Rabie Fadil

Badr Abou

El Majd

Hicham Rahil

Hassan El Ghazi

Naima El Kaabouch

Hassan El Ghazi

Multi-objective optimization approach for air traffic flow management

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The Operational Research community has studied many variants of the air traffic flow management problem since the beginning of the 90s. Along the years, the models have been refined in order to take into account new operational constraints.

One of the first formulations of air traffic flow management is the ground holding problem, which minimizes the sum of airborne and ground delay costs when the demand for the runways exceeds the allowed capacities. The first work under this formulation has been the Single Airport Ground Holding Problem [START_REF] Odoni | The Management Problem in Air Traffic Control[END_REF]. After that, [START_REF] Richetta | Dynamic Solution to the Ground-Holding Problem in Air Traffic Control[END_REF] has proposed a stochastic and dynamic version of the formulation.

Then, the Multi-Airport Ground Holding Problem was addressed by [START_REF] Vranas | The Multi-Airport Ground-Holding Problem in Air Traffic Control[END_REF]. This does not take into account the sector capacities,rerouting and speed changes.

The first two limitations were overcome with the model of [START_REF] Bertsimas | The Air Traffic Flow Management Problem with Enroute Capacities[END_REF]. Also, this work has the merit to use realistic instances with several thousand flights. To our knowledge, the most comprehensive formulation is the Air Traffic Flow Management Rerouting Problem [START_REF] Bertsimas | An Integer Optimization Approach to Large-Scale Air Traffic Flow Management[END_REF] which integrates all phases of a flight, different costs for ground and air delays, rerouting, continued flights and cancellations. Also, with the same mathematical framework, [START_REF] Agustin | On Air Traffic Flow Management with Rerouting. Part I: Deterministic Case[END_REF], [START_REF] Agustin | On Air Traffic Flow Management with Rerouting. Part II: Stochastic Case[END_REF] have formulated the problem in terms of routes instead of nodes.In the same manner, there is also the work of [START_REF] Clare | Air Traffic Flow Management under Uncertainty: Application of Chance Constraints[END_REF] which describes an optimization problem to minimize directly the probability of congestion of the sectors with the concept of chance constraint.

Besides, other techniques were used to solve similar problems. [START_REF] Oussedik | Air Traffic Management by Stochastic Optimization[END_REF] uses stochastic optimization methods for handling sector congestion with take-off delays and rerouting. Constraint programming was also used by [START_REF] Barnier | Slot Allocation with Constraint Programming: Models and Results[END_REF] and [START_REF] Flener | Air-Traffic Complexity Resolution in Multi-Sector Planning using Constraint Programming[END_REF]. The former solves the slot allocation problem with sector capacity constraints and the former minimizes an air traffic complexity metric for multiple sectors.

More recently, a multi-objective optimization approach has been used in air traffic control by [START_REF] Flener | Air-Traffic Complexity Resolution in Multi-Sector Planning using Constraint Programming[END_REF] to minimize an aggregated complexity metric, designed and validated by Eurocontrol1 , over sectors. Also, [START_REF] Delahaye | Airspace Congestion Smoothing by Multi-Objective Genetic Algorithm[END_REF] uses the multi-objective to model the trade-off between sector congestion and delays.

THE MATHEMATICAL MODEL

In this section, we briefly present the mathematical model wich is based on [START_REF] Marceau Caron | Multiobjective tactical planning under uncertainty for air traffic flow and capacity management[END_REF] and [START_REF] Hadjaz | Increasing Air Trafc: What is the Problem ?[END_REF]. It consists of two submodels, the flight model and the sector model. The former is used to compute the expected cost of delay and the probability of presence in the sectors. The latter takes the probability of presence for each flight and compute the expected cost of congestion. First, the flight model is defined as follows:

p N (t N ) = Ω ... Ω p 1:N (t 1:N )d t 1:N -1 = Ω ... Ω n i =2 p i |i -1 (t i |t i -1 ).p 1 (t 1 )d t 1 ...d t N -1 = Ω ... Ω p 3|2 (t 3 |t 2 ) Ω p 2|1 (t 2 |t 1 ).p 1 (t 1 )d t 1 p2(t2) d t 2 p3(t3) ...d t N -1
Where Ω is the time line, p 1 (t 1 ) is the marginal density function for the flight f to enter the airspace at time t 1 and p i +1|i (t i +1 |t i ) is the conditional density function to be on X i +1 at time t i +1 given it was on X i at time t i . Then the probability to be in sector during the time interval ∆t = [t mi n , t max ] is :

P S ∆t s, f = F i (t max ) -F j (t mi n ) (1)
Where F i (t ) is the cumulative density function denoting the probability that the flight f has flown over X i by time t .

