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Assessment of the political city logistics initiatives sustainability
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Transportation Research Procedia 30 (2018) 285–294 www.elsevier.com/locate/procedia
EURO Mini Conference on "Advances in Freight Transportation and Logistics" (emc-ftl-2018) EURO Mini Conference "Advances in Freight Transportation and Logistics" (emc-ftl-2018) Assessment of theon political city logistics initiatives sustainability
Assessment Snežana of the political city logistics initiatives Tadića, Slobodan Zečevića, Mladen Krstićasustainability * Abstract
a
a a 11000, Serbia Faculty of Transport and Traffic Engineering, University of Belgrade, aVojvode Stepe 305, Belgrade,
a
Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11000, Serbia
Snežana Tadić , Slobodan Zečević , Mladen Krstić *
City is the place of the largest concentration of economic and social activities, and goods delivery is a prerequisite for maintaining the Abstract urban life and business activities that encourage the growth and development of the city. Logistics systems and processes that enable the realization goods flows also support employment and generate income, but moreover they can have impacts all important City is the of place of the largest concentration of economic and social activities, and goods delivery is anegative prerequisite for on maintaining the functions city. From the aspect of sustainable development, i.e. social,ofecological and economic logistics processes, urban life of andthe business activities that encourage the growth and development the city. Logistics systemsefficiency, and processes that enable the primarily freight transport, are faremployment from optimal. The growthincome, of road but freight transport and traffic air pollution and other realizationurban of goods flows also support and generate moreover they can havecongestion, negative impacts on all important negative environmental impact, inefficient land use and growth ofi.e. thesocial, goodsecological delivery costs influence the definition and research of functions of the city. From the aspect of sustainable development, and economic efficiency, logistics processes, various City Logistics initiatives. dependsofon thefreight degreetransport of acceptability andcongestion, interest byair thepollution key stakeholders. primarily urban freight (CL) transport, are farTheir from sustainability optimal. The growth road and traffic and other For this reason, it is veryimpact, important to identify problems assessofthetheimpacts the solution all stakeholders. The main goal of this negative environmental inefficient land use andand growth goods of delivery costs on influence the definition and research of paper to analyze the(CL) sustainability political CL initiatives and on their to theand goals and requirements of different variousisCity Logistics initiatives.ofTheir sustainability depends theranking degree in of relation acceptability interest by the key stakeholders. stakeholders, as itwell as toimportant develop to and implement a methodology for impacts solving this problem. new model of multi-criteria decisionFor this reason, is very identify problems and assess the of the solutionThe on all stakeholders. The main goal of this makingiswhich combines Delphi, AHPof and SWARA in the is developed this and paper. paper to analyze the sustainability political CLmethods initiatives andfuzzy their environment ranking in relation to the in goals requirements of different stakeholders, as well as to develop and implement a methodology for solving this problem. The new model of multi-criteria decision© 2018 which The Authors. Published by Elsevier Ltd. methods in the fuzzy environment is developed in this paper. making combines Delphi, AHP and SWARA This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Copyrightand © 2018 Elsevierunder Ltd. All rights reserved. Selection peer-review responsibility of the scientific committee of the EURO Mini Conference on "Advances in © 2018 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on “Advances in Freight Freight Transportation and Logistics" (emc-ftl2018). This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Transportation and Logistics” (emc-ftl2018). Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on "Advances in Keywords: City logistics political initiative;(emc-ftl2018). sustainability; multi-criteria decision-making; fuzzy Delphi; fuzzy AHP; fuzzy SWARA Freight Transportation and Logistics" Keywords: City logistics political initiative; sustainability; multi-criteria decision-making; fuzzy Delphi; fuzzy AHP; fuzzy SWARA
1. Introduction
1. Introduction The city logistics (CL) system is very complex. It is characterized by a large number of participants, with different, most often conflicting goals and complex interactions (Tadić and Zečević, 2015). Since the local The city is logistics (CL)for system is better very complex. It is characterized by economic a large number of participants, with government responsible creating living conditions and promoting and ecological development, different, most often conflicting goals and complex interactions (Tadić and Zečević, 2015). Since the local government is responsible for creating better living conditions and promoting economic and ecological development, * Corresponding author. Tel.: +381-11-3091-237; fax: +381-11-3096-704. E-mail address: [email protected] * Corresponding author. Tel.: +381-11-3091-237; fax: +381-11-3096-704. 2352-1465 © 2018 The Authors. Published by Elsevier Ltd. E-mail address: [email protected] This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection peer-review under responsibility of the scientific 2352-1465and © 2018 The Authors. Published by Elsevier Ltd. committee of the EURO Mini Conference on "Advances in Freight Transportation and ThisLogistics" is an open(emc-ftl2018). access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on "Advances in Freight Transportation and Logistics" (emc-ftl2018).
2352-1465 Copyright 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on “Advances in Freight Transportation and Logistics" (emc-ftl2018). 10.1016/j.trpro.2018.09.031
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it should play a major role in conflict resolution among other CL participants, prepare and coordinate initiatives and implement compromise CL solutions. However, research shows that due to the lack of qualified staff and expertise, local governments mainly do not know how to solve the CL problems (Tadić and Zečević, 2016; Tadić et al., 2014a; Tadić et al., 2014b). It can be said that by applying certain local government initiatives, they ignore the nature and basic principles of logistics, and treat other participants as opponents rather than partners in the planning process. Moreover, CL initiatives require more or less changes in the behavior of system participants, and stakeholders will only support those solutions that do not cause negative consequences or if they are minor than the positive effects. Since the ultimate goal of all initiatives is the sustainable development of the city, the analysis of the sustainability of the political CL initiatives is a very modern topic. The originality of this paper is that it analyzes these initiatives from the perspective of different stakeholders, with the aim of achieving acceptability, successful implementation and environmental, social and economic sustainability of the urban environment. In addition, the originality of the paper is also in the development of a new methodology, i.e. the multi-criteria decision making (MCDM) model, which enables the solution of this problem. The paper is organized as follows. The section 2 describes the problem structure, i.e. the goals of the CL stakeholders, political initiatives representing the alternatives and the criteria for their evaluation. The section 3 describes the MCDM model and the steps for its application. The results of the model application for ranking the initiatives are given in section 4, and the discussion of the obtained results in section 5. The paper ends with the concluding remarks and future research directions. 2. Structure of the problem of the political CL initiatives sustainability assessment The problem solved in this paper is composed of eight alternatives, i.e. political CL initiatives, and ten criteria classified into three groups, which are described in more details in the following. Evaluation of the criteria is performed by the viewpoint of the four stakeholders: residents (Res.), shippers/receivers (S&R), logistics service providers (Pro.) and city administration (Adm.). Residents are the people who live, work and buy in the city. They tend to minimize traffic congestion, noise, air pollution and traffic accidents near the place of residence, work and shopping. Shippers and receivers send or receive the goods and generally require the provider to maximize the level of service, which means shorter delivery/pick-up time, greater reliability and flexibility, better information and lower service costs. Providers tend to minimize the costs of collecting or delivering the goods to the users, while maximizing profit. City administration aims to economically develop the city and increase employment opportunities while reducing the traffic congestion, improving the living conditions and increasing the safety of movement (Zečević and Tadić, 2006). 2.1. Political CL initiatives Political initiatives are defined and implemented by the public sector in order to force companies to change their activities and make them more sustainable (Tadić et al., 2014b, Awasthi & Chauhan, 2012). This paper analyzes: toll collection (I1), access restrictions on the vehicle capacity and size (I2), vehicle loading factor control (I3), lowemission zones (I4), access time windows (I5), night deliveries (I6), infrastructure reservation (I7), loading-unloading zones (I8). The aim of the toll collection (I1) is to subject the scarce capacities of road infrastructure to the market rules, and streamline the traffic flow from a temporal perspective. This further implicates congestion reduction and increase of the traffic flows speed, i.e. reduction of the harmful gases emissions. Initiatives of this type relate to passenger and freight vehicles, with the exception of an initiative that involves the use of traffic lanes reserved for freight vehicles (Meyer, 2006). The toll collection results depend on the type of charge, the amount, the existing alternatives and the intensity of application. Improved accessibility is mainly attributed to the reduction of passenger traffic, since the reduction of freight traffic and its redistribution in time is almost unfavorable (Holguin-Veras, 2004). Initiatives of restrictions on the vehicle capacity and size (I2) are used by local authorities to increase safety, improve traffic conditions and reduce damage to facilities and traffic infrastructure. On the other hand, there are environmental goals and an improvement in the quality of life. However, it has been shown that the vehicle load limitation measures have a negative impact on the accessibility of city centers, environment and logistic costs. The ban on access for commercial vehicles increases the number of light delivery vehicles, number of vehicle-kilometers, energy consumption and emissions
