Como ya comenté, la semana pasada estuve por Frankfurt en la European Transport Conference 2015. La verdad es que la experiencia estuvo bastante bien y no sólo expuse una ponencia sino que además trabajé uno de los días en la organización. En los 4 días que pasé en Frankfurt visité toda la ciudad y ya daré buena cuenta en un artículo específico sobre el transporte en esta ciudad. Mientras tanto, os dejo con la que fue mi ponencia en el congreso:
Y os dejo mi “discurso” diapositiva a diapositiva:
Good afternoon everyone. First of all, I want to thank the audience for attending this session in which I will present this research, based on my Doctoral Dissertation. I also want to apologize in advance for any errors that I may commit for two reasons: the nerves caused by the presentation; and the use of a language that isn’t my mother tongue.
So, let’s look at the question of “HOW THE SUSTAINABILITY OF THE LOCATION OF DRY PORTS SHOULD BE MEASURED”.
I’m Samir Awad Núñez and I come from Madrid. I have a MSc in Civil Engineering and I’m currently finishing my doctoral research. I hope to obtain my doctorate in late 2015 or early 2016.
There are three other members of the research team: my Thesis Directors Dr. Nicoletta González Cancelas and Dr. Alberto Camarero Orive and my collaborator in the development of the mathematical aspects of the research, Dr. Francisco Soler Flores.
The global economic structure has two main properties. First: centers of production and consumption are decentralized. Second: companies tend to minimize their stock. This has led to a change from push manufacturing to pull manufacturing. In addition, consumption patterns have encouraged a very short production cycle, leading to “just in time” production.
The consequent increase in freight traffic all over the world creates considerable problems and challenges for the freight transport sector. This situation has led shipping to become the most suitable and cheapest way to transport goods all around the world. And ports are now configured as nodes with critical importance in the logistics supply chain as a link between two transport systems, sea and land.
Increase in activity at seaports is producing three undesirable effects. First: increasing congestion in port operations and road routes serving terminals. Second: lack of open space in port areas. And, finally, a significant environmental impact on the coast. These adverse effects can be mitigated by moving part of the activity inland.
Implementation of dry ports is a possible solution to these problems and would also provide an opportunity to strengthen intermodal solutions as part of an integrated and more sustainable transport chain, acting as a link between road and railway networks. In this sense, implementation of dry ports allows the separation of the links of the transport chain, thus facilitating the shortest possible routes for the lowest capacity and most polluting means of transport.
Furthermore they have some advantages compared with other types of logistics terminals: 1) dry ports are connected with seaports by railway. And there is widespread consensus that railroad is the land transport mode with lowest external costs and least harmful effect on the environment, so they promote a more sustainable supply chain; 2) Because they allow customs clearance and other complementary activities outside seaports, they speed up the transit of goods through seaports and reduce the pressure on this link in the supply chain; 3) They extend the hinterland of the ports inland.
However, the decision of where to locate a dry port must also ensure the sustainability of the site. On this point, we must ask some questions: 1) What set of variables should we use? 2) What relationships exist between these variables? 3) How do we measure the sustainability of the location of dry ports?
Thus, the main objective of this work is to understand the variables influencing the sustainability of dry port location and how this sustainability can be evaluated.
In order to achieve this objective, we identified the following tasks as being necessary:
1)Identify the set of variables.
2)Build a database of relevant geographic information.
3)Identify the existing relationships between the variables.
4) Establish a methodology to measure and compare the sustainability of each location.
This is the complete methodology diagram. But rather than comment on the detail now, we will look at each step in turn during the rest of the presentation.
The first task is to set the framework, and consists of two parts: first, identifying the set of factors influencing the quality of the location of dry ports and variables on which they depend; and, second, collecting geographic information. For each variable we obtained a value for each dry port, called the ”Measured Criteria Assessment Score”.
In this first task, we conducted a literature review in the following areas: logistics and dry ports, methodologies employed in land use planning and the main theories of industrial location.
I don’t want to spend time on this point but I can answer your questions if you would like to know anything about this literature review.
As a result of this literature review, we established a set of 41 variables. These in turn are grouped into 17 factors. And the factors are organized into 4 categories: environmental factors, economic and social factors, accessibility factors, and location factors.
There are variables affecting the location but which are excluded from the analysis, for one of the following reasons:
– They are completely subjective and unmeasurable, such as political arrangements.
– They fall outside the focus of the evaluation we did. These are variables that affect the management of the terminal, such as the availability of technology, operating costs or the regulatory environment.
– There is no data available.
There are three environmental factors: Impact on the natural environment, Impact on the urban environment, and Hydrology. And, as I said before, all factors depend on several variables.
Economic and social factors are: Land price, Potential growth in demand and Hosting municipality range. This last term measures the balance between the benefit of proximity to a large population compared with the cost in terms of disruption to a part of that population.
Accessibility factors are those which are related to the ease of access to different infrastructures: rail network, high capacity roads network, airports, seaports and supplies and services.
Finally, Location factors are those that are related to the geography and connectivity of a place.
That is to say: Weather, Orography, Geology, Relation with other logistics platforms, Integration into the main supply chain infrastructures and Potential optimization of modal shift. The last three factors measure how influential a place can be in attracting freight traffic.