Thereafter, the probability to have n flights in the sector s during time ∆t can be expressed by :

P K ∆t s = n = 1 N ∆t s + 1 N ∆t s l =0 exp(-i wl n) . N ∆t s f =1 1 -P S ∆t s, f + P S ∆t s, f exp(i wl ) (2)
Where i = -1 and w = 2π

N ∆t s +1
. Now, we have all the elements to define our multiobjective optimization problem. Because of the stochastic context, one way to define the cost functions is to use their expected values. So the total expected cost of the delays is defined as follows:

C 1 (γ) = f ∈F E φ f T f n f ; γ | f = f ∈F Ω (t -A f ) 2 .p f n f (t ; γ | f )d t ( 3
)
Where F is the set of flights, A f is the scheduled time of arrival of flight f , n f the number of waypoints in the flight plan f and p n f refers to the marginal density function associated to the arrival point X f n f . γ | f denotes the decision vector, the target times of arrival on the different waypoints of the flight f . Indeed, in the optimization context, we are trying to find the vector γ that minimizes the cost functions. For the expected cost of congestion, the function is:

C 2 (γ) = Ω N ∆t s n=C s +1 (n -C s ) 2 .P (K ∆t s = n; γ)d t (4)
Where C s is the capacity of the sector s and N ∆t s is the maximum number of flights that can be inside the sector at the same time with probability higher than 0 and Ω a temporal horizon sufficiently large to encompass the supports of the marginal distributions.

EXPERIMENTS

In this section, we describe the experiment on minimizing both the expected cost of delays and the expected cost of congestion with a multiobjective optimization algorithm and a probabilistic model. Then the results on a simple instance are detailed.

The chosen variation operators are shown in the table below:

NSGA-II operators

Encoding: Real Population size : 1000 Maximal number of evaluations : 30000 Selection : Binary tournament Crossover : Simulated Binary Crossover , P c = 0.9 Mutation : Polynomial, P m = 1/Number O f V ar i abl es

Experimental Setting

The chosen instance for this study, implies 21 flights, including 15 departures and 6 arrivals in the Terminal Manoeuvring Area (TMA) surrounding Mohammed V International Airport.

All the results were computed on an Intel ® Core ™ i5-4200M CPU with 4× 2.5 GHz and 4Go of memory. The model was coded in Java, for NSGA-II and SMPSO we have used the implementation provided by the jMetal framework. We have also used the Apache Commons Mathematics Library for the probabilistic distributions and the computation of the integrals. This library is freely downloaded at : http://commons.apache.org/

Analysis

Now, we will analyze each step of the methodology. The first step consists in computing the marginal probabilities over the waypoints, as depicted on figure 1, We can see that the value of the modes decreases and the supports of the distributions increase with time. This simply translates the fact that uncertainty on the target time of arrival increases with time. The next step is the computation of the probability for a flight to be in a sector along the time. This is done with (1) and figure 2 shows the result.

The probability increases when the aircraft approaches the sector and once the flight is inside the sector, the probability should not decrease until the exit.

Thereafter, we need to compute the probability that there will be n flights inside the sector at a given time with (2) and we can remark in figure 3 that the probability of congestion decreases with time since the variance of the marginals increases with time. 4) respectively. We can note that minimizing the probability of congestion for every timestamp will effectively minimize the expected cost of congestion.

Finally, when the cost functions are known, one can optimize by given different objectives to the flights. Figure 4 shows the Pareto front for 12 different solutions. Every solution is a complete schedule in the decision space and so, the Pareto front shows the trade-off between minimizing both the delays and the congestion. If the decision maker wants to minimize the chance that delaying the flights congests the sector, he shall select a solution toward the lower right corner of the graph. Otherwise, if she/he believes that the controllers can manage more flights, he can choose a schedule in the top left corner of the graph, resulting in much less delays, at the price of a higher congestion in some sectors.

Post-Pareto Analysis

In this section, we attempt to reduce the number of trade-off solutions while considering the stochastic nature of the objective functions.

We will use a standard agglomerative hierarchical clustering technique. We have chosen a hierarchical clustering technique (over a partitioning one, such as kmeans) because it allows us to easily adapt the level of granularity of the clusters, and it does not require the number of clusters to be specified in advance.

When carrying out a hierarchical cluster analysis, the process can be represented on a diagram known as a dendrogram. This diagram illustrates which clusters have been joined at each stage of the analysis and the distance between clusters at the time of joining.

In our approach, we have generated a dendrogram from the solutions enclosed in the Pareto front as de-picted in figure 4. Figure 5 shows the dendrogram obtained using the Euclidean distance as a measure of similarity and the Ward's method as a hierarchical clustering procedure. The height of a node represents the distance between its two sub-clusters. Therefore, nodes that are low on the dendrogram form clusters whose elements are closer to each other than nodes that are placed higher up. For example, the cluster 1, 9 forms a cluster whose elements are closer to each other than the cluster 5, 8, 11. The visual inspection of a dendogram can also reveal long branches leading to isolated solutions, which may indicate the presence of "outliers". An outlier in our context is a solution that is radically different from other solutions in the set.

Once the dendrogram has been generated, we need to decide a cut-off at a certain level in order to obtain a set of clusters. For example, with a cut-off value of 60, shown with dotted line on figure 5, we have generated 3 clusters formed by the set of elements under the three branches cut by that line and framed by rectangles.

A recurrent problem that many clustering algorithms encounter is the choice of the number of clusters. Thus, different cluster validity indices have been suggested to address this problem.

The Silhouette [START_REF] Rousseeuw | Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[END_REF] and The Elbow method [START_REF] Thorndike | Who Belongs in the Family ?[END_REF] are two of these cluster validity techniques. They are helpful metrics to guide the choice of the best number of clusters.