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of harmful gases (Holguin-Veras et al., 2013; Quak and de Koster, 2009). Moreover, economic and ecological benefits can be achieved by mitigating the restrictions for freight vehicles (Anderson et al., 2005; McKinnon, 2005). The initiative of vehicle loading factor control (I3) aims to increase accessibility and mobility in the urban centers. By increasing the efficiency of the vehicle's cargo space, it attempts to reduce the number of vehicles in the central city zones, thus stimulating consolidation of deliveries before entering the city centers. However, flows consolidation is often limited by the delivery deadlines, time intervals and driver's work hours (Arvidsson, 2013). Still, the applications of the loading factor control have generally shown positive effects, although the conditioning to apply the measure is not easy. This raises the question whether the control of loading factor is the best way to encourage the cooperation of providers and consolidation of flows, with the aim of reducing the empty journeys and the number of partially loaded freight vehicles (Quak, 2008). By applying the initiatives of low-emission zones (I4), local authorities strive to improve air quality by excluding vehicles that emit aero pollution. Vehicles approaching the zones must meet certain criteria, which can increase the delivery costs (Browne et al., 2005). The implementation of this initiative should be known in advance so that carriers can adjust the characteristics of the vehicle fleet in time. Since the initiative has a positive impact on the environment, support by the society and the economic sector can be expected for its implementation (Anderson et al., 2005). The initiatives of access time windows (I5) are applied with the aim of improving the social aspect of the sustainability concept. It aims to increase the attractiveness of the city center by reducing the number of freight vehicles and their effects (noise, visual disturbance, safety) at the time of the highest concentration of people. However, due to the time limitation of access, the number of vehicle launches and the number of vehicle-kilometers increases, which further means additional emissions of harmful gases (Quak and de Koster, 2009). Night delivery (I6) is a variant of the access time window initiative. In order to reduce freight transport in the period of traffic peaks, the concept implies delivery of goods during the night, most often between 22:00 and 7:00. At this time, most activities in the city are reduced to a minimum, so the realization of logistics activities does not cause traffic jams and congestion on the streets. In addition, night deliveries contribute to increasing the supply efficiency, but also benefits in terms of economics and environmental protection (Zečević et al., 2015). Moreover, the night work of the drivers and the willingness of the receivers to receive the delivery have their price. In addition, night deliveries cause the disturbance by noise pollution and the lights from the delivery vehicles, therefore there are initial investment in more "silent" equipment and vehicles (Holguín-Veras, 2008; NICHES, 2009). The initiatives of infrastructure reservation (I7) aim to prohibit or restrict freight transport on certain routes or to create a network for freight vehicles in order to increase their efficiency. The ultimate goal is to improve the living conditions, traffic safety, delivery reliability and protection of roads that are not designed for heavy freight traffic. By reserving a freight transport lanes, accessibility to urban centers and transport efficiency are improved (Dablanc, 2007; Abel, 2006). Clear signaling and referral to the use of reserved lanes can be decisive for the success of this group of initiatives. Defining the loading and unloading zones (I8) is focused on two problems: the problem of lack of surfaces for loading and unloading operations, and the problem of restricted access caused by loading and unloading of the delivery vehicles. The reason for their initiation is the problem of streets congestion. In the absence of loading and unloading surfaces, the vehicles stop on the street or sidewalk during the delivery/pick/up operations, which negatively affects the capacity and safety of other traffic, but also on the costs and quality of the services of the carrier, logistics provider (Aiura and Taniguchi, 2006). By defining the location of loading and unloading zones in accordance with the requirements and conditions of the delivery/pick-up of goods, these problems can be reduced (Alho and Silva, 2014). Implementation of the initiative is simple, fast and without major financial investments, so it is often found in practice. Transport operators are willingly taking part in these initiatives because they enable them to solve the problems they face every day. However, this initiative requires better parking control, i.e. application of a system that will prevent the illegal stopping and parking of other vehicles on the reserved surfaces. Nonetheless, due to the obvious effects, initiatives of this type generally have strong social support (Russo and Comi, 2010). 2.2. Criteria for the evaluation of the CL initiatives For the evaluation of the alternatives ten criteria, which are in line with the triple bottom line (TBL) concept of sustainability (Elkington, 1997), are defined in this paper and divided into three groups: social, economic and
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ecological criteria. The criteria are defined based on the literature review (Awasthi and Chauhan, 2012; Tadić and Zečević, 2016; Tadić and Zečević, 2014a), experience of the authors and consultations with the stakeholders of CL. The group of social criteria consists of: mobility - C1, attractiveness of the city, zone - C2, freeing of public spaces - C3, traffic accidents - C4 and noise and vibration - C5. Mobility (C1) refers to changes in the conditions for the movement of passenger and freight vehicles in the city resulting from the application of the initiative. Attractiveness of the city (C2) implies the impact of the initiatives on changes in particular urban areas, zones, which can cause the environment becoming more or less attractive. The freeing of public spaces (C3) refers to the possibility of freeing certain spaces in favor of other, more profitable contents, due to more efficient realization and organization of goods and transport flows as a result of certain initiatives application. Traffic accidents (C4) relate to changes in the participation of traffic accidents caused by the delivery vehicles. Noise and vibration (C5) imply changes in noise and vibration emissions originating from logistics activities in the city, primarily freight transport. The group of economic criteria consists of: reliability and accuracy of the delivery - C6, costs of implementation and control - C7 and accessibility - C8. The reliability and accuracy of the delivery (C6) considers the availability of goods that the customer requires in an acceptable delivery time. The costs of implementation and control (C7) consider the costs of introducing initiatives and control of their application, which can significantly vary for the different initiatives. Accessibility (C8) mostly depend on the part of the city or the area being under consideration and refers to the degree of impact of the initiative on removing the obstacles (physical, regulative or organizational) for the access of traffic participants to the desired destinations, resulting in a reduction of the traffic congestion, number of vehicle kilometers, transport time, number of freight vehicles in the city, noise and gas emission, etc. The group of ecological criteria consists of: energy savings - C9 and air pollution - C10. Energy savings (C9) considers the changes in energy consumption due to the reduction in the number of delivery vehicles and their more efficient use as a result of the application of various initiatives. Air pollution (C10) takes into account the influence of various initiatives on changes in emissions of harmful gases and particles by the delivery vehicles in the city. 3. Hybrid MCDM model for the CL initiative evaluation Various MCDM methods and their combinations have been used in the literature for solving transport and logistics problems (Żak et al., 2018; Lee and Yang, 201; Zečević et al., 2017; Tadić et al., 2014c). This paper proposes a new hybrid model based on the combination of fuzzy DAHP (fuzzy Delphi based fuzzy AHP) and fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) methods for solving the MCDM problem. There are no papers in the literature in which Delphi, AHP and SWARA methods are combined in the fuzzy environment. The model is hybrid because it does not simply compound various MCDM methods, where each method is in charge of solving one part of the problem, but aggregates different segments of the methods making a unique