MINES Simon Tamayo

LOADING/UNLOADING SPACES LOCATION AND EVALUATION: AN APPROACH THROUGH REAL DATA

Keywords: urban logistics, loading/unloading spaces, delivery bays, optimization model, collaborative data

This study presents a real-data approach that aims at optimizing the location of urban loading-unloading (L/U) spaces. The originality of this paper is twofold: first, it proposes a data collection methodology in order to integrate real and up-to-date information regarding cartography, L/U parking demand and existing L/U spaces. Second, an optimization model is developed in order to determine the location of new L/U spaces by taking into account real distances, influence radius and physical constraints. Such optimization model can be used to evaluate the relevance of the existing L/U spaces and/or to determine the optimal location of new ones. For this we propose to combine the use of OpenStreetMap, Google Maps APIs, and Open Data portals. This paper provides a framework composed by 3 models, namely: data collection, demand generation and location optimization. The proposed approach is applied to the city of Paris in order to illustrate different case studies and to assess the effectiveness of the framework. Demand and potential spots for L/U spaces Pseudo-optimal location of 10 L/U spaces Existing L/U spaces and pseudo-optimal locations Result of the pairing function

INTRODUCTION

Urban freight represents between 15% and 30% of the kilometres driven by vehicles in urban areas (Janjevic et Ndiaye, 2013;Russo and Comi, 2012;Dablanc, 2007;Schoemaker et al., 2006;Kenworthy et al., 1999). Despite this relatively low percentage urban deliveries have disproportionally high impacts. Moreover, urban freight confronts numerous difficult problems, such as high levels of traffic congestion, negative environmental impacts, energy consumption, social nuisances, etc. (Lebeau, 2016;Janjevic andNdiaye, 2013, Taniguchi et al., 2001). Several studies have explored these impacts that can be broken down into three types:

• Environmental impacts: which are related to energy consumption and emissions (Browne et al., 2007). In France it is estimated that freight transport is responsible for between 16% and 50% (depending on the pollutant considered) air emissions (Dablanc, 2007). • Social impacts: which are related to the emissions of pollutants affecting public health, nuisance such as congestion, noise disturbances, accident rates, visual contamination, smell, vibration. All these externalities leading to reduce the attractiveness of urban centers. (Kim and Afzal, 2014;Quak, 2008;OECD, 2003;Van Binsbergen and Visser, 2001). • Economic impacts: which are mainly associated with transportation costs (Janjevic and Ndiaye, 2013;Hicks, 1977). It is estimated that the final transport delivery in town (last mile) represents 28% of the total cost of the transport (Wittlöv, 2012).

In this context L/U, spaces aim at facilitating the logistic operations associated with physical flows within urban areas. Numerous works have explored the problem of optimizing the location of L/U spaces.

Some authors and institutions have worked on determining best practices and recommendations when planning the location of L/U spaces. In 2009, the CERTU (French centre for networks, transport, and urban planning) issued a method to help planning such spaces [START_REF] Certu | Aménagement des aires de livraison -Guide pour leur quantification, leur localisation et leur dimensionnement[END_REF]. The method proposes a rough quantification of the needs for L/U spaces and a set of recommendations for improving their management. [START_REF] Dezi | Urban freight transport in Bologna: Planning commercial vehicle loading/unloading zones[END_REF] suggest technical solutions to improve the sizes, quantities and locations of L/U spaces with a practical application to the city of Bologna. [START_REF] Jaller | Parking in the City[END_REF] propose a set of policy recommendations in order to increase L/U spaces availability; New York City is used as a case study.

Other authors have proposed models to optimize the location and/or the management of L/U spaces. [START_REF] Aiura | Planning On-Street Loading-Unloading Spaces Considering the Behaviour of Pickup-Delivery Vehicles and Parking Enforcement[END_REF] propose a model to identify the optimal location of L/U spaces in terms of delay, cost, and traffic. [START_REF] Delaître | Mixing two French tools for delivery areas scheme decision making[END_REF] suggest a combination of two tools (FRETURB and DALSIM) and give an implementation example in the city of La Rochelle. [START_REF] Alho | A State-of-the-Art Modeling Framework to Improve Congestion by Changing the Configuration/Enforcement of Urban Logistics Loading/Unloading Bays[END_REF] develop a modelling framework for cases with lowdata availability based on six interdependent models. Gadrat and Serouge (2015) present a hybrid method (FRETURB model and the CERTU method) in order to quantify the demand of L/U spaces (often called delivery bays, or delivery parking areas).

The problem of locating L/U spaces still offers scientific challenges, especially due to the heterogeneity of information and the great complexity of logistical flows. This work contributes to the existing literature by proposing an approach that public authorities can use to capture urban needs quickly and effectively. Unlike the aforementioned works, the framework proposed in this paper allows the integration of real-data regarding cartography, logistic demand and existing L/U spaces. As a result, it gives decision-makers the possibility to:

• Quantify the need of L/U spaces by considering up-to-date businesses information.

• Optimally locate new L/U spaces taking into account the real cartography, up-to-date establishments needs, real distances (considering traffic) and current L/U spaces. • Evaluate the pertinence of L/U spaces' current location. The main motivation of the proposed approach is to enhance the use of all available data in the planning of L/U spaces. In this paper we suggest using free and/or collaborative data with 3 objectives: expedite the data collection process; feed the optimization model with pertinent inputs and allow a simple replication of the proposed framework in other cities.

PROPOSED APPROACH

This approach is developed from the standpoint of local authorities, that aim at finding a trade-off between sustaining the commercial dynamism related to local businesses and the scarcity of parking surfaces for inhabitants [START_REF] Certu | Aménagement des aires de livraison -Guide pour leur quantification, leur localisation et leur dimensionnement[END_REF]. The objectives of other stakeholders are also related to the notion of commercial dynamism; for instance, logistics providers and transporters need available L/U spaces closer to their drop-off points.

The proposed framework for L/U spaces location and evaluation is divided in three steps: 1) data collection (information on the urban area); 2) demand generation (needs regarding L/U spaces); 3) optimization (locations of L/U spaces), as shown in figure 1. In each step of the framework, the decision-maker has different choices that should enhance the flexibility process of locating and evaluating L/U spaces. In the data collection step, it is not necessary to gather all the information. For instance, it is possible to collect only the information regarding the businesses and do not take into account the existing L/U spaces (if such information is not available) or the real distances (as they can be estimated with an Euclidian calculation).

In the demand generation step, three options are available to quantify the needs of the businesses and the decision-maker can choose the most convenient.

Finally in the optimization model, three use cases are proposed: 1. Evaluation of the existing spaces, i.e. are the actual L/U spaces well adapted to the needs of the urban zone? 2. Optimization of the location of L/U spaces with no information about the existing ones, i.e. where to locate a given number of L/U spaces? 3. Optimization of the location of new L/U spaces, taking into accounts the locations of the existing spaces, i.e. where to add new spaces?

In the following sections each step of the framework is explained with more detail.

DATA COLLECTION

The data collection aims to numerically replicate important features of the area of interest. It consists in gathering information about: the businesses (location and activities), the location of existing L/U spaces (if any), and the distances between businesses and L/U spaces (which will be walked by delivery employee). Performing a local survey to gather such information can be tedious and expensive. Today's online cartography services, APIs and Open Data portals are reliable sources of information. We propose using these sources of information in order to carry out the data collection.

The typical outputs from the data collection for the businesses and existing L/U spaces would be similar to those shown in Table 1. In this example, information about businesses is obtained from OpenStreetMap and Google Maps (Google Places API), and information about the existing L/U spaces is obtained from the City of Paris Open Data portal (opendata.paris.fr). The data collection should also identify the potential spots in which new L/U spaces could be added. The aim is to determine the coordinates of the potential L/U spaces; which can be obtained by either a discretization of the roads in the area of interest; or by manually determining specific points. It is important to note that in this step the decision-maker can adapt the data to fit the local of policy of parking management. For example parking locations assigned to other uses (residential, disabled, banking, police, etc.) can be removed from the list of potential spots.