Figure 6 shows the Scree plot which represents the number of clusters against the total within sum of squares, we can see that the error decreases as the number of clusters gets larger, this is because when the number of clusters increases, they should be smaller, so distortion is also smaller. The idea of the elbow method is to choose the number of clusters at which the SSE decreases abruptly. This produces an "elbow effect" in the graph. In this case, the Elbow method has selected 3 clusters.

Figure 6: Scree plot Figure 6 shows the clusters silhouette plot with an average silhouette width of 0.47, which indicates a satisfactory cluster quality. After we have validated the num-Figure 7: Silhouette plot ber of clusters, we need to select a representative solution for each cluster. To do this, the solution that is closest to its respective cluster centroid is chosen as a good representative solution. The obtained results of the clustering analysis are depicted in table 1 and figure 8 represents the final solutions.

Conclusion

This paper has presented a probabilistic model to handle the propagation of the uncertainty from the trajectories to the sectors. Then the probabilistic model was used within an optimization algorithm for scheduling all flights at boundaries of the sectors in order to minimize the expected cumulated delays and the expected sector congestion. Furthermore, in order to illustrate how the theoretical model can be useful in practice, we presented some results on an instance with 21 flights, including 15 departures and 6 arrivals in the Terminal Manoeuvring Area (TMA) surrounding Mohammed V International Airport.

The decision-making stage was then performed with the aid of data clustering techniques to reduce the size of the Pareto-optimal set in order to help the decisionmaker to select a preferred final design solution.

Figure 1 :

 1 Figure 1: Marginals probability over two waypoints

Figure 2 :

 2 Figure 2: Probability to be in the sector

Figure 3 :Figure 4 :

 34 Figure 3: Probability of congestion

Figure 5 :

 5 Figure 5: Dendogram

Figure 8 :

 8 Figure 8: Reduced Pareto Front

Table 1 :

 1 Final compromise solutions

	Cluster Solutions	Cluster centroid	Representative solutions
			Expected Cost	Expected Cost	Expected Cost	Expected Cost
			of Delay	of Congestion	of Delay	of Congestion
	cluster1 1, 6, 9, 10	401.97	126.15	398.24	127.48
	cluster2 2, 3, 4, 7, 12	436.29	118.02	435.51	118.11
	cluster3	5, 8, 11	492.33	114.90	491.5	114.99

European Organisation for the Safety of Air Navigation, it coordinates and plans air traffic control for all of Europe. This involves working with national authorities, air navigation service providers, civil and military airspace users, airports, and other organisations. Its activities involve all gate-to-gate air navigation service operations.


T Bouhouche

A Raghib

B Abou

El Majd

M Bouya

M Boulmalf

A Middleware

Introduction

RADIO FREQUENCY IDENTIFICATION, or RFID, is a generic term for technologies that use radio waves to automatically identify people or objects [START_REF] Legner | RFID-Based maintenance at Frankfurt airport[END_REF]. This technology has recently seen growing interest from a wide range of industries such as retail, pharmaceutical, and logistics [START_REF] Gaukler | RFID in a retail supply chain with stock-out based substitution[END_REF][START_REF] Choi | Coordination and risk analysis of VMI supply chains with RFID technology[END_REF][START_REF] Vales-Alonso | Multiframe maximum-likelihood tag estimation for RFID anticollision protocols[END_REF][START_REF] Chen | A novel anti-collision algorithm in RFID systems for identifying passive tags[END_REF]. In these domains, RFID technology holds the promise eliminating many existing business problems by bridging the economically costly gap between the virtual world of IT systems and the real world of products and logistical units. Common benefits include more efficient material handling processes, elimination of manual inventory counts, and the automatic detection of empty shelves and expired products in retail stores. RFID technology has a number of advantages over other identification technologies. It does not require line of-sight alignment, multiple tags can be identified almost simultaneously, and the tags do not destroy the integrity or aesthetics of the original object. The location of tagged objects can thus be monitored automatically and continuously. In traditional RFID applications, such as access control, there was a little need for an RFID middleware because the RFID readers were not networked and the RFID data were only consumed by a single application. The appearance of the first RFID middleware on the market enables the integration of RFID technology in various fields (Air, Logistic, etc). Nevertheless, the fact remains that they allow the identification of several technical issues regarding this integration. In novel application domains, such as supply chain management and logistics, there is no longer a 1-to-1 relationship between reader and application instance. However, in these domains, many readers distributed across factories, warehouses, and distribution centers capture RFID data that need to be disseminated to a variety of applications. This introduces the need for an RFID infrastructure that hides proprietary reader device interfaces, provides configuration and system management of reader devices, and filters and aggregates the captured RFID data. This paper has been organized in the following way: The first part is comprised of a presentation of the RFID systems. A description of the proposed MedRFID and the FindLuggage application are presented in the second part. The second and final part concludes the paper.