In this stage, geographic information of each variable is gathered and entered in the Geographic Information System. We studied the 10 logistics terminals that meet the following criteria:
– Having direct connection by rail.
– Allowing customs clearance.
– Being inland.
As I said before, for each variable we obtained a value for each dry port. These ”Measured Criteria Assessment Score” were registered in a database to feed the Geographic Information System.
The second task is to build the Bayesian Network model from the Measured Criteria Assessment Score.
Bayesian Networks are classified into artificial intelligence techniques. They are graphical representation of dependencies for probabilistic reasoning. For this reason, they allow to classify datasets and to establish relationships between data elements.
According to the type of structure of the data, different structure-learning methods can be applied. To build the Bayesian Network we chose a K2 structure-learning algorithm, because it allows the variables to be stratified.
In this kind of algorithm, all structures are equally likely at the start. The K2 algorithm begins by assuming that a node has no parents and at each step incrementally adds that node’s parent whose inclusion increases the gradient. For each node, the algorithm searches for the K2 parents that maximize the gradient.
Also it has an important advantage because it has a very low computing costs.
The result of applying the algorithm is this network in which the variable Distance to Natural Spaces is the root node of the network because no path enters it.
The third task consists of establishing weightings for the variables using a novel approach to rank within the Bayesian Network and for factors using a DELPHI questionnaire.
What do I mean when talking about a new way of ranking? By assessing the importance of each variable by depth compared with the root of the network, a certain weighting is set for each variable. Depth is measured by the number of links to reach to the network root. DNS is placed in Layer 1, the variables that need one link to reach DNS are placed in layer 2, and so on.
By using this depth, greater importance is for environmental variables, so the sustainability of locations requires a great respect for the natural environment and the urban environment in which they are located.
Weightings of the factors are established by applying the DELPHI methodology. These weightings are the result of an earlier research and can be found at Procedia Social and Behavioral Sciences, in paper: Application of a model based on the use of DELPHI methodology and Multicriteria Decision Analysis for the assessment of the quality of the Spanish dry ports location.
The fourth task is to obtain the Standarised Criteria Assessment Scores from the measured geographic information.
For this standardization, we convert all of the variables to benefit variables and then we use a spline interpolation. Depending on the grade of the spline, there were 3 different kinds of boundary conditions. The kind of interpolation is selected by minimizing the difference between the Measured Criteria Assessment Score and the Standardised Criteria Assessment Score.
Thus, we obtained a Standardized Criteria Assessment Score matrix.
Using the data I’ve just described, the last task is the application of a linear weighted multicriteria decision analysis where LQR (Location Quality Rate) is the ratio of the quality of each location; EP (Environmental Protection) is a dichotomous function, which serves to exclude protected areas (worth 0 for protected locations and 1 for locations without environmental protection); SCAS (Standardized Criteria Assessment Score) is the score of the evaluation criteria for each variable and location. Finally, w (little w) and W (big w) are respectively the weightings of each variable from the Bayesian Network and that obtained from the DELPHI questionnaire to fix the importance of each factor. The locations with a higher value will be most appropriate for solving the problem.
It works in the same way as the McHarg graphic overlay method.
These are the results.
The most sustainable dry port is Monforte de Lemos, which scored well in terms of social and environmental factors and was balanced in the economic section, with 60.3% of the maximum possible score. Meanwhile, the least sustainable locations are Coslada, Abroñigal and Santander-Ebro, all of them with low social and environmental scores that are not compensated by the economic section.
For its part, Coslada has the best quality location if all the variables are taken into account, with 57.2%. These modest results show that both sustainability and quality of dry port locations in Spain are moderate. This can also be seen in the median values, of 41.3% and 48.8% respectively.
Conclusions are the following.
Determination of the most appropriate location to place dry ports is a geographic and multidisciplinary problem, with environmental, economic, social, accessibility and location repercussions.
Environmental variables prove to be the most important in deciding the location, so the sustainability of locations requires a great respect for the natural environment and the urban environment in which they are located.
Although four variables are unrelated to the rest of the network (Connectivity with the natural environment, Density of facility area, Quality of the railway and Existence of supplies and services), we must not lose sight of these variables in future evaluations since the lack of relationship is (only) due to the inability to establish preferential relationships between them because their values are practically the same for all locations of dry ports (but) in Spain.
The triangulation of different independent techniques provides greater confidence in the results, because the use of Bayesian Network and DELPHI methodology reduces the arbitrariness of the weightings of the Multicriteria Decision Analysis algorithm.
Well, that’s all. I hope the presentation of our research has been interesting for you, and I am now available to answer any questions you may have.
El debate posterior fue muy pero que muy interesante a pesar de mi dificultad para expresar en inglés conceptos matemáticos complejos acerca de cómo está configurada la red (los problemas del directo). Aunque mal del todo no debí hacerlo porque acabé nominado al premio a la mejor ponencia de la categoría “Planning for Sustainable Land Use and Transport”. Y cabe destacar que me preguntaron por qué no había tenido en cuenta el transporte fluvial. La mujer que me preguntó no debe haber visitado mucho España…