The final step of the data collection consists in calculating a distance matrix between the L/U spaces and the businesses. The distance matrix can be obtained by querying an online service cartography API such as Google Maps API, or it can be simply calculated with Euclidian distances for simpler applications. An example of the obtained matrix is shown in Table 2. The distance matrix allows an easy access to the information about the proximity of business around each L/U space. For instance, one row represents the distances of all businesses from one L/U space, and if we know which businesses are the most demanding in terms of frequency movements, we can efficiently locate new L/U spaces. This leads us to the second step of our framework presented next.

DEMAND GENERATION

The demand generation aims at quantifying the generated flow of loadings and deliveries for each business. In this part of the framework, the parking demand of each business can be calculated with a statistic-based estimation (2 choices are proposed in the next section) or with a local survey. The output of the demand generation is a list of all the businesses in the area of interest with their coordinates and their demand (i.e. frequency of loading or unloading operations). Visualizing the demand in a map allows the identification of areas with a high concentration of delivery needs.

Statistic-based estimations

Statistic-based estimations are basically time-series extrapolations of past demand. This approach is based on surveys and statistical analysis. In our framework, two options are available to quantify the demand of each business following a statistic-based estimation, namely (1) the CERTU method and (2) a statistic approximation based on an adaptation of the report of [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF]. As these are estimations, the information they generate will inevitably be imperfect in terms of level of precision; but it is to note that the cost of generating such information is practically insignificant.

CERTU Method

The CERTU method is comprehensive guide for quantifying, locating and dimensioning L/U spaces. In this method onsite survey is carried out in the studied area. Each business is associated with a type of activity, which is accordingly related to an average number of logistic movements per week. In total, CERTU proposes fourteen types of activities, each having its own frequency of movements as shown in Table 3. This table was generated from an update of a national survey done in France in the 1990s. The update has been achieved by using the French national register of businesses (SIRENE). The CERTU method has a practical approach to characterize the need of L/U spaces: besides the frequency of delivery, each type of activity has specific need in terms of organization, size and/or regulation. For example, although jewellery and furniture stores are relatively similar in terms of frequency, the deliveries for the latter are made with larger trucks.

Statistic approximation based on an adaptation of the Review of UK Urban Freight Studies

In the proposed framework, the second option to generate the demand of the urban zone consists in using historical data to approximate the frequency of movements of each business. In this study we propose an adaptation of the report of [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF]. It is important to note, that this type of approximation gives only an average estimation of the demand. As the logistic need has local specificities and varies from one city to another, there is inherent error when adapting existing estimations to new cities.

The Review of UK Urban Freight Studies [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF] summarizes the results of 30 UK urban freight studies carried out between 1996 and 2008. This report proposes a broad view in different cities in UK of urban freight activities: number of movements, type of deliveries, type of vehicles, patterns in vehicle deliveries, etc. Allen et al. highlight some difficulties when using existing surveys: first, many of the studies have a relatively small sample sizes, which might not be representative of the reality. Second, one must be careful before using studies when raw data are not available. Indeed, one topic can be investigated using different techniques (i.e. observation survey and establishment survey), and even when the same technique is used, questions can be phrased in different ways. Table 4 presents an adaptation of [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF] in which, for each type of establishment the number movements per week is approached by taking into account historical data. In order to adapt this data to the city of Paris, the types of establishments were adjusted to match the categories of businesses used by online cartography services, APIs and Open Data portals. It is important to note that the use of the estimations in the literature as an input for the demand generation must be done watchfully. The delivery estimations and especially their segmentation can contain a bias that will influence the optimization outputs.

Local survey

Local survey consists in creating a questionnaire to assess the logistic needs of businesses in the urban area. The main advantages of preforming a survey are acquiring up-to-date data with an important level of precision (i.e. distribution of the movements of goods in the week and in the day? volume? special requirements? etc.). This would help to avoid the bias of using data meant for another purpose and collected in another city or country. Nevertheless, performing local surveys for every L/U spaces planning decision, would lead to important costs for local authorities. Therefore statistic-based estimations seem to be a reasonable trade-off between cost effectiveness and level of precision.

OPTIMIZATION MODEL

The goal of the optimization model is to locate L/U spaces close to businesses with higher logistic demand. As shown in the previous section, this demand is modelled by the frequency of movements of goods for each business. A genetic algorithm (GA) is used to determine the optimal (or pseudo optimal) location of the L/U spaces. Such tools have proven their pertinence when solving facility location problems [START_REF] Fernandes | A simple and effective genetic algorithm for the two-stage capacitated facility location problem[END_REF][START_REF] Tosun | A New Recombination Operator for the Genetic Algorithm Solution of the Quadratic Assignment Problem[END_REF][START_REF] Lalla-Ruiz | A Hybrid Biased Random Key Genetic Algorithm for the Quadratic Assignment Problem[END_REF].

Mathematical model

The objective function for the localization of L/U spaces is the minimization of the weighted distance between them and the businesses creating the demand. The distance can be calculated either using the Euclidian distance between the two points, or the real distance can be queried using Google Maps API. The function considers that each L/U space has a range of action, in which all parking demand is fulfilled. To model this range, we use an influence radius parameter.

Each solution of L/U spaces allocation is represented by a binary vector in which each position of the vector is linked to an available spot (the size of the vector is equal to the number of available spots). A value of 1 indicates that there is an L/U space in the current position. The total number of ones in the vector will be equal to the L/U spaces to locate in the problem. The mathematical model is described as follows:

Minimize : Z = x j w i d ij i=1 n ∑ j=1 m ∑ with : x j ∈ {0;1} x j j=1 m ∑ = Q d ij (b i , x j ) = f (lat bi , lon bi , lat xj , lon xj ) w i d i ∀j = 0, w i d i , if if d ij (b i , x j ) ≤ r ij d ij (b i , x j ) > r ij ⎧ ⎨ ⎪ ⎩ ⎪ (1)
where,

x j : binary variable indicating if an L/U space is located in the available location j b i : business i Q : quantity of L/U spaces to locate w i : weight of the business i, given by its frequency of movements of goods In the example of Figure 2, two L/U spaces are located: s 1 and s 2 ; in order to satisfy the demand of three businesses: b 1 , b 2 and b 3 .

The weight of each business is given by its frequency of movements per period: w 1 =2; w 2 =5; w 3 =10. The distances d ij between the spaces i and the businesses j are: d 11 =80m; d 11 =70m; d 13 =55m; d 21 =90m; d 22 =75m; d 23 =10m. b 3 is within the influence radius zone of s2, as a result its demand is considered as fulfilled (w 3 =0). The weighted function of this configuration is:

(2)

Optimization algorithm

The optimization is carried out using a genetic algorithm (GA). This allocation problem belongs to the family of Quadratic Assignment Problems, in which there are a set of facilities and a set of locations. For each pair of locations, a distance is specified and for each pair of facilities a weight or flow is specified. The objective is to assign all facilities to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding flows [START_REF] Grosnan | Intelligent Systems: A Modern Approach[END_REF]. This problem is known to be NP-complete [START_REF] Garey | Computers and Intractability: A Guide to the Theory of NP-Completeness[END_REF] for which genetic algorithms have proven their performance to provide good solutions in reasonable computation time [START_REF] Tate | A genetic approach to the quadratic assignment problem[END_REF][START_REF] Tosun | A New Recombination Operator for the Genetic Algorithm Solution of the Quadratic Assignment Problem[END_REF][START_REF] Lalla-Ruiz | A Hybrid Biased Random Key Genetic Algorithm for the Quadratic Assignment Problem[END_REF].