RFID Systems

The RFID systems basically consist of two or three elements: a tag/transponder and a reader for a Simplified RFID system, or a tag/transponder a reader and a middleware deployed at a host computer. The RFID tag is a data carrier part of the RFID system, which is placed on the objects to be uniquely identified. The RFID reader is a device that transmits and receives data through radio waves using the connected antennas. Its functions include powering the tag, and reading/writing data to the tag. As shown in Fig. 1, the signals sent by the reader's antennas form an interrogation zone made up of an electromagnetic field. When a tag enters this zone, it gets activated to exchange data with the reader [START_REF] Al-Mousawi | Performance and Reliability of Radio Frequency Identification (RFID)[END_REF]. Later, the identification data read by the RFID reader is processed by the software system, known as the RFID middleware. The RFID middleware manages readers, as well as filters and formats the RFID raw tag data so that they can be accessed by the various interested enterprise applications [START_REF]Information Security Radio Frequency Identification Technology in the Federal Government[END_REF]. Hence, the middleware is a key component for managing the flow of information between tag readers and enterprise applications [START_REF] Burnell | What Is RFID Middleware and Where Is It Needed?[END_REF]. Major advantages of using RFID as an auto-ID system are the following: • RFID readers do not require a line of sight to access data from the RFID tags.

• RFID systems can read data over varied range from few centimeters to few hundred meters.

• RFID readers can interrogate, and make RFID tags readings much faster.

• RFID systems can read and write different sizes of data from reader / to the tag, based on the type of tag.

• RFID systems can read tags in harsh environments, without any human interference. Indeed, since the introduction of RFID technology in global solutions, a recurring issue is the integration of RFID in a transparent and efficient manner. As a result, there is a strong mobilization on the issue of research and development in mainly three areas:  Objects Identification:

For a better identification of object, we need to focus the development on a standardization of RFID tags and a good transcription codes on RIFD tags (Algorithm).

 Communication between objects and middleware:

The start point for this part is a good choice of useful RFID technologies, after a development of communication protocols between readers is necessary, following the middleware standardization can help us to done a good follow up with the objects, then the normalization of protocols shall needed for the better lopping of the communication steps.

 The information processing:

Decoding the RFID information collected (standard, algorithm) is important for structuring collected RFID information to make it usable directly by any application of the information system, which need a real Time treatment volume (architecture allows scaling mass treatment of RFID data collected).

A. RFID System Components:

RFID systems are produced by many manufacturers and exist in countless variants. However, a RFID system consists mainly of three components; the transponder/tag, reader, and RFID middleware.

1) RFID Transponder/Tag: A RFID transponder, or tag, consists of a chip and an antenna [START_REF] Roy | Ubiquitous Electronic Tagging[END_REF]. A chip can store a unique serial number or other information based on the tag's type of memory. The tag's type of memory can be read-only, read-write, or write-once and read-many [START_REF]Information Security Radio Frequency Identification Technology in the Federal Government[END_REF]. Read-only tags are much cheaper to produce and are used in most current applications. Read-write tags are useful when information needs to be updated [START_REF] Al-Mousawi | Performance and Reliability of Radio Frequency Identification (RFID)[END_REF]. The antenna is used to transmit information from the chip to the reader, and the larger the antenna the longer the read range. The RFID tag can be either attached or embedded in an object to be identified, and can be scanned by mobile or stationary using radio waves [START_REF]Information Security Radio Frequency Identification Technology in the Federal Government[END_REF][START_REF] Jaselskis | Implementing radio frequency identification in the construction process[END_REF][START_REF] Chae | Application of RFID Technology to prevention of collision accident with heavy equipment[END_REF][START_REF] Legner | RFID-Based maintenance at Frankfurt airport[END_REF][START_REF] Want | An introduction to RFID technology[END_REF].

2) RFID Reader: A RFID Reader is a scanning device that reliably reads the tags and communicates the results to the middleware. A reader uses its own antennae to communicate with the tag by broadcasting radio waves to which all tags within range will respond. Readers can process multiple items at once, allowing for increased read processing times. They can be either mobile or stationary, so that their optimal employment in the area can be found [START_REF] Raghib | Multi-level optimal deployment of RFID readers by using particle swarm optimization[END_REF][START_REF] Bhattacharya | Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization[END_REF], and they are differentiated by their storage capacity, processing capability, and the frequency they can read [START_REF]Information Security Radio Frequency Identification Technology in the Federal Government[END_REF]. Different designs of readers exist, because different applications have different requirements. RFID readers are classified into three types [START_REF] Al-Mousawi | Performance and Reliability of Radio Frequency Identification (RFID)[END_REF]: • OEM readers: Original Equipment Manufacturers readers are mostly used for data capture systems, access control systems, and robots. • Industrial use readers: used in assembly and manufacturing plant.

• Portable readers: These readers are more mobile than the other readers, and supported with a LCD display and keypad. This kind of readers is used in animal identification, device control and asset management applications.

3) RFID Middleware : The middleware refers broadly to software or devices that connect RFID readers and the data they collect to enterprise information systems. RFID middleware helps make sense of RFID tag reads, applies filtering, formatting and logic to tag data captured by a reader, and provides this processed data to back-end applications [START_REF] Burnell | What Is RFID Middleware and Where Is It Needed?[END_REF]. RFID middleware serves in managing the flow of data between tag readers and enterprise applications, and is responsible for the quality, and therefore usability of the information. It provides readers with connectivity, context-based filtering and routing, and enterprise/B2B integration. RFID middleware design and components will be discussed further in the next sections.

When designing a RFID middleware solution, the following issues need to be considered:

 Multiple hardware support: The middleware must provide a common interface to access different kinds of hardware offering different features.

 Synchronization and scheduling:

There should be intelligent scheduling and synchronization among all the processes of the middleware. This minimizes the latency and improves the efficiency of the middleware.