model. The goal was to exploit the advantages of the methods while minimizing their drawbacks. AHP method, as one of the most widely used MCDM methods, was taken as the basis for the model development. However, as the complexity of the AHP method exponentially increases with the number of elements (criteria and alternatives), the SWARA method, which is easier to use especially by the less experienced users, requires considerably less comparisons and does not require consistency check, is introduced for the alternatives evaluation and ranking. As criteria are evaluated by the multiple decision makers (DM), part of the Delphi method is aggregated into the AHP method in the process of obtaining criteria weights, which enabled the integration of different evaluations. As the DMs' judgments on decision factors are often imprecise, vague and ambiguous due to incomplete information or inability of their treatment in a decision environment, all methods are solved in the fuzzy environment. The fuzzy set theory can efficiently deal with the vagueness in thinking and expressing preferences of the DMs. The first part of the model implies the application of the fuzzy DAHP method for establishing connections between the elements of the hierarchical structure and for determining the final criteria weights. The fuzzy AHP method is selected due to its ability to adequately address the hierarchical structure created in this paper by interconnected elements (criteria). Traditional Delphi method is introduced by Dalkey and Helmer (1963) and has since been widely used in various fields. The aim of the method is to collect data from the field of expertise of the respondents. The Delphi method is characterized by anonymity, iteration, controlled feedback, statistical group responses, and stability in responses among the DMs on a specific issue (Shen et al., 2011). The method is extended in the fuzzy environment by Murry et al. (1985). The AHP method developed by Saaty (1980), deals with the
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determination of the criteria relative importance in the MCDM problems. The method allows simultaneous consideration of intangible qualitative criteria, as well as tangible quantitative criteria (Badri, 2001). The fuzzy extension of the AHP method is performed by Van Laarhoven and Pedrycz (1983). In the second part of the model, SWARA method was used to evaluate and select the most favorable alternative, i.e. to select the most favorable political CL initiative from the aspect of sustainability. SWARA method for MCDM was developed by Keršulienė et al. (2010). The method is used to determine the weight (value, importance) of an alternative, and is based on determining the order of importance of the alternative (from the most important to the least important) by the DM or group of DMs based on their knowledge, experience and information. There are no papers in the literature in which the SWARA method is extended in the fuzzy environment. The steps of the proposed hybrid MCDM model are described below. Step 1: Define the problem structure, i.e. form the sets of alternatives and evaluation criteria. Step 2: Define the fuzzy scale for the evaluation of criteria and alternatives by the DMs. Linguistic terms and corresponding triangular fuzzy values are given in Table 1. Table 1. Fuzzy scale for the evaluations Linguistic term
None
Very Low
Low
Fairly Low
Medium
Fairly High
High
Very High
Abbreviations
N
VL
L
FL
M
FH
H
VH
Fuzzy scale
(1, 1, 2)
(1, 2, 3)
(2, 3, 4)
(3, 4, 5)
(4, 5, 6)
(5, 6, 7)
(6, 7, 8)
(7, 8, 9)
Step 3: Obtain the criteria weights by applying the fuzzy DAHP method. Step 3.1: Obtain the pair vise comparisons of criteria in relation to all stakeholders, by applying the linguistic scale which can be transformed into triangular fuzzy numbers by applying the relations given in Table 1. The triangular fuzzy numbers are denoted by a~ijl = (lijl, mijl, uijl) which indicate the importance of the element i in relation to the criteria j, i,j=1,...,n, by the stakeholder l. lijl, mijl and uijl are lower, medium and upper values of the triangular fuzzy evaluation a~ijl . o is the number of the considered stakeholders. Step 3.2: Integrate the assessments by the representatives of various stakeholders by applying the following equations which represent the part of the fuzzy Delphi method (adapted from Mikaeil et al. (2013)): ~
ij ij , ij , ij , ij ij ij
(1)
ij Minlijl , l 1,..., o
(2)
o
ij mijl l 1
1/ o
, l 1,..., o
ij Maxuijl , l 1,..., o
(3) (4)
~ where ij , ij and ij are lower, medium and upper values of the integrated fuzzy evaluation ij , respectively. ~ Step 3.3: By applying the equations (1-4), integrated fuzzy evaluations ij of the elements' pair wise ~ comparisons are obtained, based on which the fuzzy judgment matrix is formed in the following way:
/ ~12 ~1n ~ ~ / 2n ~ 21 ~ ~ n1 n 2 /
(5)
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Step 3.4: Calculate the relative criteria weights. For the pair vise comparison matrix it is necessary to obtain the n ~ priority vector. For obtaining the priority vector ( W w1 ,..., wn 0 , wi 1 ) from the fuzzy matrix the i 1
"logarithmic fuzzy preference programming" (LFPP) method is used in this paper (Wang and Chin, 2011). Elements ~ ~ of the fuzzy comparison matrix ( ) are triangular fuzzy judgments ij ij , ij , ij of comparing element i in relation to element j, taken by the following approximate equation:
~ ln ij ln lij , ln mij , ln uij , i, j 1,2,..., n
(6)
For obtaining the elements' weights (wi) the following nonlinear priority model is proposed: n 1
Min J 1 M 2
ij2 ij2 n
i 1 j i 1
xi x j ln mij / lij ij ln lij , i 1,..., n 1; j i 1,..., n, xi x j ln u ij / mij ij ln u ij , i 1,..., n 1; j i 1,..., n, s.t. xi 0, i 1,..., n, , 0, i 1,..., n 1; j i 1,..., n, ij ij
(7)
where xi=lnwi for i=1,...,n, and M is a specified sufficiently large constant such as M=103. εij and ηij for i=1,...,n-1 and j=1,...,n are the nonnegative deviation variables introduced to avoid membership degree λ from taking a negative value. It is most desirable that these values are as small as possible, and they have to meet the following inequalities:
ln wi ln w j ln mij / lij ij ln lij , i 1,..., n 1; j i 1,..., n,
ln w i ln w j ln u ij / mij ij ln u ij , i 1,..., n 1; j i 1,..., n. Let xi i 1,..., n be the optimal solution to model (7). The normalized criteria weights for fuzzy pair wise
~
~
comparison matrix ij
nn
can then be obtained as:
expx , i 1,..., n,
wi exp xi
n
j 1
j
(8)
where exp() is the exponential function, namely expxi e x for i=1,...,n. This method results in crisp normalized weights. In order to control the result of the method, the Consistency Ratio (CR) for each matrix is calculated as follows (Saaty, 1980): i
CR CI / RI ,
(9)
where CI is the Consistency Index and is calculated as follows: CI max n n 1
(10)
~ max is the principal eigenvalue of the matrix . RI is the Random Index whose values for matrices of various
sizes are contained in Saaty (1996). The comparisons are acceptable only if the CR values are less than 0.10.
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Step 4: Evaluate the alternatives by applying the fuzzy SWARA method. Step 4.1: For each criterion i, arrange the alternatives in decreasing order by the expected importance. Step 4.2: Evaluate the relative importance of alternative k in relation to the alternative (k-1) starting from the second alternative. This relation is called the comparative importance of the average value, and it is denoted as ~ sk , where ~ sk (l k , mk , u k ) , k=1,...,m, is a triangular fuzzy number which corresponds to the linguistic terms given in Table 1. l, m and u denote lower, middle and upper value of the triangular fuzzy number. Step 4.3: Calculate the coefficient ~ rk in the following way:
1,1,1, k 1 ~ rk . u, mk / max u, u k / max u k 1,1,1, k 1,..., m lk / max k k k
(11)
Step 4.4: Calculate the preliminary values of the alternatives q~k in the following way:
1,1,1, k 1 . q~k ~ ~ q k 1 rk , k 1,..., m
(12)
Step 4.5: Calculate the relative preference values of the alternatives v~k in the following way: v~k q~k q~k
(13)
k
The obtained values need to be defuzzyfied by applying the following equation (Kutlu and Ekmekcioglu, 2012):
Crispv~k l kv 4 * mkv u kv / 6
(14)
Step 5: Obtain the final ranking of the alternatives, i.e. the overall utility of the alternatives. First it is necessary to obtain the aggregated weighted values of the alternatives Qk by applying the following equation: n
Qk wi * v k , k 1,..., m
(15)
i 1
By arranging the obtained values Qk in the decreasing order, the final ranking of the alternatives is obtained. 4. Selection of the most appropriate political CL initiative
By applying the proposed MCDM model, the ranking of the political CL initiatives is performed. For the defined problem it was necessary to perform the criteria comparison in relation to the stakeholders' representatives in order to obtain the criteria weights. Experts dealing with the CL problems, acting as the stakeholders' representatives, performed the comparisons of all criteria pairs by giving the linguistic assessments, as shown in Table 2. Evaluations of the stakeholders' representatives are then transformed into the fuzzy values by applying the relations given in Table 1, and then by applying the equations (1) - (4) the integrated evaluations of the criteria comparison are obtained. For the obtained values, the non-linear priority model (7) is solved. After applying the equation (8) for the optimal solutions of the model (7) the following normalized values of the criteria weights are obtained: C1 (0,111), C2 (0,116), C3 (0,072), C4 (0,171), C5 (0,066), C6 (0,121), C7 (0,079), C8 (0,093), C9 (0,098) and C10 (0,073). After obtaining the criteria weights it is necessary to evaluate the initiatives. First, the experts arranged the initiatives in the decreasing orders by the expected importance in relation to each criterion. Then the importance of initiative k in relation to (k-1) initiative is evaluated by each criterion using the relations given in Table 1. The order of the initiatives by each criterion and their evaluations are given in Table 3.