Genetic algorithms are metaheuristic methods inspired by the process of natural evolution. They apply the principles of natural selection by "evolving" a population of potential solutions to the optimization problem [START_REF] Tamayo | Recherche opérationnelle appliquée à la gestion industrielle[END_REF]. According to the evolutionary principles, after a certain number of generations, the solutions of the population will be more and more adapted to the problem. The main steps of a genetic algorithm are:

• Selection: which is intended to keep information from the best individuals, i.e. the solutions that are most likely to reproduce and survive. In our application, the best solutions are the ones that locate L/U spaces closer to businesses with higher logistic demand; the selection operator ensures that such solutions will have more chances of reproducing and surviving. • Reproduction (crossing): which allows generating new solutions from the selected individuals (parents) by "inheriting" their information. The new solution (son) will have a part of the L/U spaces located in the same places as its father, and the other part in the same places as its mother. • Mutation: which is applied to randomly modify the solutions. This helps the algorithm in escaping local optima of the optimization landscape. The mutation operator changes the location of one or more L/U spaces in the mutated solution according to a given mutation rate. These operators have been developed to meet the specificities of the L/U spaces location problem. Figure 3 illustrates the encoding of solutions as well as the operators of reproduction and mutation. In this example there are 18 available spots for locating 5 L/U spaces (m=18, Q=5). The solution "FATHER" locates the new spaces in the spots 2, 6, 7, 13 and 16. The solution "MOTHER" locates the new spaces in the spots 3, 4, 8, 10 and 17. As shown in Figure 3 the crossover operator must combine the locations of the two parent solutions while producing a valid son (i.e. all solutions must have the same number of L/U spaces). The output of the crossover is the solution "SON", that locates the new L/U spaces in spots 2, 6, 8, 10 and 13. Subsequently this solution undergoes a mutation in which some of the locations are

Evaluation of existing L/U spaces

In order to evaluate the pertinence of a given set of existing L/U spaces, the following evaluation process is performed: 1. The existing L/U spaces are ignored and optimal localizations are computed for the same number of spaces. 2. A paring function is used to compute the number of "real" L/U spaces within the influence zone of "optimal" spaces. The pairing function measures the distance between each "real" place and the nearest "optimal" place, if this distance is less than or equal to the radius of influence, then the locations are considered to be equivalent and the function validates the pairing. 3. A pertinence indicator P is computed by comparing the validated pairings (i.e. the relevant L/U spaces) to the total number of L/U spaces as follows:

0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 1 2 3 4
0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 FATHER MOTHER 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 SON 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 MUTATED SON 0 1 2 3 4
P = number of validated pairings number of existing L /U spaces (2)

APPLICATION AND FINDINGS

The proposed framework was applied to the 5th arrondissement of Paris. Three scenarios were explored (1) the location of 10 L/U spaces if there are no prior spaces in the area;

(2) the location of 2 new L/U spaces taking into account the existing spaces and (3) the evaluation of the existing L/U spaces in the area.

Data collection and demand generation

OpenStreetMap and Google Maps (Google Places API) have been used to gather the location and the type of businesses (red dots in Figure 5 The demand generation was performed by statistic estimation. The report of [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF] was adapted to the City of Paris and such adaptation was used to assess the number of movements per week for each type of business. It is important to note that the obtained frequencies are similar to those indicated in the CERTU method. Authors chose to use [START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF] as the diversity of businesses characterized was more relevant. In Figure 5 (right) the halo around each business represents its logistic need. At this point it is easy to have a rough idea if actual L/U spaces are well located.

On-site survey showed that none of the businesses in the area had a private L/U space. If it were the case, as the logistic need would be fulfilled, the weight of establishment would be marginal (see equation 1). On-site survey also revealed that the information gathered about almost every business and L/U spaces were correct.

Given the characteristics of this area of Paris, only on-street L/U spaces have been considered. For this purpose, the streets have been discretized in order to propose candidate locations. The discretization was set to one L/U space every 20 meters along a street (this density of possible spots is a parameter that can be modified if need be). In order to better fit to the specificities of each urban area, it is possible to remove candidate locations (e.g. in front banks, next to police stations, etc.) or to add candidate locations (e.g. off-street L/U spaces). For the use cases given hereafter, the radius of influence was set to 50 meters. This assumption is made to imply that drivers will use L/U spaces if they are less than a half a block away from the delivery points. The obtained locations are coherent to the logistic need of the area, i.e. they are in the hot areas in terms of frequency of movements. It is interesting to note that the calculation time for this optimization is very low (these results are obtained quasi-instantaneously).

Location

Location of 2 new L/U spaces taking into account the existing spaces

This use case explores the problem related to the creation of new L/U spaces while considering the existing ones. As there are L/U spaces in the area, the demand of some businesses is already fulfilled (i.e. the business is within the radius of influence of at least one space); therefore such demand is not considered for the optimization (see Figure 7 The 2 new L/U spaces seem to be appropriately located (Figure 7 right) as they are placed in high demand zones. These results are encouraging as they allow to better meet the needs of an evolving area that can show a given growth in terms of commercial activity.

Evaluation of the existing L/U spaces in the area

This use case considers the evaluation of the 29 existing L/U spaces in the area of interest (grey dots in Figure 8 left). In order to evaluate the relevance of these "real" spaces, the optimization model is run to locate the same number of spaces without taking into account the existing ones (as done in the first use case). Subsequently, a pairing function is used in order to identify the equivalent L/U spaces, i.e. those within the radius of influence of the pseudooptimal ones. The validated pairings are shown in Figure 8 (right).

The pairing function returns 22 equivalent L/U spaces. This information is used to compute the pertinence indicator as follows:

P = 22 29 = 76%
This result shows that most of the existing L/U spaces are mostly well located. In fact, most of them are within (or close to) the hot areas in terms of movement frequency. Nonetheless it is important to highlight that the pertinence result does not necessarily indicates that the existing L/U spaces satisfy exactly 76% of the logistic need of the area. This result gives only Updated demand and existing L/U spaces Pseudo-optimal location of 2 new L/U spaces a rough representation of the correspondence between the existing infrastructure and the pseudo-optimal configuration given by the optimization algorithm. Moreover, it is to note that the pertinence result depends highly on the radius of influence, which in this case was set to 50 meters (roughly half a block). Such radius can be considered larger or shorter depending on the parking behaviour and the sanctioning policies in place.

CONCLUSION

This paper proposed a framework for evaluating and optimally locating L/U spaces in urban areas. The framework is divided in three parts, namely: (1) data collection, in which relevant information about the urban area is gathered from online cartography services, APIs and Open Data portals; (2) demand generation, in which the logistic need of each business is estimated form its frequency of movements of goods and (3) optimization model, in which the location of the existing L/U spaces can be evaluated, or new spaces can be optimally located.

This paper opens the way to use new information tools for a rather old problem. First, a survey revealed that the information available online is a good representation of the reality of this area. Second, despite the algorithm's simplicity, the application shows that the framework proposes coherent results. It is a promising result for the development of more sophisticated algorithms.

The proposed framework was designed to embody simplicity, openness and flexibility. As the framework is simple, decision-makers (particularly local authorities) can easily apply it to other cities. The openness of the framework allows actors to enhance it and provides easy access to the statistic approximation data. The flexibility of the framework admits the use of exhaustive information, if available, but it can also operate if less information is provided. E.g. the demand generation can be performed by a local survey (exhaustive information) or can be estimated (non-exhaustive information).

The framework has been developed from a local authorities' perspective, which aim at finding an optimal trade-off between sustaining the commercial dynamism (i.e. provide L/U spaces close to local businesses) and cope with the scarcity of parking surfaces. This approach allows decision-makers to satisfy the constraints regarding the quantity of L/U spaces in ac-cordance to the local parking management policy (i.e. preserve some residential parking, provide disabled parking, etc.), while permitting the optimal location of the L/U spaces in terms of commercial dynamism.

There are three strong hypotheses in the proposed framework. First, the model for the location of L/U spaces is fairly simple for it assumes that carriers will park in the L/U spaces in a given influence radius; double-parking is not studied and L/U spaces sizes are not considered. Second, the demand generation is quantified on a weekly average basis. However, previous studies show that urban freight has peak hours during the day [START_REF] Muñuzuri | Modelling peak-hour urban freight movements with limited data availability[END_REF][START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF]. As a result, the proposed estimation of "frequency of movements" lacks the necessary level of detail to model situations such as simultaneous arrivals at a single L/U space, which could happen during peak hours. Third, the size of the businesses has a significant impact on the frequency of movements, which is not yet considered.. As a perspective, upcoming versions of our optimization model should take into account double-parking as well as L/U spaces sizes. Moreover, the next stages in the data collection will explore the possibility of quantifying the parking demand during the different hours of the day. This will allow a better location of L/U places and could set the cornerstone for future models of dynamic allocation of L/U spaces. Moreover, further versions of the framework should take into account the size of the businesses in the demand generation.