 Real-time handling of incoming data from the RFID readers: The middleware should handle the huge amount of data captured by the connected readers in real time without read misses.

 Interfacing with multiple applications:

The middleware should be capable of interacting with multiple applications simultaneously, by catering to all the requirements of the applications with minimal latency.

 Device neutral interface to the applications:

The application developer should only use the generic set of interfaces provided by the middleware independently of the type of hardware connected to the system.

 Scalability: The middleware design must allow easy integration of new hardware and data processing features.

B. RFID Middleware Components:

A RFID middleware is the interface that sits between the RFID hardware and RFID applications. It provides the following advantages:  It hides the RFID hardware details from the applications.

 It handles and processes the raw RFID data before passing it as aggregated events to the applications.

 It provides an application level interface for managing RFID readers and querying the RFID data.

A layer of the RFID middleware incorporates all the device drivers of different hardware and exposes the application standard interfaces to access this hardware. If the application was provided with all the device drivers of all connected readers, it will be a hard job to manage and interface each of the devices. The application developer will then need to understand all the hardwarespecific internals and operations. Also, the application, if provided with the huge amount of raw tag data reported by the readers, will find it very difficult to process the data in real time. A RFID middleware provides a standardized way of dealing with this flood of information, which processes the raw data and provides the application with clean and filtered data. RFID middleware is generally composed of four major layers:  Reader Interface:

The reader interface is the lowest layer of the RFID middleware which handles the interaction with the RFID hardware. It maintains the device drivers of all the devices supported by the system, and manages all the hardware related parameters like reader protocol, air interface, and host-side communication.

 Data Process or and Storage:

The data processor and storage layer is responsible for processing and storing the raw data coming from the readers. Examples of processing logic carried by this layer are data filtering, aggregation, and transformation. This layer also processes the data level events associated with a specific application.

 Application Interface:

The application interface provides the application with an API to access, communicate, and configure the RFID middleware. It integrates the enterprise applications with the RFID middleware by translating the applications" requests to low level middleware commands.

 Middleware Management:

The middleware management layer helps managing the configuration of the RFID middleware, and provides the following capabilities:  Add, configure, and modify connected RFID readers.

 Modify application level parameters such as filters, and duplicate removal-timing window.

 Add and remove services supported by the RFID middleware.

RFID readers are typically abstracted as a logical reader which is either a collection of several readers or a part of the reader. This grouping mechanism is used where there is a need to have a set of readers capturing data from a particular area such as a warehouse with many loading docks. The advantage of this is that the application can query a small number of logical readers rather than having to aggregate events from each of the individual readers.

There are two standardized interaction models used to define the communication between the middleware and the applications. An application can operate at synchronous mode when requesting services on demand or asynchronous mode when it registers for information to be sent to it when certain conditions are met. RFID middleware usually provide some kind of data filtering, because sometimes it might be required to report only certain type and value of the tag data to the application. The application must provide a set of defined patterns to the middleware. The middleware then allows only data that matches the pattern to be reported to the application. For example, if an application must only see tag data that starts with a specific pattern such as "XYZ20", the filter can be set to this value by the application and communicated to the middleware [START_REF] Al-Mousawi | Performance and Reliability of Radio Frequency Identification (RFID)[END_REF].

MEDRFID

Introduction: Why MedRFID?

Because some existing RFID middleware use a standard functionalities, our concern is to design and implement a specific middleware platform called "MedRFID, which is distinguished by its innovative aspects in terms of architecture including mobility to enable the collection of information from mobile RFID readers, operating aspects of the data in real time with the ability to generate a business rules engine and especially by integrating communication protocol RFID readers disregarding the dependency to the initial manufacturer protocol. The implementation of this innovative architecture will give real flexibility for users of the middleware and above all a means of real-time processing a large volume of data that emanate from heterogeneous environments while ensuring ease of integration of these data and information and their best use by business applications systems.

MedRFID: Design and Architecture

RFID middleware is the core in the implementation of RFID technology in companies that have large amount of data to be processed and, above all, to share with the various components of the value chain and with its partners. Thus, the successful integration of RFID within companies is conditioned by the successful design and implementation of RFID middleware. That is why our research team chose to design and implement a RFID middleware which integrates the latest innovations in the field.

MedRFID is a middleware that has been designed to meet the overall functionality that must contain a conventional RFID middleware, but it also contains areas of innovation mainly around the mobility and independence of manufacturers. It is an innovative device management and event processing platform at the edge of the enterprise. It is designed to provide a scalable, extensible platform for deployment, and management of rich RFID and sensor solutions. As shown in Fig. 2, the MedRFID architecture is composed of seven major layers

2.1) Collect:

The collect module is the heart of the middleware. It's responsible of all collected tag's data, and it's a key instrument of the connection with the readers and the software intelligence. Moreover, it's used to control the readers, add data to the tags and format them in a single format regardless the used reader. To control the readers, this module has embedded an LLRP driver but also each driver needed for RFID readers, which don't support LLRP. These drivers are often provided by RFID manufacturer in different programming languages. Each tag is formatted in an abstract layer in order to have the same format for all the tags whether it come from a mobile device, a LLRP or a specific reader (manufacturer driver).