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Table 2. Evaluations of the criteria by the stakeholders' representatives (Res., S&R, Pro., Adm.) C1
C2
C3
C1
/,/,/,/
VL,/,VL,/
VL,N,L,N
N,/,/,/
L,VL,N,N
N,/,/,VL
FL,N,N,/
L,/,/,VL
FL,/,/,N
VL,VL,N,N
C2
/,VL,/,VL
/,/,/,/
N,VL,N, VL
/,N,/,N
VL,L,/,VL
/,/,/,L
L,VL,/,N
VL,/,/,L
L,N,/,VL
N,L,/,VL
C3
/,N,/,N
N,/,N,/
/,/,/,/
/,/,/,/
VL,VL,/,N
/,/,/,VL
L,N,/,/
VL,/,/,VL
L,/,/,N
N,VL,/,N
C4
N,VL,L, VL
VL,N,FL, N
VL,VL,FL, VL
/,/,/,/
L,L,L,VL
N,/,VL,L
FL,VL,L,N
L,/,VL,L
FL,N,N, VL
VL,L,L,VL
C5
/,/,N,N
/,/,VL,/
/,/,L,N
/,/,/,/
/,/,/,/
/,/,/,VL
VL,/,N,/
N,/,/,VL
VL,/,/,N
/,N,N,N
C6
N,L,VL,/
VL,VL,L,/
VL,L,L,/
N,VL,/,/
L,FL,VL,/
/,/,/,/
FL,L,VL,/
L,N,N,N
FL,VL,/,/
VL,FL,VL,/
C7
/,N,N,VL
/,/,VL,N
/,N,L,VL
/,/,/,N
/,VL,N,VL
/,/,/,L
/,/,/,/
/,/,/,L
N,/,/,VL
/,VL,N,VL
C8
/,L,VL,/
/,VL,L,/
/,L,L,/
/,VL,/,/
N,FL,VL,/
/,N,N,N
VL,L,VL,/
/,/,/,/
VL,VL,/,/
/,FL,VL,/
C4
C5
C6
C7
C8
C9
C10
C9
/,VL,L,N
/,N,FL,/
/,VL,FL,N
/,N,N,/
/,L,L,N
/,/,VL,VL
N,VL,L,/
/,/,VL,VL
/,/,/,/
/,L,L,N
C10
/,/,N,N
N,/,VL,/
N,/,L,N
/,/,/,/
VL,N,N,N
/,/,/,VL
L,/,N,/
VL,/,/,VL
L,/,/,N
/,/,/,
C7
C8
Table 3. Order of the initiatives and its evaluations C1
C2
I6
C3
I6
C4
I4
C5
I7
C6
I4
I6
I2
C9
I6
C10
I3
I4
I3
L
I3
L
I6
L
I4
L
I3
L
I8
L
I8
L
I3
L
I4
L
I6
L
I4
L
I4
L
I5
L
I6
L
I7
L
I7
L
I1
L
I1
L
I6
L
I3
L
I7
M
I8
M
I8
M
I3
M
I5
M
I5
M
I5
M
I7
M
I7
M
I7
M
I1
N
I2
N
I3
N
I5
N
I1
N
I1
N
I7
N
I8
N
I1
N
I5
N
I8
M
I7
M
I7
M
I8
M
I2
M
I2
M
I4
M
I4
M
I8
M
I1
M
I2
VL
I5
VL
I2
VL
I1
VL
I6
VL
I4
VL
I3
VL
I2
VL
I2
VL
I2
VL
I5
N
I1
N
I1
N
I2
N
I8
N
I3
N
I6
N
I5
N
I5
N
I8
N
For each criterion the following procedure is repeated. First the values of the coefficient ~ rk are calculated by ~ applying the equation (11). Then the preliminary values of the alternatives q k are calculated by applying the equation (12). For the obtained values the relative preference values of the alternatives v~k are obtained by applying the equation (13), which are then defuzzyfied by applying the equation (14). In order to obtain the final ranking of the initiatives. i.e. the overall utility of the initiatives, the aggregated weighted values of the alternatives Qk are obtained by applying the equation (15), which are then arranged in the decreasing order. All of the above values, as well as the final ranking of the initiatives are presented in the Table 4. The initiative of night deliveries (I6) is obtained as the best ranked political initiative in the given circumstances and for the considered criteria. 5. Discussion
The initiative of night deliveries, I6 (0.218), is obtained as the best-ranked political initiative, under given conditions and for the observed criteria. The initiative is in fact a compromise solution because it was obtained as a result of evaluation by various stakeholders. Many positive effects could be achieved by applying night delivery, identified in the numerous practical testing of this initiative. One such example is testing by a Serbian trade company that performed night deliveries for the retail stores working 24 hours. From the receivers' point of view, the greater delivery reliability can be achieved. From the logistics providers' perspective, lower costs and shorter delivery times can be achieved, and from the aspect of the residents, higher availability of products in the early morning hours, lower emissions and reduced traffic during the day can be achieved (Zečević et al., 2015). According to the above, this initiative is evaluated as the most favorable from the aspects of city attractiveness, freeing of public spaces, reliability and accuracy of delivery and accessibility, whereas these criteria were evaluated as very important by the stakeholders. Moreover, the problem of night
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delivery is the residents' disturbance by the noise, vibration and lights, but also the increased risk and exposure to crime, both staff and goods (Holguín-Veras, 2008). For these reasons, the initiative is poorly assessed from the aspect of noise and vibration and the costs of implementation and control. Solution of the problem also demonstrated the applicability of the proposed model, which is not limited solely to solving this type of problems. With certain adjustments it could be also used to solve some other problems from the field of logistics, such as defining the distribution network, i.e., the number and allocation of different categories of logistics centers, logistics systems technology selection, logistics provider selection, etc., as well as for the problems in other fields. Table 4. Results of the initiatives evaluations and the final ranking C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
wi
0.111
0.116
0.072
0.171
0.066
0.121
0.079
0.093
0.098
0.073
Qk
Rank
I1
0.089
0.019
0.014
0.029
0.055
0.057
0.175
0.170
0.107
0.042
0.072
6
I2
0.034
0.097
0.026
0.016
0.030
0.032
0.332
0.047
0.035
0.023
0.061
8
I3
0.222
0.219
0.077
0.128
0.263
0.010
0.021
0.206
0.236
0.183
0.153
3
I4
0.168
0.181
0.320
0.235
0.359
0.018
0.039
0.082
0.209
0.294
0.181
2
I5
0.018
0.036
0.169
0.080
0.102
0.100
0.113
0.025
0.019
0.079
0.070
7
I6
0.294
0.266
0.242
0.190
0.016
0.368
0.011
0.232
0.186
0.242
0.218
1
I7
0.128
0.063
0.046
0.291
0.177
0.166
0.069
0.141
0.154
0.139
0.150
4
I8
0.063
0.138
0.118
0.050
0.009
0.256
0.250
0.117
0.065
0.012
0.110
5
6. Conclusion
For the efficient realization of logistics activities in the city, all functions and all structures of the city are interested (shippers, receivers, carriers, logistic providers, residents, administration). They all have a common goal: an attractive city in terms of economic, social, traffic, ecological, cultural and other criteria. However, individual goals are in conflict and the change, which is perceived as positive by one group, can cause a number of negative effects to others. In order to improve sustainability, different CL initiatives have been defined. The goals and effects of the initiatives differ depending on the initiator, the application possibilities, the structure and characteristics of the generators of goods flows, etc. In this paper, the ranking of the sustainability of the political initiatives for solving the problems of city logistics is performed, taking into account the requirements and objectives of different stakeholders. To solve the problem, a new hybrid MCDM model which combines Delphi, AHP and SWARA methods in the fuzzy environment is defined. This model, as well as the original approach to problem solving that address and integrate the different stakeholders' attitudes in order to obtain a compromise solution, are the main contributions of this paper. Future research could move towards analyzing and evaluating a wider set of initiatives, measures and concepts of city logistics. In addition, the defined model could also be used to solve problems from other areas. Moreover, new hybrid models which could include the proposed model or some of its parts could also be developed for solving various problems. References Abel, H. 2006. Urban Freight Management in Barcelona (Spain), in "BESTUFS - Best Practice Handbook 2006". In: Abel, H., Karrer, R. (Eds.), BESTUFS II. Aiura, N., Taniguchi, E. 2006. Planning on-street loading-unloading spaces considering the behaviour of pick-up/delivery vehicles. In: Tanigucji, E., Thompson, R.G. (eds), Recent advances in city logistics, Elsevier, Oxford, 107–116. Alho, A.R., Silva, J.A. 2014. Analyzing the relation between land-use/urban freight operations and the need or dedicated infrastructure/enforcement - Application to the city of Lisbon. Research in Transport Business & Management 11, 85–97. Anderson, S., Allen, J., Browne, M. 2005. Urban logistics - how can it meet policy makers' sustainability objectives? Journal of Transport Geography 13.1, 71-81. Arvidsson, N. 2013. The milk run revisited: A load factor paradox with economic and environmental implications for urban freight transport. Transportation Research Part A: Policy and Practice 51, 56–62. Awasthi, A., Chauhan, S.C. 2012. A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Applied Mathematical Modelling 36, 573–584.