Another interesting perspective of this work would be a large-scale application to a metropolis. Such an application would undoubtedly generate interesting results to understand the needs of the different sectors as well as the quality in the location of the existing L/U spaces.

The proposed tool yields partial solutions to a rather complex problem. It is clear that the pseudo-optimal solutions generated by the model lack of detail and could integrate other optimization criteria. However, it should be remarked that the framework presented here allows the integration of different sources of information and helps in tackling, at least in part, the heterogeneity of the urban movements. Indeed, the use of collaborative information opens up passionate perspectives towards integrating the complexity of the urban logistics ecosystem.

Figure 1 .

 1 Figure 1. Structure of the proposed framework

  Figure 2. Example of the objective function b 1

Z

  = 1065 meters * movements / period randomly changed; in this case the L/U spaces of spots 6 and 13 are relocated to spots 5 and 13 respectively.

Figure 3 .

 3 Figure 3. Operators of the genetic algorithm

Figure 4 .

 4 Figure 4. Convergence of the optimization algorithm

  left). The locations of the L/U spaces are available on the City of Paris Open Data portal (grey dots in Figure 5 left). Google Maps (Google Distance Matrix API) has been used to evaluate the real distances between the businesses and the L/U spaces. The locations of the potential spots for new L/U spaces have been obtained by discretizing the streets in the local area (black dots in Figure 5 right).

Figure 5 .

 5 Figure 5. Data collection results for the 5 th arr. of Paris

  of 10 L/U spaces if there are no prior spaces in the area This use case considers the location of 10 L/U spaces in an area where no L/U space yet exists. The algorithm considers the original demand (Figure 6 left); it aims at finding the optimal locations of 10 L/U spaces. The white circles indicate the pseudo-optimal locations for this urban zone (Figure 6 right).

Figure 6 .

 6 Figure 6. Location of 10 L/U spaces

  left).

Figure 7 .

 7 Figure 7. Location of 2 new L/U spaces taking into account the existing spaces

Figure 8 .

 8 Figure 8. Evaluation of the relevance existing L/U spaces in the area

Table 2 .

 2 Distance matrix between L/U spaces and businesses

		Business 1	Business 2	Business 3	…
	L/U space 1	15.6 m	0.6 m	0.6 m	…
	L/U space 2	13.8 m	234 m	234 m	…
	!	!	!	!	!

Table 3 .

 3 Movements of goods per week for each type of activity[START_REF] Certu | Aménagement des aires de livraison -Guide pour leur quantification, leur localisation et leur dimensionnement[END_REF]

	Type of activity	Movements Type of activity	Movements
	Supermarket (≥ 400 m2)	83.94	Local craft and SMBs	7.81
	Pharmacy	31.76	Others (jewellery, sports, …) 7.53
	Wholesale	21.67	Furniture store	7.5
	Bookshop, office supplier	13.8	Cafés, hotels, restaurants	6.25
	Butcher	10.5	Clothes shop	3.53
	Supermarket (< 400m2)	9.53	Tertiary and artisans	2.43
	Bakery	8.07

Table 4 .

 4 Movements of goods per establishment, adaptation based on[START_REF] Allen | Review of UK Urban Freight Studies (Work Module 9)[END_REF]

	Establishment	Movements Establishment	Movements
	Convenience grocery	92.5	Department store	12.0
	Supermarket	60.0	Bakery	10.0
	Chemist/pharmacy	37.0	Pizza restaurant	10.0
	Bulders merchant	35.0	Other services	9.7
	Book shop	32.5	Office supplies shop	9.0
	Newsagent	25.0	Florist	8.0
	Hotel	24.5	Printing/photocopy shop	6.0
	Furniture shop	22.0	Shoe shop	5.5
	Public house	19.5	Clothes shop	4.0
	Computer shop	18.0	Fast food restaurant	3.0
	Hardware shop	18.0	Gift shop	3.0
	Pub/café	16.0	Travel agent	2.0
	Variety store	15.0	Dry cleaning	1.0
	Cinema	12.0

ACKNOWLEDGMENTS

This work is supported by ADEME (French environment and energy management agency), La Poste, Marie de Paris (City of Paris), Pomona Group and RENAULT, within the context of the Urban Logistics Chair at MINES ParisTech.


MINES Sarra Jlassi

SIMULATION APPLIED TO URBAN LOGISTICS: A STATE OF THE ART

Keywords: urban logistics, simulation, state of the art, literature review

This work provides a review of simulation techniques applied to urban logistics with a focus on practical applications. Several techniques and tools have been proposed to simulate different systems in urban logistics. In most cases, simulation choices depend on the objective of the simulation, the role of the decision maker and the type of problem. The paper offers a state of the art in which we analyse the existing simulation solutions for a given problem and/or a given stakeholder. As a result, it offers a practical reference for urban logistics researchers and practitioners who wish to use simulation in order to study the behaviour and performance of their systems. We propose an analytical framework allowing an easy overview of advantages and drawbacks of each technique, output criteria and examples.

INTRODUCTION

The world's global population is increasingly concentrating in cities, 52% of the population currently lives in urban areas and by 2050 this number is expected to reach 67% (World Urbanization Prospects, 2014). Urban logistics becomes a major issue of interest for both municipal authorities and the Transport & Logistics Industry. For people, urban logistics ensures the supply of goods in stores and for firms it forms a vital link with suppliers and customers [START_REF] Crainic | Advanced freight transportation systems for congested urban areas[END_REF]. For local authorities, the inexpensive and quick movement of goods is crucial for ensuring that their cities remain competitive, attractive and environmentally friendly. For logistics providers and transporters, last-mile logistics can be expensive and complex to manage, especially with the growth of new channels of distribution notably e-commerce (see [START_REF] Durand | Urban Logistics and E-Grocery: Have Proximity Delivery Services a Positive Impact on Shopping Trips?[END_REF]. In the last decades, a notable increase in the number of vehicles is noticed as well as a saturation of transportation infrastructures [START_REF] Cagliano | ITS for e-grocery business: The simulation and optimization of Urban logistics project[END_REF]. Consequently, congestion, noise, accidents, transportation delays, infrastructure degradation, and polluting emissions are some of the most serious problems transportation and city managers have to deal with.

As the importance of urban logistics increases several authors have explored best practices, regulatory measures and organization schemes for improving urban freight in terms of cost, traffic congestion, logistic efficiency, environmental nuisances, or other criteria depending on the stakeholders taken into account. In order to address these stakes, many modelling techniques have been implemented and documented [START_REF] Bozzo | Decision support system for city logistics: Literature review, and guidelines for an ex-ante model[END_REF], Comi et al., 2014). Nonetheless, in many cases the developed models do not lead to simulation applications.

The paper proposes a state of the art of the existing simulation techniques in the field of urban logistics. Section 1 aims at clarifying the ambiguity between the concepts of modelling and simulation. A brief review on the main models related to urban logistics in Europe is given. In section 2, the method used for literature review is described. Section 3 proposes a review and classification of the publications dealing with simulation in urban logistics. A synthetic view is presented as a table, where each reviewed publication is characterized in terms of scope, stakeholder and output. After exploring the literature referring to simulation in urban logistics, it was observed that the number of papers ending up with real simulation applications is limited. In this context, our contribution is to propose an analytical framework allowing an easy overview of the existing simulation techniques, their inherent choices and their advantages and drawbacks. This framework, described in section 4, examines the choices of simulation techniques carried out in the different publications, the typologies of problems that are approached with similar simulation tools and the software solutions chosen by the authors. A synthesis of the opportunities for simulation in urban logistics is proposed and finally the last section provides some final conclusions and directions for future work.