2.1.1) LLRP :

LLRP (Low Level Reader Protocol) is a standard that facilitate the communication between the software and the readers. All the RFID readers speak the same language, so they can all understand the software whichever their types. This allows to the customers the choice of its hardware with the software. If they want to change the hardware, it could be without affecting the software solution. The collect process is as follow:

The software sends to the readers a RO Spec (Reader Operation spec). The RFID readers use these specs when they are enabled and when the start condition is reached. Then, the reader sends to the software a report of each tag or a list of them.

2.1.2) Custom Drivers :

Some readers do not support LLRP, but these readers may be interesting to support. Indeed, some of them are really good in RFID performance. Fortunately, readers manufacturer provide most often the driver in different languages to communicate with the reader. Languages mostly supported are C, C# and JAVA. So, to implement the driver for these readers, we have to develop a separate module which is an interface between the driver provided by manufacturer and the command provided by the middleware.

2.1.3) Mobile Readers :

Some of the mobile readers fully support LLRP and they can be used like fixed readers. For others, there are also drivers provided by manufacturers for control like the fixed RFID readers. These drivers can be embedded in a mobile app and transmit data to the middleware backend when it is connected to the cellular network.

2.1.3) Sensors:

The middleware should also support Sensors. There are two types of sensors: IN Sensors and OUT Sensors. IN Sensors are detectors, radars and are used to trigger the collection of readers. For example, when a tracked object is coming, IN Sensors detect it and trigger the start of the collection. With these sensors, it is possible to read only when there is something to track and facilitate the process (hardware + software). OUT Sensors are used by users on the ground. It can be for example lights or alarms. They are used to secure the RFID process, for example, a green light may be switched on when a tag is read to show to the user that it can continue. Or a red light can be switched on if there is a problem with the tag read or if an access is forbidden to this user.

2.2) Mobile Application:

The software should include a mobile application. Mobile RFID readers allow RFID collect stationary objects. The reader is embedded in a mobile device with powerful RFID antennas and can communicate with the backend over cellular network. Tags are stored directly in database if a connection is established with the backend or the mobile application stock tags read in the local memory if not. Like the collect module, tags are formatted in the same way in an abstract layer. For the backend, reader device is transparent whether it is from a fixed reader or a mobile device. A mobile application is useful for huge objects to track and it is not possible to read them with a fixed reader.

2.3) Administration :

The administration module concerns the settings of the middleware. RFID Readers, antennas and sensors settings can be modified such as IP Address, Power, and numbers of antennas… In addition, each reader's RO Specs can be altered: Start/Stop trigger, numbers of tag reports… Tag process preferences can also be modified, for example: Duration of the duplicate elimination process, decoding process…. Finally, Business rules can be added, edited or removed. Reader supervision will also be embedded in this module. The supervision is a module which consists of logging each event about a reader. When something does not work properly, the middleware should send an alert (via mail, messages, SMS …) to explain where the problem is.

Problems can be about the network, the reader, or its antennas. Ideally, the middleware should explain what type of alert it is and what the problem is. This is possible by doing multiple tests: For example, if there is no tag reads for X hours but the ping to the reader is OK, we may think it's a problem in the reader or its antennas (or both) but not a network issue.

2.4) Tag Process:

Each tag will go through a tag process that can have multiple operations such as filtering, eliminating duplicates, time stamping, contextualization and/or formatting. The tag process is composed of three layers:

 Tags Filter: The Tags filter is the layer which removes tags that we don't want to read. Each tag has a Unique ID and a User Memory where you can write personal data. This memory is often used to filter tags because every tag that we are interested in contains this value in its User Memory.

 Tags Aggregation: The Tags aggregation is the mapping between the Tag Number (UID) and the business data we have in a database. The tag number is just an ID but it's often (always) linked to personal or business data. For example, the box with the RFID N°XXX (UID) contains three of YYY items (Business Data).

 Tags Format: In some case, some tags are encoding following a strict format. So, the number contains data. There are multiple formats for RFID Tags. The middleware should include encoding and decoding algorithm in order to get back the information.

2.4.1) Duplicate Elimination:

The duplicate elimination is very important in an RFID application. It's often the first layer in the tag process in a middleware. RFID readers can read more than 100 tags per second. Indeed, the same tag is read more than once but only one read is important during this second. So the duplicate elimination process permits to keep only one read of a tag during a customizable duration. With the duplicate elimination filter, the middleware eases the database from storing duplicate data. So, instead of 300 reads/writes in 3 seconds, there are only 3. Each tag will then be evaluated in a filter layer and then a business rules engine to redirect it if needed.

2.4.2) Filtering:

A filter defines rules that allows or stops tags during the tag process. Sometimes, multiple actors in the same location use RFID tags. In order to only keep the interesting tags, filter rules allows removing tags that we don't need to read. Filtering can be on specific serial numbers of tags, or in business number of a tag. Some tags are encoding following different norms. These norms such as IATA RP1740c or GRAI-96 encode business data in the tag. For example, IATA RP1740c encodes data about flights, so in the tag number is encoded: the company prefix, the date time of the flight, source and destination airports etc… By decoding the tag, it is possible to filter tags that have the company prefix for AIR France or other companies. If a tag match the filter value, then the tag will be saved and continue through the tag process. Else, it is deleted from the tag process. With the filtering, only interesting tags are collected and saved in the database.