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Badri, M. A. 2001. A combined AHP-GP model for quality control systems. International Journal of Production Economics 72, 27-40. Browne, M., Allen, J., Anderson, S. 2005. Low emission zones: the likely effects on the freight transport sector. International Journal of Logistics Research and Applications 8.4, 269–281. Dablanc, L. 2007. Goods transport in large European cities: Difficult to organize, difficult to modernize. Transportation Research Part A 41.3, 280-285. Dalkey, N.C., Helmer, O. 1963. An experimental application method to the use of experts. Management Science 9.3, 458–467. Elkington, J. 1997. Cannibals with forks – Triple bottom line of 21st century business. Stoney Creek, CT: New Society Publishers. Holguin-Veras, J. 2004. On the estimation of the maximum efficiency of the trucking industry: Implications for city logistics, in "Logistics Systems for Sustainable Cities, proceedings of the 3rd international conference on City Logistics". In: Taniguchi, E., Thompson, R. G. (Eds.). Elsevier, Amsterdam, pp. 123-134. Holguin-Veras, J. 2008. Necessary conditions for off-hour deliveries and the effectiveness of urban freight road pricing and alternative financial policies in competitive markets. Transportation Research Part A: Policy and Practice 42, 392–413. Holguin-Veras, J., Cruz, C., Ban, X. 2013. On the comparative performance of urban delivery vehicle classes. Transportmetrica A: Transport science 9.1, 50–73. Keršuliene, V., Zavadskas, E.K., Turskis, Z. 2010. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management 11, 243-258. Kutlu, A.C., Ekmekcioglu, M. 2012. Fuzzy failure modes and effects analysis by using fuzzy TOPSIS/based fuzzy AHP. Expert Systems with Applications 39, 61–67. Lee, P.T.W., Yang, Z. (Eds.) 2018. Multi-Criteria Decision Making in Maritime Studies and Logistics: Applications and Cases. Springer International Publishing AG, Cham, Switzerland. McKinnon, A. 2005. The economic and environmental benefits of increasing maximum truck weight: the British experience. Transport Research Part D: Transport and Environment 10.1, 77–95. Meyer, M.D. 2006. Feasibility of a Metropolitan Truck-only Toll Lane Network: The Case of Atlanta, Georgia, in Metrans. National Urban Freight Conference (NUFC), Long Beach, California. Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., Hosseini, S.M. 2013. Ranking the sawability of ornamental stone using Fuzzy Delphi and multicriteria decision-making techniques. International Journal of Rock Mechanics & Mining Sciences 58, 118-126. Murry, T.J., Pipino, L.L., Gigch, J.P. 1985. A pilot study of fuzzy set modification of Delphi. Human Systems Management 5.1, 76–80. NICHES. 2009. Innovative Approaches in City Logitics: Alternative Solutions for Home Delivery. Forkert, S., Eichhorn, C. (Authors). European Commission, Brussels, Belgium. Quak, H. 2008. Sustainability of urban freight transport - Retail Distribution and Local Regulations in Cities. Erasmus Research Institute of Management (ERIM), Erasmus University, Roterdam, PhD. Quak, H., de Koster, M.B. 2009. How to deal with Urban Policy Restrictions and the Environment. Transportation science 43.2, 211–227. Russo, F., Comi, A. 2010. A classification of city logistics measures and connected impacts. The Sixth International Conference on City Logistics in Procedia Social and Behavioral Sciences 2, 6355–6365. Saaty, T. L. 1980. The analytic hierarchy process. New York, McGraw-Hill. Saaty, T. L. 1996. The analytic network process. Pittsburgh: RWS Publications. Shen, Y.C., Lin, G.T.R., Tzeng, G.H. 2011. Combined DEMATEL techniques with novel MCDM for the organic light emitting diode technology selection. Expert Systems with Applications 38, 1468-1481. Tadić S., Zečević, S. 2016. Modelling city logistics concepts (in Serbian). Faculty of Transport and Traffic Engineering, University of Belgrade. Tadić, S., Zečević, S. 2015. Integrated planning aimed at the sustainability of the city logistics solutions (in Serbian). Tehnika 70.1, 164-173. Tadić, S., Zečević, S., Krstić, M. 2014a. Ranking of Logistics System Scenarios for Central Business District. Promet – Traffic&Transportation 26.2, 159-167. Tadić, S., Zečević, S., Krstić, M. 2014b. City logistics initiatives aimed at improving sustainability within existing context of urban area (in Serbian). Tehnika 61.3, 487-495. Tadić, S., Zečević, S., Krstić, M. 2014c. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems with Applications 41, 8112–8128 Van Laarhoven P.J.M, Pedrycz W. 1983. A fuzzy extension of Saaty’s priority theory. Fuzzy Set and Systems 11, 229–241. Wang, Y.M., Chin, K.S. 2011. Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. 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EURO Mini Conference on "Advances in Freight Transportation and Logistics" (emc-ftl-2018) EURO Mini Conference "Advances in Freight Transportation and Logistics" (emc-ftl-2018) Assessment of theon political city logistics initiatives sustainability
Assessment Snežana of the political city logistics initiatives Tadića, Slobodan Zečevića, Mladen Krstićasustainability * Abstract
a
a a 11000, Serbia Faculty of Transport and Traffic Engineering, University of Belgrade, aVojvode Stepe 305, Belgrade,
a
Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11000, Serbia
Snežana Tadić , Slobodan Zečević , Mladen Krstić *
City is the place of the largest concentration of economic and social activities, and goods delivery is a prerequisite for maintaining the Abstract urban life and business activities that encourage the growth and development of the city. Logistics systems and processes that enable the realization goods flows also support employment and generate income, but moreover they can have impacts all important City is the of place of the largest concentration of economic and social activities, and goods delivery is anegative prerequisite for on maintaining the functions city. From the aspect of sustainable development, i.e. social,ofecological and economic logistics processes, urban life of andthe business activities that encourage the growth and development the city. Logistics systemsefficiency, and processes that enable the primarily freight transport, are faremployment from optimal. The growthincome, of road but freight transport and traffic air pollution and other realizationurban of goods flows also support and generate moreover they can havecongestion, negative impacts on all important negative environmental impact, inefficient land use and growth ofi.e. thesocial, goodsecological delivery costs influence the definition and research of functions of the city. From the aspect of sustainable development, and economic efficiency, logistics processes, various City Logistics initiatives. dependsofon thefreight degreetransport of acceptability andcongestion, interest byair thepollution key stakeholders. primarily urban freight (CL) transport, are farTheir from sustainability optimal. The growth road and traffic and other For this reason, it is veryimpact, important to identify problems assessofthetheimpacts the solution all stakeholders. The main goal of this negative environmental inefficient land use andand growth goods of delivery costs on influence the definition and research of paper to analyze the(CL) sustainability political CL initiatives and on their to theand goals and requirements of different variousisCity Logistics initiatives.ofTheir sustainability depends theranking degree in of relation acceptability interest by the key stakeholders. stakeholders, as itwell as toimportant develop to and implement a methodology for impacts solving this problem. new model of multi-criteria decisionFor this reason, is very identify problems and assess the of the solutionThe on all stakeholders. The main goal of this makingiswhich combines Delphi, AHPof and SWARA in the is developed this and paper. paper to analyze the sustainability political CLmethods initiatives andfuzzy their environment ranking in relation to the in goals requirements of different stakeholders, as well as to develop and implement a methodology for solving this problem. The new model of multi-criteria decision© 2018 which The Authors. Published by Elsevier Ltd. methods in the fuzzy environment is developed in this paper. making combines Delphi, AHP and SWARA This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Copyrightand © 2018 Elsevierunder Ltd. All rights reserved. Selection peer-review responsibility of the scientific committee of the EURO Mini Conference on "Advances in © 2018 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on “Advances in Freight Freight Transportation and Logistics" (emc-ftl2018). This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Transportation and Logistics” (emc-ftl2018). Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on "Advances in Keywords: City logistics political initiative;(emc-ftl2018). sustainability; multi-criteria decision-making; fuzzy Delphi; fuzzy AHP; fuzzy SWARA Freight Transportation and Logistics" Keywords: City logistics political initiative; sustainability; multi-criteria decision-making; fuzzy Delphi; fuzzy AHP; fuzzy SWARA
1. Introduction
1. Introduction The city logistics (CL) system is very complex. It is characterized by a large number of participants, with different, most often conflicting goals and complex interactions (Tadić and Zečević, 2015). Since the local The city is logistics (CL)for system is better very complex. It is characterized by economic a large number of participants, with government responsible creating living conditions and promoting and ecological development, different, most often conflicting goals and complex interactions (Tadić and Zečević, 2015). Since the local government is responsible for creating better living conditions and promoting economic and ecological development, * Corresponding author. Tel.: +381-11-3091-237; fax: +381-11-3096-704. E-mail address: [email protected] * Corresponding author. Tel.: +381-11-3091-237; fax: +381-11-3096-704. 2352-1465 © 2018 The Authors. Published by Elsevier Ltd. E-mail address: [email protected] This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection peer-review under responsibility of the scientific 2352-1465and © 2018 The Authors. Published by Elsevier Ltd. committee of the EURO Mini Conference on "Advances in Freight Transportation and ThisLogistics" is an open(emc-ftl2018). access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on "Advances in Freight Transportation and Logistics" (emc-ftl2018).