Modelling Vs Simulation

It is important to distinguish between modelling and simulation. A model is a representation of a system. It can be described qualitatively with an influence diagram or quantitatively using mathematics. The model is similar to but simpler than the system it represents [START_REF] Maria | Introduction to modelling and simulation[END_REF]. A good model is a judicious trade-off between realism and simplicity. A simulation is the operation of the model. It is the process of using a model in order to understand how systems behave over time and to estimate and evaluate their performances. As described in [START_REF] Garrido | Introduction to Elementary Computational Modeling: Essential Concepts, Principles, and Problem Solving[END_REF] the notion of simulation relates to a set of techniques, methods, and tools for developing a simulation model of a system and using and manipulating such model to gain more knowledge about the dynamic behaviour of the system. In simpler words, simulation consists in asking "what if" questions about the real system and observing the impacts of variables change over time on the behaviour of the system. From this standpoint, simulating implies modelling, but modelling does not necessarily imply simulating. Important models have been developed in the field of urban logistics, particularly to estimate movements of goods. Two of the most present models in the literature are the models WIVER and FRETURB. The WIVER model, developed in Germany by [START_REF] Sonntag | A Computer Model of Urban Commercial Traffic, in Transport[END_REF], produces O/D matrices for road-based urban goods movements and other commercial related activities. The FRETURB model, developed in France by [START_REF] Routhier | FRETURB V3, a policy oriented software tool for modelling urban goods movement[END_REF], estimates the movements of commercial vehicles in a given urban area on the basis of a set of socioeconomic variables. Based on the same approach implemented in FRETURB, [START_REF] Gentile | Movement generation and trip distribution for freight demand modelling applied to city logistics[END_REF] developed the CityGoods model in Italy. Since this work's main objective is to provide a review of simulation techniques applied to urban logistics, we will not focus in these models, but rather in their possible application to simulation. Readers interested in models should refer to (Gonzalez-Feliu and[START_REF] Gonzalez-Feliu | A simulation framework for evaluating the impacts of urban goods transport in terms of road occupancy[END_REF][START_REF] Comi | Urban Freight Models[END_REF].

RESEARCH METHOD

The following steps allowed the query and selection of the articles in order to identify the different simulation models and tools applied to urban logistics. First: we identified the noteworthy keywords related to the urban logistics field. These keywords were generated by combining the "initial keys" (A) and the "specific keys" (B) shown in Table 1. Querying with these criteria in several databases (Google scholar, Elsevier, Science Direct, Emerald, IEEE, Scopus, AMC and Web of Science) furnished a significant number of studies that were included in an initial meta-analysis. Second: in order to extract studies and articles dealing with simulation we applied a search filter using the boolean function "AND" with the keyword "simulation". This search resulted in a collection of 40 publications that were classified by year of publication, output criteria (measured or evaluated by the simulation), type of stakeholder and simulation technique, as shown in Table 2. Note that several of the reviewed works combine different simulation techniques; thus the total of the right column does not add up to 100%. Our literature review shows that most of the simulation works have a local authority standpoint (75%), some involve private actors, such as carriers (50%) and Shippers / receivers (35%) and few of them involve residents (10%). Also, it revealed that five main simulation techniques are used to address urban logistics problems. As shown in Table 3, the main simulation techniques used in urban logistics are:

Instance generation (IGS): consists in carrying out tests on configurations of a problem (possibly non-exhaustive) in order to assess the performances of a system, its inherent algorithms and/or the impacts of certain parameters. Monte Carlo simulation (MCS): makes use of random sampling and statistical analysis to compute results. MCS generates an important number of random configurations of a system sampling from the probability distributions of the events that can occur [START_REF] Vose | Quantitative risk analysis: a guide to Monte Carlo simulation modelling[END_REF], and furthermore performs statistical analysis in order to obtain significant conclusions.

Discrete event simulation (DES): is characterized by discrete-state models (as opposed to continuous-state models) in which systems evolve in a discrete space where time is driven by events. Agent based simulation (ABS): allows the representation of actions and interactions of autonomous entities that following their own rules and interests [START_REF] Dieussaert | SUSTAPARK: an agent-based model for simulating parking search[END_REF]. ABS is well adapted to simulate complex systems where several agents are involved and in which establishing general rules for their interactions is difficult. Time management in this kind of simulation is also discrete: time-driven or event-driven. System dynamics (SD): focuses on flows around networks rather than on the individual behaviour of entities [START_REF] Maidstone | Discrete Event Simulation, System Dynamics and Agent Based Simulation: Discussion and Comparison[END_REF]. In SD the real-world processes are represented in terms of three main objects; stocks, flows and delays [START_REF] Borshchev | From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques[END_REF].

It is to note that these approaches are not mutually excusive, for example, Monte Carlo simulation could be considered as a form of discrete-event simulation. Moreover, these techniques are often combined, as it will be exposed in the next sections.

ANALYTICAL FRAMEWORK Simulation techniques used in different types of problems

Our literature review on papers dealing with simulation in urban logistics highlighted 40 simulated problems that can be classified in 5 categories: (1) Transportation, Network and Infrastructure (TNIP): 52.5% of the publications (2) Vehicle routing (VRP): 17.5% (3) Urban Consolidation & Mutualization (UCMP): 22.5% (4) Intermodality (IP): 15.0% and ( 5) Electromobility (EP): 7.5%.

Several of the reviewed works deal with different problems; as a result, the total of the latter percentages does not add up to 100%. Figure 2 shows, for each type of problem, the frequency of utilisation of each simulation technique. In this section, these different problems are examined and an analysis based on the arguments found in the reviewed papers and on the answers given by the corresponding authors after an email survey is proposed.

Transportation, network and infrastructure problems (TNIP)

TNIP correspond to the wider category amongst the reviewed problems. They include planning of freight movements and operations in urban environments, traffic management and parking and facilities location. In this category, we counted 21 publications in which ABS is the most used simulation technique followed by DES (see Figure 2, top left). 2016) denoted that ABS is a more natural and flexible technique to model complex systems rather than SD and DES. However, some characteristics of ABS such as their micro-level of operation, the need for explicit interactions and the path dependency of results can make them difficult to apply and to validate. 2012) justified the use of ABS as it allows the 4 agents (shipper, transport service provider, carrier and driver) to act as independent decision-makers or behave as single entities. Moreover, the ABS can focus on the behaviours of the agents as well as implement notions of market coordination, learning capabilities and limited perceptions. 2010) addressed both TNIP and VRP. They presented a methodology for evaluating city logistics measures considering the behaviour of several stakeholders. They proposed a framework based on two sub models: a learning model for stakeholders (using Q-Learning and MCS) and a vehicle routing model. The use of ABS was justified as they considered stakeholders to be independent agents that have their own objectives and behaviours. MCS was used to represent changes in the stakeholder's behaviours after perceiving changes in their environment (e.g. new city logistics measures). A hybrid solution procedure based on ant colony optimization (ACO) and DES (using ARENA®) was proposed. DES allowed to evaluate the impacts of vehicle routes on the expected total costs. Computational experiments were carried out using a total of 40 random-generated instances. SD (exclusive) has been implemented in 2 publications: Qiu et al. ( 2015) proposed a SD model using Vensim® to simulate the dynamics of the logistics demand in the city of Beijing. They used of SD in order to integrate macroeconomic factors (unemployment rate, GDP, population size, productivity, etc.) and operational factors (order production, backlog order, volume of the vehicles, etc.). 2005) used SD to analyse the Guangzhou logistics system. The simulation was run using Vensim®. Gui et al. mentioned that SD is more intuitive and understandable than other techniques, as it relies on a consequence chart describing the influence and feedback mechanisms amongst variables.

Schroeder et al. (

Gui et al. (

A combination of DES and SD has been implemented in 1 publication: Delaître (2009) developed a hybrid model in order to help local authorities in the sizing and allocation of delivery areas by taking into account the impacts on the global traffic flows. The proposed tool is composed by two modules: a simulation of delivery areas at local level based on queuing systems (DES), and a simulation of the overall traffic spread (SD). MCS (exclusive) has been implemented in 1 publication: Pinto et al. ( 2016) addressed the problem of location and sizing of parking lay-by areas. In their approach MSC was used to sample arrival intervals and service times of trucks using the lay-by areas.