2.4.2) Business Rules Engine:

Business rules are rules that match some tags or dates and practice different services according the tag. This allows for example to send data to a client for all the tags of its client but hide him the rest of the reads. It is possible to edit and add new rules to the engine. Then, rules are affected to the tags read events through the engine.

2.5) Connectors:

Tag reads, once being treated in the tag process and by the business rule engine, will then be transmitted via connectors such as Database connector, Files or Queues. The connectors allow external application to get the data provided by the middleware. External applications may be presentation software in order to show statistics, tag reads, etc. It can also be formatted files such as commaseparated files or web services in order to send the data to another software in order to automatically fulfill stocks etc.

2.6) The User Interface:

This is the view of the middleware. It is composed of three tabs: Reads, Writes and Administration. The UI of the middleware shows tag reads in real time and permits the configuration of RFID readers / application. The "Reads" view provides information about reads, EPC, date time, reader, antennas and ensures that the tag has been written into the database. The user interface can also be developed by using the web service API. The "Administration" view represents all the settings alterable for the middleware. Reader settings: It comprises all the settings for each reader. How a read is triggered, how many antennas are connected to the reader, how long is the duplicate elimination. The middleware settings represent all the settings alterable for the middleware itself: Connect or disconnect readers to the middleware, the SQL connection settings, and paths to different log files.

2.7) Client's IT System:

Each client of the middleware can be connected to it with the connectors and add data to its information system. The middleware provides different connectors to transmit data to other information system. So, it is possible for external clients to develop a little interface in order to add the RFID data to their existing application or develop a new application by using these connectors. The client's IT system is not a part of the middleware but we have to think it may be connected to it.

FIND LUGGAGE APPLICATION

With tens of millions of passengers traveling through the major European cities each year, through hundreds of check-in desks, luggage is extremely important for these airports. Traditional moving-belt bar code scanners require 'line of sight' in order to correctly read labels, but these could be hidden as the luggage moves on conveyor belts, rendering the luggage unidentifiable. This could slow the process down, as manual intervention is required to obtain the information and locate the luggage, and could directly contribute to misplaced or lost items. In the airline industry, RFID considered as one of the best technologies that help the sector to remain competitive and innovative and to face the demands of increasingly cost and security, and increased traffic. This, to accommodate the needs of travelers but also to be operational. Travelers are always afraid of luggage loss or theft. Now, RFID helps airports and airlines to limit this kind of risk, and meet the growing demands in the field of luggage handling (speed, security, cost control, etc.). As for industries, they benefit from changes in the direction of lower costs, faster processes, and improved communication.

The actors that interact directly with the system studied are:

 Holders of RFID tags: The luggage.

 RFID Readers: responsible for receive data, activation of RFID tags in each area, and communicate data to the middleware (MedRFID).

 MedRFID: collecting and sorting the data transmitted by the readers, to share with relevant the application or store them in a database.

 Employee: who may be responsible or single control officers who watch over the path of luggage to aircraft in good conditions, otherwise make alerts or even answer questions dedicated to specific luggage. Our study consists of the management and reporting of the processing of luggage immediately following registration (check-in). The RFID tag is already encoded and contains luggage's data, printed and stored on it. Then, by using MedRFID, we will track the luggage at any time, and any different points in passages (the inputs / outputs of luggage sorting areas, security & control, loading / unloading, etc.) as shown for example in figure 3, which allows an automated traceability, reliable and in real-time for a good geo-location. To properly manage followed, we offer an application that will be the RFID interface solution installed and designed for both the environment and luggage, in order to track, locate luggage in real time, and ensure safe movement of flights, as well as avoiding wastage of time lost in inventories. When a passenger makes the check-in step, an RFID tag is attached on his luggage containing specific information (ticket number, destination, etc.). Exploitation of this tag allows identifying his luggage at various traffic areas along its presence in the airport. After the passage of luggage in an area, the RFID reader passes to the tag an enough energy to its activation, with the aim to read its information or to add new data, by interacting in both cases with the MedRFID. After receiving the luggage data, MedRFID is responsible for centralizing, sorting and filtering it for the needs of business applications, by providing understandable and reliable data to the other applications or database. Therefore, the user can easily locates a specific luggage, checks the safety, sorts, and even monitors it in a very small time. Our application deals with actual and real requirements by identifying the objectives, priorities, rules of management and key processes of the organization as shown in figure 4. The use of RFID traceability optimizes all stages of the processing airport, from check-in to loading bunkers namely: Registration luggage, security checks, sorting, reconciliation and loading. To overcome these problems and automate tracking of luggage, we decided to design and implement a web application: FindLuggage, based on the RFID solution and using the middleware MedRFID for tracking and monitoring a luggage in real time, and reducing the costs. As shown in figure 5, the use of this service requires the implementation of a RFID reader at different points of traceability, and the placement of each piece on an RFID tag; this is followed by the use of MedRFID luggage tracking to collect information on these different points of traceability at the airport and save them in a database. Latterly, the application plays a role in integrating these data and providing information about the luggage. RFID readers can be deployed at both the check-in and the luggage collection stages, which enable to read the attached tags as it left the airport or, prior to collection by passengers. After the deployment of RFID readers, the middleware MedRFID collects traceability information on luggage at all tracking points within the airport and makes them available to the application via a database. The application FindLuggage operates and integrates these data and then uses them to be able to indicate the pathway of each luggage through the airport. It allows to find a misplaced luggage or give more details on the passage of a bag at a given collection point. As show in figures 6 and 7, the FindLuggage application facilitates access to information for the employee according to its type (lead or manager). He has the possibility to track the complete path of all flight's luggage or a specific one, using the flight number or the identifier of a / set of RFID tag. This possibility is also allowed to the passengers in order to have a vision on their luggage, but with very limited access to the consultation option.