2352-1465 Copyright 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the EURO Mini Conference on “Advances in Freight Transportation and Logistics" (emc-ftl2018). 10.1016/j.trpro.2018.09.031
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it should play a major role in conflict resolution among other CL participants, prepare and coordinate initiatives and implement compromise CL solutions. However, research shows that due to the lack of qualified staff and expertise, local governments mainly do not know how to solve the CL problems (Tadić and Zečević, 2016; Tadić et al., 2014a; Tadić et al., 2014b). It can be said that by applying certain local government initiatives, they ignore the nature and basic principles of logistics, and treat other participants as opponents rather than partners in the planning process. Moreover, CL initiatives require more or less changes in the behavior of system participants, and stakeholders will only support those solutions that do not cause negative consequences or if they are minor than the positive effects. Since the ultimate goal of all initiatives is the sustainable development of the city, the analysis of the sustainability of the political CL initiatives is a very modern topic. The originality of this paper is that it analyzes these initiatives from the perspective of different stakeholders, with the aim of achieving acceptability, successful implementation and environmental, social and economic sustainability of the urban environment. In addition, the originality of the paper is also in the development of a new methodology, i.e. the multi-criteria decision making (MCDM) model, which enables the solution of this problem. The paper is organized as follows. The section 2 describes the problem structure, i.e. the goals of the CL stakeholders, political initiatives representing the alternatives and the criteria for their evaluation. The section 3 describes the MCDM model and the steps for its application. The results of the model application for ranking the initiatives are given in section 4, and the discussion of the obtained results in section 5. The paper ends with the concluding remarks and future research directions. 2. Structure of the problem of the political CL initiatives sustainability assessment The problem solved in this paper is composed of eight alternatives, i.e. political CL initiatives, and ten criteria classified into three groups, which are described in more details in the following. Evaluation of the criteria is performed by the viewpoint of the four stakeholders: residents (Res.), shippers/receivers (S&R), logistics service providers (Pro.) and city administration (Adm.). Residents are the people who live, work and buy in the city. They tend to minimize traffic congestion, noise, air pollution and traffic accidents near the place of residence, work and shopping. Shippers and receivers send or receive the goods and generally require the provider to maximize the level of service, which means shorter delivery/pick-up time, greater reliability and flexibility, better information and lower service costs. Providers tend to minimize the costs of collecting or delivering the goods to the users, while maximizing profit. City administration aims to economically develop the city and increase employment opportunities while reducing the traffic congestion, improving the living conditions and increasing the safety of movement (Zečević and Tadić, 2006). 2.1. Political CL initiatives Political initiatives are defined and implemented by the public sector in order to force companies to change their activities and make them more sustainable (Tadić et al., 2014b, Awasthi & Chauhan, 2012). This paper analyzes: toll collection (I1), access restrictions on the vehicle capacity and size (I2), vehicle loading factor control (I3), lowemission zones (I4), access time windows (I5), night deliveries (I6), infrastructure reservation (I7), loading-unloading zones (I8). The aim of the toll collection (I1) is to subject the scarce capacities of road infrastructure to the market rules, and streamline the traffic flow from a temporal perspective. This further implicates congestion reduction and increase of the traffic flows speed, i.e. reduction of the harmful gases emissions. Initiatives of this type relate to passenger and freight vehicles, with the exception of an initiative that involves the use of traffic lanes reserved for freight vehicles (Meyer, 2006). The toll collection results depend on the type of charge, the amount, the existing alternatives and the intensity of application. Improved accessibility is mainly attributed to the reduction of passenger traffic, since the reduction of freight traffic and its redistribution in time is almost unfavorable (Holguin-Veras, 2004). Initiatives of restrictions on the vehicle capacity and size (I2) are used by local authorities to increase safety, improve traffic conditions and reduce damage to facilities and traffic infrastructure. On the other hand, there are environmental goals and an improvement in the quality of life. However, it has been shown that the vehicle load limitation measures have a negative impact on the accessibility of city centers, environment and logistic costs. The ban on access for commercial vehicles increases the number of light delivery vehicles, number of vehicle-kilometers, energy consumption and emissions
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of harmful gases (Holguin-Veras et al., 2013; Quak and de Koster, 2009). Moreover, economic and ecological benefits can be achieved by mitigating the restrictions for freight vehicles (Anderson et al., 2005; McKinnon, 2005). The initiative of vehicle loading factor control (I3) aims to increase accessibility and mobility in the urban centers. By increasing the efficiency of the vehicle's cargo space, it attempts to reduce the number of vehicles in the central city zones, thus stimulating consolidation of deliveries before entering the city centers. However, flows consolidation is often limited by the delivery deadlines, time intervals and driver's work hours (Arvidsson, 2013). Still, the applications of the loading factor control have generally shown positive effects, although the conditioning to apply the measure is not easy. This raises the question whether the control of loading factor is the best way to encourage the cooperation of providers and consolidation of flows, with the aim of reducing the empty journeys and the number of partially loaded freight vehicles (Quak, 2008). By applying the initiatives of low-emission zones (I4), local authorities strive to improve air quality by excluding vehicles that emit aero pollution. Vehicles approaching the zones must meet certain criteria, which can increase the delivery costs (Browne et al., 2005). The implementation of this initiative should be known in advance so that carriers can adjust the characteristics of the vehicle fleet in time. Since the initiative has a positive impact on the environment, support by the society and the economic sector can be expected for its implementation (Anderson et al., 2005). The initiatives of access time windows (I5) are applied with the aim of improving the social aspect of the sustainability concept. It aims to increase the attractiveness of the city center by reducing the number of freight vehicles and their effects (noise, visual disturbance, safety) at the time of the highest concentration of people. However, due to the time limitation of access, the number of vehicle launches and the number of vehicle-kilometers increases, which further means additional emissions of harmful gases (Quak and de Koster, 2009). Night delivery (I6) is a variant of the access time window initiative. In order to reduce freight transport in the period of traffic peaks, the concept implies delivery of goods during the night, most often between 22:00 and 7:00. At this time, most activities in the city are reduced to a minimum, so the realization of logistics activities does not cause traffic jams and congestion on the streets. In addition, night deliveries contribute to increasing the supply efficiency, but also benefits in terms of economics and environmental protection (Zečević et al., 2015). Moreover, the night work of the drivers and the willingness of the receivers to receive the delivery have their price. In addition, night deliveries cause the disturbance by noise pollution and the lights from the delivery vehicles, therefore there are initial investment in more "silent" equipment and vehicles (Holguín-Veras, 2008; NICHES, 2009). The initiatives of infrastructure reservation (I7) aim to prohibit or restrict freight transport on certain routes or to create a network for freight vehicles in order to increase their efficiency. The ultimate goal is to improve the living conditions, traffic safety, delivery reliability and protection of roads that are not designed for heavy freight traffic. By reserving a freight transport lanes, accessibility to urban centers and transport efficiency are improved (Dablanc, 2007; Abel, 2006). Clear signaling and referral to the use of reserved lanes can be decisive for the success of this group of initiatives. Defining the loading and unloading zones (I8) is focused on two problems: the problem of lack of surfaces for loading and unloading operations, and the problem of restricted access caused by loading and unloading of the delivery vehicles. The reason for their initiation is the problem of streets congestion. In the absence of loading and unloading surfaces, the vehicles stop on the street or sidewalk during the delivery/pick/up operations, which negatively affects the capacity and safety of other traffic, but also on the costs and quality of the services of the carrier, logistics provider (Aiura and Taniguchi, 2006). By defining the location of loading and unloading zones in accordance with the requirements and conditions of the delivery/pick-up of goods, these problems can be reduced (Alho and Silva, 2014). Implementation of the initiative is simple, fast and without major financial investments, so it is often found in practice. Transport operators are willingly taking part in these initiatives because they enable them to solve the problems they face every day. However, this initiative requires better parking control, i.e. application of a system that will prevent the illegal stopping and parking of other vehicles on the reserved surfaces. Nonetheless, due to the obvious effects, initiatives of this type generally have strong social support (Russo and Comi, 2010). 2.2. Criteria for the evaluation of the CL initiatives For the evaluation of the alternatives ten criteria, which are in line with the triple bottom line (TBL) concept of sustainability (Elkington, 1997), are defined in this paper and divided into three groups: social, economic and