IGS (exclusive) has been implemented in 3 publications: Amaral et al. ( 2015) considered a two-tiered system and proposed a two-layered optimization model for the urban freight transport, taking into account delivery timewidows for the coordination of flows. An instance generator has been developed to generate and manipulate different instances of the problem. The choice of IG is supported by the simplicity of this technique when dealing with a large number of parameters. In their case, the travel times and costs are time and vehicle-dependent; as a result, a single instance of the problem will have 1920 independent parameters. Roca-Riu et al. ( 2015) presented the Parking Slot Assignment Problem (PAP), consisting in finding assignments of carriers to parking places that satisfy their time windows requests. In this paper, the authors evaluated and compared experimentally the proposed models by solving a set of test instances using CPLEX. 60 instances were generated in an experimental study in the city of Barcelona (Spain). 2012) proposed a framework to simulate the impacts that ecommerce flows have on urban road occupancy. The authors instantiated a set of scenarios that consider different population densities and household supply strategies.

Gonzalez-Feliu et al. (

Vehicle routing problems (VRP)

According to our literature review, problems related to vehicle routing are the second most important application of simulation to urban logistics mainly solved using IGS and MCS as shown in Figure 2 A combination of DES and IGS has been implemented in 1 publication: Herazo-Padilla et al. (2013) (as presented before) addressed the location-routing problem (SLRP) with DES and IGS. In their approach, Ant Colony Optimization metaheuristics were used to solve the VRP. Computational experiments were carried out using a total of 40 random-generated instances

Urban consolidation & mutualisation problems (UCMP)

This category includes publications about urban consolation, pooling and mutualisation of resources and/or infrastructure. These problems relate specially to urban consolidation centres UDC and urban logistic spaces ULS. Our review counted 9 publications.

ABS has been implemented in 4 publications: 2013) addressed both UCMP and EP (electro mobility). They evaluated the introduction of electric vehicles in urban distribution centres. Input data from CityDepot in Hasselt was used to model this system with Arena®. The use of DES is justified on the grounds of their capacity for detecting layout problems and bottlenecks in the system.

A combination of MCS and IGS techniques has been implemented in 1 publication: Muñoz-Villamizar and Angel (2013) (cf. previous description). [START_REF] Gattuso | A freight urban distribution center design with micro-simulation support for city logistics[END_REF] proposed a methodology to analyse the problems related to the functional organization of a UDC through a micro-simulation approach using WITNESS. The proposed model is a decision support tool for operators of existing UDC, since it enables them to define the layout, to dimension the internal spaces of the UDC and to evaluate its efficiency.

Intermodality problems (IP)

Intermodal Transportation Systems are logistics networks integrating different transportation services, designed to move goods from origin to destination in a timely manner and using multiple modes of transportation [START_REF] Dotoli | The impact of ICT on intermodal transportation systems: A modelling approach by Petri nets[END_REF]. This category was discussed in 6 publications. DES (exclusive) has been implemented in 5 publications: Fatnassi and Chaouachi (2016) used DES technique with Anylogic® to evaluate loading/unloading operations in the context of intermodal transportation and resources pooling, they considered the case of train and freight-rapid-transit (FRT). Fanti et al. ( 2015) developed a decision support tool to manage the flow of goods and the business transactions in logistics networks composed by a port and a dry port. DES was presented as well adapted for representing Intermodal Transportation Systems (ITS). They explained that ITS are logistics networks systems whose dynamics depend on the interaction of discrete events, such as demands, departures and arrivals. Cortés et al., (2007) used DES (Arena®) to analyse the freight traffic in the Seville inland port. The approach integrated all the types of cargo (e.g. containers, cereals, cements, etc). Authors justified the use of DES on the grounds of its easiness to provide operational indicators such as crane performances and berth occupations. Parola and Sciomachen, (2005) used a DES (Witness®) model to analyse the impact of new road and railway networks on the logistics system in the intermodal chain of the Italian ports. DES was chosen because of its capacity to represent synchronous processes between the handling resources and the arrivals and departures of vessels, trains and trucks.

Motraghi et al. (2012) (cf. previous description).

A combination of DES and ABS techniques has been implemented in 1 publication: Maione (2008) proposed a multi-agent system architecture for controlling operations in intermodal container terminal systems. The interactions between agents were modelled according to the DES formalism to describe the discrete and independent actions of agents.

Electromobility problems (EP)

The use of zero emission vehicles is one of the solutions proposed to deal with pollution (emissions), noise and other nuisances due to freight transportation in city centres. Three of the reviewed papers use different techniques (respectively IGS, DES and ABS) to deal with this topic:

Software solutions

The Figure 4 provides a synthesis of the software used in the reviewed works. It is important to highlight that various publications do not specify the software solution choices. Arena®, a flow-chart based DES environment (see [START_REF] Readiness | Survey and Comparison of Modelling Software[END_REF], is the most used tool in the reviewed publications. Surveys on simulation software (ORMS, 2015;[START_REF] Abu-Taieh | Computer Simulation Using Excel without Programming[END_REF] show that the usability of Arena® is an attractive feature (easy to use with available reference literature). Fanti el al. (2015) justified the use of the Arena on the grounds of its capacity for dealing with large-scale and modular systems. We performed a survey amongst the authors of the reviewed publications that use Arena® to understand why they choose this tool. The common answer is that they were Arena® users beforehand, although they consider Simul8, Simio, Witness and Anylogic to be software solutions of similar quality. This literature review points out several opportunities for simulation in urban logistics. One important gap relates to the lack of traffic micro-simulation, which could simulate the impacts of urban freight on traffic. This can be explained because of the complexity of traffic simulation and the lack of information on real freight transport.

Although e-commerce is growing fast and is a quite visible aspect of urban logistics, few simulations have been done in this field. A perspective of research could be simulation of how the infrastructures of cities can undertake the growth of logistic operations related to ecommerce. More precisely, simulation in this field could assess the pertinence of new distribution models as well as their environmental impacts.

A primary source of debate in urban logistics is the pertinence of regulations regarding vehicles (sizes, weights, load factors and/or engine types) and restrictions of access (restricted areas and/or time windows). These topics are capital for both practitioners and local authorities. Simulation would certainly help to better understand the impacts of these regulations on each actor of a city.

Many countries are testing alternative fuel vehicles including electric vehicles, hybrid vehicles, natural gas vehicles, and fuel cell vehicles; indeed urban logistics operations are directly affected by these trends. In this field, simulation could be useful to study the needs and investments (infrastructure and/or fleets) in order to ensure a flawless evolution towards greener energies.

In general, we notice the absence of statistical analysis in order to validate the reviewed simulations. Statistical examination of the inputs and the data would allow a better definition of the model's limits and a most relevant interpretation of its results.

CONCLUSION

In the light of the reviewed publications, different techniques for simulating urban logistics systems can be distinguished. Agent based simulation (ABS) and Discrete Event Simulation (DES) are the most broadly used simulation techniques.

ABS is useful to better understand real-world systems in which the representation or modelling of many individuals is important and for which the individuals have autonomous behaviours. As a result, ABS is gaining more and more interest in urban logistics as it allows

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the interaction between different urban entities (freight carriers, truck drivers, retailers and local authorities) and it allows evaluating and improving the objectives of different stakeholders. Nonetheless, agent based modelling can be harder to develop [START_REF] Lebeau | Implementing electric vehicles in urban distribution: A discrete event simulation[END_REF]. Several applications of ABS studying the performances of urban distribution centres can be highlighted in the literature. ABS is particularly useful when there is little knowledge about the global interdependencies between coexisting stakeholders in an urban zone. Our literature review shows that works aiming to evaluate the performances of urban distribution centres are usually tackled with ABS (see [START_REF] Lebeau | Implementing electric vehicles in urban distribution: A discrete event simulation[END_REF] or DES [START_REF] De Oliveira | Simulation of an Urban Logistic Space for the Distribution of Goods in Belo Horizonte, Brazil[END_REF]. For further understanding of the main advantages of ABS compared to other techniques, refer to [START_REF] Siebers | Discrete-event simulation is dead, long live agentbased simulation![END_REF].