MedRFID's FindLuggage application has a several attractive features:

 It improves traceability due to better-read rates.

 An easy integration of many readers, allowing a real time tracking of luggage.

 Reducing labor costs.

 Reducing the number of lost or delayed luggage.

 Reducing the luggage handling time.

 Larger encoding capabilities: Passenger/ Luggage data traveling with luggage.

 Improves the comfort and safety of people in transit, but also to simplify their movement within airports.

 To have a global vision on luggage present in a specific area and manage it in real time.

 Generate reports and statistics of usage history.

 Filtering, aggregation and Fusion data from multiple readers in real time, Detect anomalies.

 Analysis and research related data in external databases.

 Etc.

These advantages make the application more robust by having two major concepts applied for the traceability of luggage. First, it provides a flexible and convenient structure for monitoring and management of luggage. Second, it can be adapted to the needs of airlines and passengers for an organization more personalized in the airport.

Conclusion

During the last decade, this wide spectrum of applications that have been observed is nothing but the result of the emergence of research and development, which were conducted in different areas such as logistics, air, and military to field security and personal services. It falls into the so-called Internet objects or connected objects. Connected objects and communicating could provide opportunities for the industry in the years to come, and a new middleware architecture become strategic regarding a massive rise of "unintelligent" internet of objects is expected by 2020. This work response to this need with a vision of connecting functions placed in objects. An estimated 50 billion items could be tagged by 2020. This ability of communication will develop virtual reality devices. Thus research and innovation in the field of data integration collected by technologies that deal with communication between objects is still needed to support this development and is a major factor for the success of these technologies and their relevance. This paper introduces RFID technology and the role of RFID middleware in the enterprise information systems. It also describes the design of our middleware MedRFID and its architecture. With such an innovative architecture, MedRFID provides extensibility, scalability, abstraction and ease of deployment like the FindLuggage application demonstrates. To be compatible with different areas including logistics and airports, it integrates respectively EPCglobal standards and IATA standards making it easily deployed in heterogeneous sites.

Figure 1 .

 1 Figure 1. RFID System components.

Figure 2 .

 2 Figure 2. MedRFID Architecture.

Figure 3 .

 3 Figure 3. Example of platform tracking luggage in the airport.

Figure 4 .

 4 Figure 4. Luggage functional process.

Figure 5 .

 5 Figure 5. Communication between MedRFID and FindLuggage.

Figure 6 .

 6 Figure 6. MedRFID application : FindLuggage.

Figure 7 .

 7 Figure 7. MedRFID application : FindLuggage.


Hicham Rahil
email: hichamrahil@gmail.com

Badr Abou

El Majd

Mohammed Bouchoum

Airline Schedule Disruption Management The impact of flight delays on connection loss

Air travel demand is important and many travellers choose to drive to larger airports instead of flying from a small airport for many reasons, especially availability of non-stop flights. Another reason is perceived reliability of service. Consultants have pointed to a large number of delays and cancellations as reasons for low passenger. However, the effect of these flight delays on actual travel times is less clear. Because connections are usually necessary when traveling from small airports, departure delays may lead to missed connections. In the case of a cancellation, need to wait several hours (often overnight) for the next flight due to the small number of daily departures. This paper evaluate the impact of delays and cancellations on the profit earned through the seats captured on new opened routes. This aspect of decision-making comes in the form of multi-objective problem by testing the impact of a new opened route in terms of flight delays costs, financial gain and the quality of the service provided to a target customer. The NSGA-II algorithm is adopted to generate a front of Paretooptimal compound of a number of optimal departure times to the new destination while ensuring the best fill rate, and a minimum flight delays. The experiences are based on the flights of the Royal Air Maroc Company on the Casablanca hub.

Introduction

In Air Transport field, the most important factor sought by the potential passenger is the shortness of the trip. Passengers therefore preferred the direct flights between the airport of origin and destination. When the passenger is obliged to carry out a correspondence, airlines have an incentive to attract these passengers to increase the profitability of their lines by providing a continuous service from airport of departure, passing via the connecting airport until reaching the final destination. Royal Air Morocco (RAM), the Morocco's flag carrier, hopes to take place among the major airlines pulling profits from the position of the Morocco being the Africa portal on Europe and well obviously at the air hub put at his disposal in Casablanca. In addition to its flights to more than 80 destinations, Royal Air Morocco now offers more flights around the world by many airline partners. An alliance would benefit the position of RAM to the Morocco, but also central Africa / Western, the largest international market of RAM after Europe. In this work, we are interested in the problem of insertion of the new departure and arrival times to the existing program of an airline, and we opted for multi-objective optimization to solve this problem, the multi objective optimization meets the need to satisfy conflicting requirements. