. By a12 a 22 ao2
o
solving this system of equations, while respecting the condition
wk 1 , the values of the element weights wk are k 1
obtained. Step 4: Determine the consistency of the evaluations. In order to control the results of the method it is necessary to calculate the Consistency Ratio (CR) for each matrix and the overall inconsistency of the hierarchical structure. CR is calculated as follows (Saaty, 1996): (14) CR CI / RI , where CI denotes the Consistency Index and can be calculated as: n CI max (15) n 1 RI denotes the Random Index values of which, for the matrix of different sizes, can be seen in the paper of Saaty (1996). CR is used for checking the consistency of pair wise comparisons and must be less than 0.10. Only then it can be said that the comparisons are acceptable. 4. Case study In Serbia, in 2015, more than half a million tons of goods are imported from the countries of the Far East (National Statistics Institute). From the Far East ports, these goods are delivered in containers to the European ports, from where they are transported to Serbia. Most of the goods are coming into Europe via the northern-European ports, among which the port of Hamburg has a significant role in the realization of these flows. Port of Hamburg had in 2015 a turnover of 8.8 million TEUs, out of which over 50% was achieved with the Far Eastern ports (Port of Hamburg - statistics). Due to all the above, the objective of this paper is to analyse the part of the intermodal transport chain from the port of Hamburg in Germany to trimodal terminal (road, rail, river) in Belgrade, Serbia. For the realization of this transport chain, several variants are defined by combining available modes and different technologies of transport. The problem includes the transport of 1,500t of goods, i.e. 3,000 pallets with the dimensions of 1,000x1,200 mm, whereby each pallet holds 500 kg of goods. 20 feet containers are filled with palletized goods in such a way that 100 containers are needed for transporting the entire quantity of goods, wherein each container holds 30 pallets. For the realization of the transport chain five variants (V) described below are defined (Figure 1).
V1 Hamburg
Belgrade
Hamburg
Belgrade
V2 V3 Hamburg
Budapest
Belgrade
V4 Hamburg
Regensburg
Belgrade
Hamburg
Regensburg
Belgrade
V5 Fig. 1. Transport chain variants
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V1 – Direct road transport. The first variant includes a direct road transport of goods from the port of Hamburg to trimodal terminal in Belgrade. Goods packed in containers arrive to the port of Hamburg by ocean carriers. In the port of Hamburg containers are loaded onto the road transport vehicles that transport them to the Belgrade terminal. As the transportation from Hamburg to Belgrade is performed only by road transport, there are no breaking points. The goods are being unloaded from the containers in the terminal in Belgrade. V2 – Direct railway transport. Second variant implies a direct rail transport from the port of Hamburg to the terminal in Belgrade. Similar to the variant 1, in the port of Hamburg containers are loaded onto railroad cars that transport them to Belgrade, without breaking points. Transport from Hamburg to Prague is performed through the part of the European rail freight corridor "North Sea-Baltic", then through the part of the corridor "Orient" from Prague to Budapest, and at last through B branch of the pan-European Corridor 10 from Budapest to Belgrade. The goods are being unloaded from the containers in the terminal in Belgrade. V3 – Rail-road transport. The third option involves the transport of containers by using rail and road transport. Containers are loaded onto the railroad cars in Hamburg, and then transported from Hamburg to Budapest through the rail freight corridors "North Sea-Baltic" and "Orient". In Budapest, the change from rail to road transport mode occurs, since the section of the route from Budapest to Belgrade does not belong to the network of European rail freight corridors, therefore the costs are higher and speed considerably lower. The goods are being unloaded from the containers in the terminal in Belgrade. V4 – Road-inland waterway transport. The fourth variant involves the transport of containers, loaded in Hamburg on the road freight vehicles, from the port of Hamburg to the port of Regensburg on the Danube. In the port of Regensburg, containers are being transshipped from road transport vehicles to the river barges, and then transported by Danube to the terminal in Belgrade. Containers are being transshipped and the goods unloaded from the containers in the terminal in Belgrade. V5 – Rail-inland waterway transport. In the fifth variant, the containers are being loaded onto the railroad cars in the port of Hamburg and transported to the port of Regensburg. Between the ports of Hamburg and Regensburg there are regular lines of block trains, which transport 5 times a week. In the port of Regensburg, the containers are being transshipped from the railroad wagons onto the river barges and then transported by the Danube to the terminal in Belgrade, where the containers are being transshipped from the barges and the goods unloaded from the containers. The distances traveled in each variant, by each mode of transport, as well as the total distances traveled are shown in Table 3. Table 3 Distances traveled in each variant of the transport chain (km) V1 V2 V3 V4 V5
Road 1,534 / 380 704 /
Railway / 1,505 1,185 / 750
Inland waterway / / / 1,210 1,210
Total 1,534 1,505 1,565 1,914 1,960
First, it is necessary to define the hierarchical structure of the problem, which is shown in Figure 2. Variant selection
Goal
Criteria
Variants
C1
C2
V1
C4
C3
C5
V3
V2
C6
C7
V4
C9
C8
V5
Fig. 2. Hierarchical structure of the problem Afterwards it is necessary to determine the criteria weights. By comparing the pairs of criteria in relation to the objective, ussing the Saaty scale given in Table 2, the resulting matrix A shown in Table 4 is obtained.
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Table 4 Criteria pair wise comparison matrix C1 C2 C3 1.00 3.00 2.00 C1 0.33 1.00 0.50 C2 0.50 2.00 1.00 C3 0.17 0.25 0.20 C4 0.50 2.00 1.00 C5 0.20 0.33 0.25 C6 0.14 0.20 0.16 C7 0.25 0.50 0.33 C8 0.20 0.33 0.25 C9
C4
C5
C6
6.00 4.00 5.00 1.00 5.00 2.00 0.50 3.00 2.00
2.00 0.50 1.00 0.20 1.00 0.25 0.16 0.33 0.25
C7 5.00 3.00 4.00 0.50 4.00 1.00 0.33 2.00 1.00
C8 7.00 5.00 6.00 2.00 6.00 3.00 1.00 4.00 3.00
C9 4.00 2.00 3.00 0.33 3.00 0.50 0.25 1.00 0.50
5.00 3.00 4.00 0.50 4.00 1.00 0.33 2.00 1.00
By solving the equation (13) for the given matrix the largest eigenvalue max 9.205 is obtained. Matrix equation (12) is then transformed into the system of linear equations, and by solving it the values of criteria weights W=(0.277, 0.120, 0.184, 0.033, 0.184, 0.050, 0.023, 0.079, 0.050) are obtained. By applying the equations (14) and (15) the value of CR=0.018 is obtained. As the values of CR is less than 0.10 it can be said that the evaluation is consistent. After the criteria weights are obtained, the calculation of values of the variants in relation to criteria is performed by using the equations (1) – (9) and the parameters shown in Table 1. On the basis of these values, a comparison of all pairs of variants in relation to each criterion is performed by using the Saaty scale given in Table 2. Matrices obtained in this way are shown in Table 5. Table 5 Matrices of pair wise comparisons of variants in relation to criteria C1 V1 V2 V3 V4 V5
V1 1.00 6.00 4.00 3.00 5.00
V2 0.17 1.00 0.33 0.25 0.50
V3 0.25 3.00 1.00 0.50 2.00
V4 0.33 4.00 2.00 1.00 3.00
V5 0.20 2.00 0.50 0.33 1.00
C2 V1 V2 V3 V4 V5
V1 1.00 5.00 3.00 2.00 4.00
V2 0.20 1.00 0.33 0.25 0.50
V3 0.33 3.00 1.00 0.50 2.00
V4 0.50 4.00 2.00 1.00 3.00
V5 0.25 2.00 0.50 0.33 1.00
C3 V1 V2 V3 V4 V5
V1 1.00 7.00 6.00 5.00 7.00
V2 0.14 1.00 0.50 0.33 1.00
V3 0.17 2.00 1.00 0.50 2.00
V4 0.20 3.00 2.00 1.00 3.00
V5 0.14 1.00 0.50 0.33 1.00
C4 V1 V2 V3 V4 V5
V1 1.00 0.17 0.20 4.00 0.33
V2 6.00 1.00 2.00 9.00 4.00
V3 5.00 0.50 1.00 8.00 3.00
V4 0.25 0.11 0.13 1.00 0.17
V5 3.00 0.25 0.33 6.00 1.00
C5 V1 V2 V3 V4 V5
V1 1.00 5.00 4.00 3.00 5.00
V2 0.20 1.00 0.50 0.33 1.00
V3 0.25 2.00 1.00 0.50 2.00
V4 0.33 3.00 2.00 1.00 3.00
V5 0.20 1.00 0.50 0.33 1.00
C6 V1 V2 V3 V4 V5
V1 1.00 7.00 5.00 4.00 8.00
V2 0.14 1.00 0.33 0.25 2.00
V3 0.20 3.00 1.00 0.50 4.00
V4 0.25 4.00 2.00 1.00 5.00
V5 0.13 0.50 0.25 0.20 1.00
C7 V1 V2 V3 V4 V5
V1 1.00 1.00 0.50 0.50 0.50
V2 1.00 1.00 0.50 0.50 0.50
V3 2.00 2.00 1.00 1.00 1.00
V4 2.00 2.00 1.00 1.00 1.00
V5 2.00 2.00 1.00 1.00 1.00
C8 V1 V2 V3 V4 V5
V1 1.00 1.00 0.50 0.33 0.33
V2 1.00 1.00 0.50 0.33 0.33
V3 2.00 2.00 1.00 0.50 0.50
V4 3.00 3.00 2.00 1.00 1.00
V5 3.00 3.00 2.00 1.00 1.00
C9 V1 V2 V3 V4 V5
V1 1.00 4.00 3.00 3.00 6.00
V2 0.25 1.00 0.50 0.50 2.00
V3 0.33 2.00 1.00 1.00 3.00
V4 0.33 2.00 1.00 1.00 3.00
V5 0.17 0.50 0.33 0.33 1.00
By applying the equation (13) and by solving the system of equations obtained by transforming the equation (12) for each matrix in Table 5, the preference values of variants in relation to each criteria are obtained. These values, as well as the final weighted preference values of variants are shown in Table 6. Table 6 Preference values of variants and final ranking Criterion C1 C2 C3 C4 0.277 0.120 0.184 0.033 Weight 0.049 0.062 0.036 0.231 V1 0.420 0.419 0.325 0.037 V2 0.164 0.160 0.192 0.055 V3 0.102 0.097 0.121 0.562 V4 0.265 0.263 0.325 0.114 V5
C5 0.184 0.053 0.322 0.188 0.115 0.322
C6 0.050 0.036 0.295 0.135 0.089 0.445
C7 0.023 0.286 0.286 0.143 0.143 0.143
C8 0.079 0.313 0.313 0.176 0.098 0.098
C9 0.050 0.056 0.247 0.141 0.141 0.415
Preference value 0.0809 0.3453 0.1674 0.1244 0.2818
Rank 5 1 3 4 2
Based on the results, it can be seen that the variant 2, which involves the railway transport of containers from the port of Hamburg to the trimodal terminal in Belgrade, proved to be the most favorable one in terms of the considered criteria. On the other hand, the least favorable variant is the variant of road transport of containers. In addition, all variants which included road transport were worse than the variants without the road transport, which confirms the thesis that (in