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ecological criteria. The criteria are defined based on the literature review (Awasthi and Chauhan, 2012; Tadić and Zečević, 2016; Tadić and Zečević, 2014a), experience of the authors and consultations with the stakeholders of CL. The group of social criteria consists of: mobility - C1, attractiveness of the city, zone - C2, freeing of public spaces - C3, traffic accidents - C4 and noise and vibration - C5. Mobility (C1) refers to changes in the conditions for the movement of passenger and freight vehicles in the city resulting from the application of the initiative. Attractiveness of the city (C2) implies the impact of the initiatives on changes in particular urban areas, zones, which can cause the environment becoming more or less attractive. The freeing of public spaces (C3) refers to the possibility of freeing certain spaces in favor of other, more profitable contents, due to more efficient realization and organization of goods and transport flows as a result of certain initiatives application. Traffic accidents (C4) relate to changes in the participation of traffic accidents caused by the delivery vehicles. Noise and vibration (C5) imply changes in noise and vibration emissions originating from logistics activities in the city, primarily freight transport. The group of economic criteria consists of: reliability and accuracy of the delivery - C6, costs of implementation and control - C7 and accessibility - C8. The reliability and accuracy of the delivery (C6) considers the availability of goods that the customer requires in an acceptable delivery time. The costs of implementation and control (C7) consider the costs of introducing initiatives and control of their application, which can significantly vary for the different initiatives. Accessibility (C8) mostly depend on the part of the city or the area being under consideration and refers to the degree of impact of the initiative on removing the obstacles (physical, regulative or organizational) for the access of traffic participants to the desired destinations, resulting in a reduction of the traffic congestion, number of vehicle kilometers, transport time, number of freight vehicles in the city, noise and gas emission, etc. The group of ecological criteria consists of: energy savings - C9 and air pollution - C10. Energy savings (C9) considers the changes in energy consumption due to the reduction in the number of delivery vehicles and their more efficient use as a result of the application of various initiatives. Air pollution (C10) takes into account the influence of various initiatives on changes in emissions of harmful gases and particles by the delivery vehicles in the city. 3. Hybrid MCDM model for the CL initiative evaluation Various MCDM methods and their combinations have been used in the literature for solving transport and logistics problems (Żak et al., 2018; Lee and Yang, 201; Zečević et al., 2017; Tadić et al., 2014c). This paper proposes a new hybrid model based on the combination of fuzzy DAHP (fuzzy Delphi based fuzzy AHP) and fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) methods for solving the MCDM problem. There are no papers in the literature in which Delphi, AHP and SWARA methods are combined in the fuzzy environment. The model is hybrid because it does not simply compound various MCDM methods, where each method is in charge of solving one part of the problem, but aggregates different segments of the methods making a unique model. The goal was to exploit the advantages of the methods while minimizing their drawbacks. AHP method, as one of the most widely used MCDM methods, was taken as the basis for the model development. However, as the complexity of the AHP method exponentially increases with the number of elements (criteria and alternatives), the SWARA method, which is easier to use especially by the less experienced users, requires considerably less comparisons and does not require consistency check, is introduced for the alternatives evaluation and ranking. As criteria are evaluated by the multiple decision makers (DM), part of the Delphi method is aggregated into the AHP method in the process of obtaining criteria weights, which enabled the integration of different evaluations. As the DMs' judgments on decision factors are often imprecise, vague and ambiguous due to incomplete information or inability of their treatment in a decision environment, all methods are solved in the fuzzy environment. The fuzzy set theory can efficiently deal with the vagueness in thinking and expressing preferences of the DMs. The first part of the model implies the application of the fuzzy DAHP method for establishing connections between the elements of the hierarchical structure and for determining the final criteria weights. The fuzzy AHP method is selected due to its ability to adequately address the hierarchical structure created in this paper by interconnected elements (criteria). Traditional Delphi method is introduced by Dalkey and Helmer (1963) and has since been widely used in various fields. The aim of the method is to collect data from the field of expertise of the respondents. The Delphi method is characterized by anonymity, iteration, controlled feedback, statistical group responses, and stability in responses among the DMs on a specific issue (Shen et al., 2011). The method is extended in the fuzzy environment by Murry et al. (1985). The AHP method developed by Saaty (1980), deals with the
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determination of the criteria relative importance in the MCDM problems. The method allows simultaneous consideration of intangible qualitative criteria, as well as tangible quantitative criteria (Badri, 2001). The fuzzy extension of the AHP method is performed by Van Laarhoven and Pedrycz (1983). In the second part of the model, SWARA method was used to evaluate and select the most favorable alternative, i.e. to select the most favorable political CL initiative from the aspect of sustainability. SWARA method for MCDM was developed by Keršulienė et al. (2010). The method is used to determine the weight (value, importance) of an alternative, and is based on determining the order of importance of the alternative (from the most important to the least important) by the DM or group of DMs based on their knowledge, experience and information. There are no papers in the literature in which the SWARA method is extended in the fuzzy environment. The steps of the proposed hybrid MCDM model are described below. Step 1: Define the problem structure, i.e. form the sets of alternatives and evaluation criteria. Step 2: Define the fuzzy scale for the evaluation of criteria and alternatives by the DMs. Linguistic terms and corresponding triangular fuzzy values are given in Table 1. Table 1. Fuzzy scale for the evaluations Linguistic term
None
Very Low
Low
Fairly Low
Medium
Fairly High
High
Very High
Abbreviations
N
VL
L
FL
M
FH
H
VH
Fuzzy scale
(1, 1, 2)
(1, 2, 3)
(2, 3, 4)
(3, 4, 5)
(4, 5, 6)
(5, 6, 7)
(6, 7, 8)
(7, 8, 9)
Step 3: Obtain the criteria weights by applying the fuzzy DAHP method. Step 3.1: Obtain the pair vise comparisons of criteria in relation to all stakeholders, by applying the linguistic scale which can be transformed into triangular fuzzy numbers by applying the relations given in Table 1. The triangular fuzzy numbers are denoted by a~ijl = (lijl, mijl, uijl) which indicate the importance of the element i in relation to the criteria j, i,j=1,...,n, by the stakeholder l. lijl, mijl and uijl are lower, medium and upper values of the triangular fuzzy evaluation a~ijl . o is the number of the considered stakeholders. Step 3.2: Integrate the assessments by the representatives of various stakeholders by applying the following equations which represent the part of the fuzzy Delphi method (adapted from Mikaeil et al. (2013)): ~
ij ij , ij , ij , ij ij ij
(1)
ij Minlijl , l 1,..., o
(2)
o
ij mijl l 1
1/ o
, l 1,..., o
ij Maxuijl , l 1,..., o
(3) (4)
~ where ij , ij and ij are lower, medium and upper values of the integrated fuzzy evaluation ij , respectively. ~ Step 3.3: By applying the equations (1-4), integrated fuzzy evaluations ij of the elements' pair wise ~ comparisons are obtained, based on which the fuzzy judgment matrix is formed in the following way:
/ ~12 ~1n ~ ~ / 2n ~ 21 ~ ~ n1 n 2 /
(5)
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Step 3.4: Calculate the relative criteria weights. For the pair vise comparison matrix it is necessary to obtain the n ~ priority vector. For obtaining the priority vector ( W w1 ,..., wn 0 , wi 1 ) from the fuzzy matrix the i 1
"logarithmic fuzzy preference programming" (LFPP) method is used in this paper (Wang and Chin, 2011). Elements ~ ~ of the fuzzy comparison matrix ( ) are triangular fuzzy judgments ij ij , ij , ij of comparing element i in relation to element j, taken by the following approximate equation:
~ ln ij ln lij , ln mij , ln uij , i, j 1,2,..., n
(6)
For obtaining the elements' weights (wi) the following nonlinear priority model is proposed: n 1
Min J 1 M 2
ij2 ij2 n
i 1 j i 1
xi x j ln mij / lij ij ln lij , i 1,..., n 1; j i 1,..., n, xi x j ln u ij / mij ij ln u ij , i 1,..., n 1; j i 1,..., n, s.t. xi 0, i 1,..., n, , 0, i 1,..., n 1; j i 1,..., n, ij ij
(7)
where xi=lnwi for i=1,...,n, and M is a specified sufficiently large constant such as M=103. εij and ηij for i=1,...,n-1 and j=1,...,n are the nonnegative deviation variables introduced to avoid membership degree λ from taking a negative value. It is most desirable that these values are as small as possible, and they have to meet the following inequalities:
ln wi ln w j ln mij / lij ij ln lij , i 1,..., n 1; j i 1,..., n,
ln w i ln w j ln u ij / mij ij ln u ij , i 1,..., n 1; j i 1,..., n. Let xi i 1,..., n be the optimal solution to model (7). The normalized criteria weights for fuzzy pair wise
~
~
comparison matrix ij
nn
can then be obtained as:
expx , i 1,..., n,
wi exp xi
n
j 1
j
(8)
where exp() is the exponential function, namely expxi e x for i=1,...,n. This method results in crisp normalized weights. In order to control the result of the method, the Consistency Ratio (CR) for each matrix is calculated as follows (Saaty, 1980): i
CR CI / RI ,
(9)
where CI is the Consistency Index and is calculated as follows: CI max n n 1
(10)
~ max is the principal eigenvalue of the matrix . RI is the Random Index whose values for matrices of various
sizes are contained in Saaty (1996). The comparisons are acceptable only if the CR values are less than 0.10.
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Step 4: Evaluate the alternatives by applying the fuzzy SWARA method. Step 4.1: For each criterion i, arrange the alternatives in decreasing order by the expected importance. Step 4.2: Evaluate the relative importance of alternative k in relation to the alternative (k-1) starting from the second alternative. This relation is called the comparative importance of the average value, and it is denoted as ~ sk , where ~ sk (l k , mk , u k ) , k=1,...,m, is a triangular fuzzy number which corresponds to the linguistic terms given in Table 1. l, m and u denote lower, middle and upper value of the triangular fuzzy number. Step 4.3: Calculate the coefficient ~ rk in the following way:
1,1,1, k 1 ~ rk . u, mk / max u, u k / max u k 1,1,1, k 1,..., m lk / max k k k
(11)
Step 4.4: Calculate the preliminary values of the alternatives q~k in the following way:
1,1,1, k 1 . q~k ~ ~ q k 1 rk , k 1,..., m
(12)
Step 4.5: Calculate the relative preference values of the alternatives v~k in the following way: v~k q~k q~k
(13)
k
The obtained values need to be defuzzyfied by applying the following equation (Kutlu and Ekmekcioglu, 2012):
Crispv~k l kv 4 * mkv u kv / 6
(14)
Step 5: Obtain the final ranking of the alternatives, i.e. the overall utility of the alternatives. First it is necessary to obtain the aggregated weighted values of the alternatives Qk by applying the following equation: n
Qk wi * v k , k 1,..., m
(15)
i 1
By arranging the obtained values Qk in the decreasing order, the final ranking of the alternatives is obtained. 4. Selection of the most appropriate political CL initiative
By applying the proposed MCDM model, the ranking of the political CL initiatives is performed. For the defined problem it was necessary to perform the criteria comparison in relation to the stakeholders' representatives in order to obtain the criteria weights. Experts dealing with the CL problems, acting as the stakeholders' representatives, performed the comparisons of all criteria pairs by giving the linguistic assessments, as shown in Table 2. Evaluations of the stakeholders' representatives are then transformed into the fuzzy values by applying the relations given in Table 1, and then by applying the equations (1) - (4) the integrated evaluations of the criteria comparison are obtained. For the obtained values, the non-linear priority model (7) is solved. After applying the equation (8) for the optimal solutions of the model (7) the following normalized values of the criteria weights are obtained: C1 (0,111), C2 (0,116), C3 (0,072), C4 (0,171), C5 (0,066), C6 (0,121), C7 (0,079), C8 (0,093), C9 (0,098) and C10 (0,073). After obtaining the criteria weights it is necessary to evaluate the initiatives. First, the experts arranged the initiatives in the decreasing orders by the expected importance in relation to each criterion. Then the importance of initiative k in relation to (k-1) initiative is evaluated by each criterion using the relations given in Table 1. The order of the initiatives by each criterion and their evaluations are given in Table 3.