The wide presence of DES could be explained by the large availability and experience of the operational research community with DES software. In urban logistics DES techniques are mainly used to simulate systems involving traffic and lead times, this can be related to the capability of DES to deal with queuing structures. [START_REF] Siebers | Discrete-event simulation is dead, long live agentbased simulation![END_REF] described DES as useful for problems containing networks of queues. It is interesting to note that all the reviewed papers dealing with intermodal transportation systems (ITS) make use of DES as simulation technique. ITS are logistics networks systems whose dynamics depends on the interaction of discrete events, such as demands, departures and arrivals of means of transportation at terminals and acquisitions and releases of resources by vehicles and thus can be successfully modelled as Discrete Event Systems.

Monte Carlo Simulation (MCS) has been applied to a diverse range of problems in urban logistics. This technique seems to be a good answer to problems with significant uncertainties, whenever there is need of estimations, forecasts and/or decisions. Most of the reviewed publications use MCS to simulate the variability of urban freight demand, behaviour of actors, and duration of operations. The main applications involve facility location decisions and scheduling. For further reading on the advantages of MSC refer to [START_REF] Raychaudhuri | Introduction to Monte Carlo simulation[END_REF].

Instance Generation Simulation (IGS) is mostly found in publications dealing with vehicle routing problems and network design. Instances usually represent distribution systems with multiple suppliers, capacitated warehouses, capacitated retailers, identical capacitated vehicles and unit-sized items. Such items are to be transported from the suppliers to the warehouses, and subsequently delivered to the retailers by vehicles. The reviewed publications implementing IGS intend to model only important scenarios of a system and in most cases pay particular attention to travel times.

It is interesting to highlight the almost absence of System Dynamics Simulation (SD), which focuses more on the behaviour of the system rather than the individuals composing it. In SD, the real-world processes are represented in terms of stocks and flows. According to [START_REF] Maidstone | Discrete Event Simulation, System Dynamics and Agent Based Simulation: Discussion and Comparison[END_REF], DES tends to look at the smaller detail of a system (microscopic), whereas SD tends to take a more overall perspective (macroscopic). As described in [START_REF] Rabelo | Enterprise simulation: a hybrid system approach[END_REF], SD is suitable for high level strategic modelling, because (1) it proposes a holistic approach of systems, integrating many subsystems (2) it focuses on policies and system structure (3) it make use of feedback loops to represent the effects of policy decisions.

Finally, we draw the reader's attention to consider the use of hybrid simulation techniques. Indeed, combining simulation techniques could provide better representation of the reality and/or more efficient computing environment by capitalizing on advantages of both techniques. As noticed in works where ABS and DES are combined, authors take advantage of DES to represent complex queuing systems, and of ABS to model different stakeholders that act as independent decision-makers and/or behave as single entities.

Figure 1 .

 1 Figure 1. Distribution of reviewed works by type of publication

2014

  Simulate and analyse the road occupancy rates of urban goods movement in terms of running vehicles IGS (FRETURB model) effects of demographic and socioeconomic changes of end-consumer choices and the relocation of commercial and logistics centres on shopping activities. benefits of sharing parking spaces between passenger cars and freight vehicles associated to electric vehicles on the degree of congestion ABS

Figure 2 .

 2 Figure 2. Utilization of simulation techniques when solving the 4 types of problems

  Joubert et al. (2010) used an ABS technique to reconstruct commercial activity chains. These chains were simulated using the ABS toolkit MATSim for a large-scale scenario in Gauteng, South Africa. This work, listed three advantages of ABS: (1) they represent the heterogeneous nature of freight transport actors and objects; (2) optimization engines can be incorporated into the individual agents' behavioural models; and (3) micro-model results can be aggregated to support decisions in a more global context. Dieussaert et al. (2009) justified the use of ABS on the grounds that it has the advantage of allowing local interactions between the drivers and their environment for the problem of parking search and choice. Ossowski et al. (2005) described a design method for building agent-based Decision Support System (DSS) for real-world traffic management problems. They outlined the role of multi-agent techniques in the design and implementation of such tools for the problems of road traffic management. Van Heeswijk (2016) highlighted that ABS techniques are suitable to evaluate the effectiveness of urban logistics schemes (both governmental policies and company-driven initiatives), as they are capable of monitoring and altering the behaviour of autonomous agents under varying conditions. Nuzzolo et al. (2014) as well as Comi and Rosati (2015) presented DSS based on the ABS tool "CLASS", developed to support ex-ante assessments to simulate goods movements and capture the effects due to urban freight transport measures on end-behaviours. The results of its application to Padua a medium-size town in northern Italy are reported. Hunt and Stefan (2007) developed an agent-based micro simulation framework using a tour-based approach of urban commercial transport movements in the region of Calgary. A combination of ABS and MCS has been implemented in 2 publications: Tamagawa et al. (

  Wisetjindawat et al. (2007) developed a micro-simulation model for Tokyo Metropolitan area freight. They proposed a hybrid technique (ABS and MCS) in which each freight agent individually determines the characteristics of a set of freight movements, which proved to be representative of a real urban area. The MCS was used to generate virtual firms and their attributes (production, consumption, distribution, etc.). DES (exclusive) has been implemented in 1 publication: Motraghi et al. (2012) dealt with both TNIP and IP. They examined the utilisation of existing urban rail networks for freight distribution in cities. A DES model was developed to study a possible implementation in the Newcastle Metro. The choice of DES was justified on the grounds of simplicity when representing complex stochastic systems. A combination of DES and MCS has been implemented in 1 publication: Nourinejad et al. (2014) developed a traffic simulation module in the "Paramics traffic simulation®", a microscopic time-stepping DES. This module incorporated a parking choice model in which the implementation of DES is essential for the representation of queues and transitions. In addition to DES, they made use of MCS in order to integrate the parking choice model and to measure the dwell time for each vehicle. A combination of DES and IGS has been implemented in 1 publication: Herazo-Padilla et al. (2013) dealt with TNIP and VRP. They considered the stochastic version of the location-routing problem (SLRP) in which transportation costs and vehicle travel speeds are both stochastic.

  (top right). This category gathered 7 publications. A combination of IGS and MCS has been implemented in 3 publications: Tadei et al. (2014) addressed the multi-path TSP with stochastic travel costs. In order to evaluate the stochastic objective function of their problem, they used a MCS implemented in C++. Two instances have been generated and solved using the Concorde TSP solver. Maggioni et al. (2014) highlighted that the instances presented by Tadei et al. (2014) do not fully reflect real cases although costs were generated according to realistic rules. In order to fulfil this gap, they have generated new instances based on the real traffic sensor network. Muñoz-Villamizar and Angel (2013) addressed the problem of locating distribution centers in urban areas and the corresponding freight vehicle routing. Their algorithm makes use of MCS when dealing with determining the location of depots and assigning each customer to the corresponding depot. IGS was applied to solve the different VRP instances. IGS (exclusive) has been implemented in 1 publication: Qureshi et al. (2010) developed a column generation based exact optimization algorithm for the Vehicle Routing and scheduling Problem (VRSP) with Semi Soft Time Windows. The algorithms were implemented in MATLAB. Computational experiments were carried out using eight test instances based on the Solomon's R101 benchmark. A combination of MCS and ABS has been implemented in 1 publication: Tamagawa et al. (2010) (as presented before) combined ABS and MCS. Their VRP-TW-F model plans and implements delivery schedules of trucks for each freight carrier (agent). DES (exclusive) has been implemented in 1 publication: Barceló et al. (2007) proposed a decision tool for the VRP in city logistics and its application to the delivery of goods in two Italian cities. The simulator used was AIMSUN® NG, TSS 2005, a microscopic DES program. The detail level of DES allowed the approach of specific problems such as VRP with Time Windows (VRPTW) and a Pickup and Delivery Problem with Time Windows (PDPTW).