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terms of the considered criteria) it is more favorable to shift the goods flows from road to other modes of transport (railway and inland waterway) as much as possible. 5. Conclusion In most developed countries, most of the freight transport is performed by road transport, while railway and inland waterway transport are either underutilized or very disorganized in terms of functioning and coordination with other modes of transport. Road freight transport recorded a steady increase in the past few decades. This has led to serious overload of road networks without significant improvement of existing infrastructure resulting in many negative effects such as higher external costs, traffic congestion, increased energy consumption, negative environmental impacts, etc. These negative effects can be significantly mitigated through the efficient organization of intermodal transport system. Such systems can dramatically improve the utilization of transport resources and services, which leads to better planning and deliveries with lower logistics costs and higher levels of efficiency, and one of the ways to achieve these results is adequate planning of transport chains. The subject of this paper was the planning of one part of the transport chain, from Hamburg to Belgrade, i.e. the selection of the best variant of realization of the chain in relation to the defined criteria. As the problem involves consideration of a large number of criteria, it was necessary to apply a multicriteria decision-making method for its solution, and accordingly in this paper the AHP method is used for this purpose. The results clearly show that minimization of the use of road transport, with greater use of other modes of transport (such as railway and inland waterway) lead to better results in terms of the considered criteria, whereby in the case considered in this paper, the variant that included the railway transport of containers is obtained as the best one. References Bayernhafen Regensburg - press release. Available from Internet: Demir, E.; Huang, Y.; Scholts, S.; Van Woensel, T. 2015. A selected review on the negative externalities of the freight transportation: modeling and pricing, Transportation Research Part E: Logistics and Transportation Review, 77: 95114. ECMT - European Conference of Ministers of Transport, 1993. Terminology on combined transport. Available from Internet: . European Commission – DG Mobility and Transport, 2014. Update of the Handbook on External Costs of Transport Final report. European Commission, 2012. Measuring Road Congestion. Technical Report. European Commission. Eurostat, 2013. Freight transport statistics. Available from Internet: . Kummer, S. 2006. Einführung in die Verkehrswirtschaft. Vienna: Facultas. OECD, 2010. Globalisation, transport and environment. Available from Internet: . Port of Hamburg - statisics. Available from Internet: . Qu, L.; Chen, Y. 2008. A Hybrid MCDM Method for Route Selection of Multimodal Transportation Network”. Part I, LNCS 5263, 374-383. Republički zavod za statistiku, Republika Srbija. Available from Internet: . Saaty, T.L. 1996. The analytic network process. Pittsburgh: RWS Publications. Saaty, T.L. 1980. The Analytic Hierarchy Process. New York, NY: McGraw-Hill International. Sawadogo, M.; Anciaux, D. 2009. Intermodal transportation within the green supply chain: An approach based on the ELECTRE method, Computers & Industrial Engineering (CIE). International Conference on Industrial Engineering, 69 July 2009, 839-844. Sawadogo, M.; Anciaux, D. 2010. Reducing the environmental impacts of intermodal transportation: a multi-criteria analysis based on ELECTRE and AHP methods. In Proceedings of the 3rd International Conference on Information Systems, Logistics and Supply Chain Creating value through green supply chains ILS 2010 – Casablanca (Morocco), April 14-16. UNCTAD, 2013. Review of Maritime Transport. United Nations. Available from Internet: .
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