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Table 2. Evaluations of the criteria by the stakeholders' representatives (Res., S&R, Pro., Adm.) C1
C2
C3
C1
/,/,/,/
VL,/,VL,/
VL,N,L,N
N,/,/,/
L,VL,N,N
N,/,/,VL
FL,N,N,/
L,/,/,VL
FL,/,/,N
VL,VL,N,N
C2
/,VL,/,VL
/,/,/,/
N,VL,N, VL
/,N,/,N
VL,L,/,VL
/,/,/,L
L,VL,/,N
VL,/,/,L
L,N,/,VL
N,L,/,VL
C3
/,N,/,N
N,/,N,/
/,/,/,/
/,/,/,/
VL,VL,/,N
/,/,/,VL
L,N,/,/
VL,/,/,VL
L,/,/,N
N,VL,/,N
C4
N,VL,L, VL
VL,N,FL, N
VL,VL,FL, VL
/,/,/,/
L,L,L,VL
N,/,VL,L
FL,VL,L,N
L,/,VL,L
FL,N,N, VL
VL,L,L,VL
C5
/,/,N,N
/,/,VL,/
/,/,L,N
/,/,/,/
/,/,/,/
/,/,/,VL
VL,/,N,/
N,/,/,VL
VL,/,/,N
/,N,N,N
C6
N,L,VL,/
VL,VL,L,/
VL,L,L,/
N,VL,/,/
L,FL,VL,/
/,/,/,/
FL,L,VL,/
L,N,N,N
FL,VL,/,/
VL,FL,VL,/
C7
/,N,N,VL
/,/,VL,N
/,N,L,VL
/,/,/,N
/,VL,N,VL
/,/,/,L
/,/,/,/
/,/,/,L
N,/,/,VL
/,VL,N,VL
C8
/,L,VL,/
/,VL,L,/
/,L,L,/
/,VL,/,/
N,FL,VL,/
/,N,N,N
VL,L,VL,/
/,/,/,/
VL,VL,/,/
/,FL,VL,/
C4
C5
C6
C7
C8
C9
C10
C9
/,VL,L,N
/,N,FL,/
/,VL,FL,N
/,N,N,/
/,L,L,N
/,/,VL,VL
N,VL,L,/
/,/,VL,VL
/,/,/,/
/,L,L,N
C10
/,/,N,N
N,/,VL,/
N,/,L,N
/,/,/,/
VL,N,N,N
/,/,/,VL
L,/,N,/
VL,/,/,VL
L,/,/,N
/,/,/,
C7
C8
Table 3. Order of the initiatives and its evaluations C1
C2
I6
C3
I6
C4
I4
C5
I7
C6
I4
I6
I2
C9
I6
C10
I3
I4
I3
L
I3
L
I6
L
I4
L
I3
L
I8
L
I8
L
I3
L
I4
L
I6
L
I4
L
I4
L
I5
L
I6
L
I7
L
I7
L
I1
L
I1
L
I6
L
I3
L
I7
M
I8
M
I8
M
I3
M
I5
M
I5
M
I5
M
I7
M
I7
M
I7
M
I1
N
I2
N
I3
N
I5
N
I1
N
I1
N
I7
N
I8
N
I1
N
I5
N
I8
M
I7
M
I7
M
I8
M
I2
M
I2
M
I4
M
I4
M
I8
M
I1
M
I2
VL
I5
VL
I2
VL
I1
VL
I6
VL
I4
VL
I3
VL
I2
VL
I2
VL
I2
VL
I5
N
I1
N
I1
N
I2
N
I8
N
I3
N
I6
N
I5
N
I5
N
I8
N
For each criterion the following procedure is repeated. First the values of the coefficient ~ rk are calculated by ~ applying the equation (11). Then the preliminary values of the alternatives q k are calculated by applying the equation (12). For the obtained values the relative preference values of the alternatives v~k are obtained by applying the equation (13), which are then defuzzyfied by applying the equation (14). In order to obtain the final ranking of the initiatives. i.e. the overall utility of the initiatives, the aggregated weighted values of the alternatives Qk are obtained by applying the equation (15), which are then arranged in the decreasing order. All of the above values, as well as the final ranking of the initiatives are presented in the Table 4. The initiative of night deliveries (I6) is obtained as the best ranked political initiative in the given circumstances and for the considered criteria. 5. Discussion
The initiative of night deliveries, I6 (0.218), is obtained as the best-ranked political initiative, under given conditions and for the observed criteria. The initiative is in fact a compromise solution because it was obtained as a result of evaluation by various stakeholders. Many positive effects could be achieved by applying night delivery, identified in the numerous practical testing of this initiative. One such example is testing by a Serbian trade company that performed night deliveries for the retail stores working 24 hours. From the receivers' point of view, the greater delivery reliability can be achieved. From the logistics providers' perspective, lower costs and shorter delivery times can be achieved, and from the aspect of the residents, higher availability of products in the early morning hours, lower emissions and reduced traffic during the day can be achieved (Zečević et al., 2015). According to the above, this initiative is evaluated as the most favorable from the aspects of city attractiveness, freeing of public spaces, reliability and accuracy of delivery and accessibility, whereas these criteria were evaluated as very important by the stakeholders. Moreover, the problem of night
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delivery is the residents' disturbance by the noise, vibration and lights, but also the increased risk and exposure to crime, both staff and goods (Holguín-Veras, 2008). For these reasons, the initiative is poorly assessed from the aspect of noise and vibration and the costs of implementation and control. Solution of the problem also demonstrated the applicability of the proposed model, which is not limited solely to solving this type of problems. With certain adjustments it could be also used to solve some other problems from the field of logistics, such as defining the distribution network, i.e., the number and allocation of different categories of logistics centers, logistics systems technology selection, logistics provider selection, etc., as well as for the problems in other fields. Table 4. Results of the initiatives evaluations and the final ranking C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
wi
0.111
0.116
0.072
0.171
0.066
0.121
0.079
0.093
0.098
0.073
Qk
Rank
I1
0.089
0.019
0.014
0.029
0.055
0.057
0.175
0.170
0.107
0.042
0.072
6
I2
0.034
0.097
0.026
0.016
0.030
0.032
0.332
0.047
0.035
0.023
0.061
8
I3
0.222
0.219
0.077
0.128
0.263
0.010
0.021
0.206
0.236
0.183
0.153
3
I4
0.168
0.181
0.320
0.235
0.359
0.018
0.039
0.082
0.209
0.294
0.181
2
I5
0.018
0.036
0.169
0.080
0.102
0.100
0.113
0.025
0.019
0.079
0.070
7
I6
0.294
0.266
0.242
0.190
0.016
0.368
0.011
0.232
0.186
0.242
0.218
1
I7
0.128
0.063
0.046
0.291
0.177
0.166
0.069
0.141
0.154
0.139
0.150
4
I8
0.063
0.138
0.118
0.050
0.009
0.256
0.250
0.117
0.065
0.012
0.110
5
6. Conclusion
For the efficient realization of logistics activities in the city, all functions and all structures of the city are interested (shippers, receivers, carriers, logistic providers, residents, administration). They all have a common goal: an attractive city in terms of economic, social, traffic, ecological, cultural and other criteria. However, individual goals are in conflict and the change, which is perceived as positive by one group, can cause a number of negative effects to others. In order to improve sustainability, different CL initiatives have been defined. The goals and effects of the initiatives differ depending on the initiator, the application possibilities, the structure and characteristics of the generators of goods flows, etc. In this paper, the ranking of the sustainability of the political initiatives for solving the problems of city logistics is performed, taking into account the requirements and objectives of different stakeholders. To solve the problem, a new hybrid MCDM model which combines Delphi, AHP and SWARA methods in the fuzzy environment is defined. This model, as well as the original approach to problem solving that address and integrate the different stakeholders' attitudes in order to obtain a compromise solution, are the main contributions of this paper. Future research could move towards analyzing and evaluating a wider set of initiatives, measures and concepts of city logistics. In addition, the defined model could also be used to solve problems from other areas. Moreover, new hybrid models which could include the proposed model or some of its parts could also be developed for solving various problems. References Abel, H. 2006. Urban Freight Management in Barcelona (Spain), in "BESTUFS - Best Practice Handbook 2006". In: Abel, H., Karrer, R. (Eds.), BESTUFS II. Aiura, N., Taniguchi, E. 2006. Planning on-street loading-unloading spaces considering the behaviour of pick-up/delivery vehicles. In: Tanigucji, E., Thompson, R.G. (eds), Recent advances in city logistics, Elsevier, Oxford, 107–116. Alho, A.R., Silva, J.A. 2014. Analyzing the relation between land-use/urban freight operations and the need or dedicated infrastructure/enforcement - Application to the city of Lisbon. Research in Transport Business & Management 11, 85–97. Anderson, S., Allen, J., Browne, M. 2005. Urban logistics - how can it meet policy makers' sustainability objectives? Journal of Transport Geography 13.1, 71-81. Arvidsson, N. 2013. The milk run revisited: A load factor paradox with economic and environmental implications for urban freight transport. Transportation Research Part A: Policy and Practice 51, 56–62. Awasthi, A., Chauhan, S.C. 2012. A hybrid approach integrating Affinity Diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Applied Mathematical Modelling 36, 573–584.
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