  Baykasoglu et al. (2011) proposed a novel multi-agent based general load consolidation system for solving dynamic truckload consolidation problems. The proposed system was implemented within JACK™ an agent-oriented environment integrated with Java. Van Duin et al. (2012) used ABS to evaluate the performances of an UDC. The use of ABS was justified as it allows an explicit modelling of individual stakeholder behaviour and actions, which has impact on the overall outcome of the model. Wangapisit et al. (2014) used ABS to study the effects of city logistics measures such as urban distribution centres (UDC) and parking space restrictions. The ABS technique was used to represent the multi-objective environment of the different stakeholders and to finetune the parameters of UDC. Boussier et al. (2009) designed a software tool for the management of goods distribution in small and medium size cities using electric vehicles. They focused on the management process of parking spaces. ABS was used to simulate impacts and benefits of sharing parking spaces between passenger cars and freight vehicles. DES (exclusive) has been implemented in 5 publications: De Oliveira et al. (2014) proposed a simulation of an Urban Logistic Space (ULS) in central area of Belo Horizonte using Arena®. They generated indicators to evaluate the performance of this space according to different operating configurations. Makhloufi et al. (2015) developed a discrete event simulator (MODUMS) in order to evaluate the pertinence of UDCs located in the outskirts of the city and connected by a ring in a medium-size European city. Lebeau et al. (

Figure 4 .

 4 Figure 4. Software solutions used in the reviewed articles

Table 1 .

 1 Areas of research and resulting simulations techniques

	INITIAL KEY (A)	SPECIFIC KEYS (B)
	Urban	Logistics	Freight movements	Demand
	City	Distribution	Freight transport	Traffic
	Last mile	Delivery	Freight demand	Parking
		Planning	Transportation Routing	Commercial movements
		Goods distribution	Delivery spaces/areas/bays	Decision support
		Goods movements	Loading/unloading bays

Table 2

 2 Distribution of the reviewed papers

	YEAR		OUTPUT	STAKEHOLDER	TECHNIQUE
	2016 12.5%	2010 7.5%	Economic 92.5%	Local authorities 75.0%	Discrete event 35.0%
	2015 15.0%	2009 7.5%	Environment. 52.5%	Carrier 50.0%	Agent-based 40.0%
	2014 15.0%	2008 2.5%	Social 17.5 %	Shipper/Receiver 35.0%	Monte Carlo 20.0%
	2013 10.0%	2007 10.0%		Residents 10.0%	Instance generation 22.5%
	2012 10.0%	2005 7.5%			System dynamics 7.5%
	2011 2.5%

Table 3 .

 3 Summary of simulation works in the field of urban logistics

	YEAR	2016	2015		2015		2015	2014		2014		2013		2013		2012		2009		2008	2007		2007	2005	2016	2015	2015	2014
	AUTHOR	Fatnassi and	chaouachi	Fanti et al.		Makhloufi et al.		Gattuso et al.	De Oliveira et al.		Nourinejad et al.		Lebeau et al.,	Herazo-Padilla et	al.	Motraghi et al.		Delaître		Maione	Cortés et al.		Barceló et al.	Parola and	Sciomachn	Teoh et al.	Amaral and	Aghezzaf	Roca-Riu et al.	Tadei et al.	Maggioni et al.
	OUTPUT CRITERIA	Economic (waiting time)	Economic (lead times)	Economic (filling rates and	distances) and Environmental	(CO2 emissions)	Economic (time, cost)	Economic (costs, waiting times	and profits)	Economic (travel times) and	Environmental (CO2 emissions)	Economic (traffic)	Economic (times and costs) and	Environmental (CO2 emissions)	Economic (times and costs) and	Environmental (CO2 emissions)	Environmental (CO2 emissions)	and Economic (times)	Economic (costs, waiting times	and profits)	Economic (times)	Economic (costs and times),	Environmental (CO2 emissions)	and Social	Economic (costs)	Environmental (CO2 emissions)	Economic (costs, distances and	times)	Economic (times)	Economic (travel times and	distances) and Environmental	(CO2 emissions)	Economic (costs and travel	times) and Environmental	(emissions)
	CONTEXT	Tunisia	SAIL and CO-GISTIC (Italy)		MODUMS Project (France)		Reggio Calabria (Italy)	City of Belo Horizonte (Brazil)	City of Toronto Canadian Freight	Transportation Research Program	(Canada)	MOBI Research Group and CityDepot	Brussels-Hasselt (Belgium)	Colombian Department of Science,	University of Leeds, Project	"DISRUPT" (Colombia & UK)	City of Newcastle upon Tyne	(England)		La Rochelle (France)		Taranto container (Italy)	Transport Research Programme -The	Port of Seville (Spain)		Cities of Lucca and Piacenza (Italy)	Italian ports of the Ligurian sea (Italy)	Singapore National Research	Foundation (Singapore)	CAPES Foundation (Brazil)	The Spanish Ministry of Economy and	Competitiveness (Spain)	PIE VERDE project UrbeLOG project	(Italy)	PIE VERDE project UrbeLOG project	(Italy)
	STAKEHOLDER	Carriers	Carriers		Local authorities		Local authorities	Local authorities	Carriers and Local	authorities	Shippers / receivers		Carriers		Carriers and Local	authorities		Local authorities		Shippers / receivers	Local authorities	Carriers and Local	authorities	Local authorities	Carriers and Local	authorities	Local authorities	Carriers and Local	authorities	Local authorities	Local authorities
	TECHNIQUE	DES (Anylogic)	DES (ARENA)		DES (N/A)		DES (WITNESS)	DES (ARENA)	DES + MCS	(PARAMICS)	DES (ARENA)		DES + IGS (ARENA)		DES (ARENA)		DES + SD (DALSIM)		DES + ABS (MULTI-	AGENT CONTROL)	DES (ARENA)		DES (AIMSUN)	DES (WITNESS)	IGS (FASTSIM &	XCARGO -EXCEL	BASED)	IGS (MATLAB)	IGS (CPLEX 12.1)	IGS + MCS (CONCORDE	TSP SOLVER & C++)	IGS (CONCORDE TSP	SOLVER) & MCS (C++)
	PROBLEM	Simulate the loading/ unloading operation in an intermodal	transportation tool	Planning of flow of goods and business transactions	between a port and a dry port.		Organization of urban distribution based on consolidation		Simulate the operational activities and evaluate the	efficiency of a UDC	Analyse the impacts of an Urban Logistics Space (ULS)	operation	Evaluate the impact of dedicating on-street parking in a	busy street system in the Toronto CBD.	Evaluate the impact of replacing trucks with electric	vehicles in a urban distribution centre	Stochastic location-routing problem. Evaluate the impact of	vehicle routes on the expected total costs of location and	transport.	Analyse the merits of transporting urban freight by rail	The simulation of scenarios of deliveries areas distribution	inside a medium sized city and estimate their impacts on	global flows	Controlling operations in intermodal container terminal	The simulation of the freight traffic in the port of Seville	Transport planning, microscopic traffic simulation, demand	and traffic data analysis.	Analyse the impact of new road and railway networks on	the Intermodal Transportation Systems	Evaluate the suitability of electro mobility in urban	logistics operations	Network design and routing	The Parking Slot Assignment Problem (PAP)	The multi-path TSP with stochastic travel costs	Urban routing problems (multi-path Traveling Salesman	Problem)

Teoh et al. (2016) developed

  a methodology to evaluate the operational suitability of electro mobility concepts for last-mile delivery. IGS was used to validate the methodology through two key outputs: accuracy of the energy estimation (FASTSim -Excel-based) and comparing scheduled and routing performance (computed with XCargo logistics software) in regards to real data. It is important to mention that authors pointed out that this work could be done using an agent-based simulation to identify correctly the roads or lanes to be electrified.

	Lebeau et al. (2013) (cf. previous description).
	Boussier et al. (2009) (cf. previous description).

ACKNOWLEDGMENTS

This work is supported by ADEME (Agency for the Environment and Energy Management), La Poste Group, Mairie de Paris, Pomona Group and RENAULT through the Urban Logistics Chair at MINES ParisTech. The authors would like to thank Pr. A. Comi, Pr. P. Cortés, Pr. J. W. Joubert, Dr. M. Marinov, Dr. L. K. de Oliveira, Dr. G. Iacobellis, Pr. J. Gonzalez-Feliu, Pr. G. Gentile and Dr. L. Delaître for their constructive feedback.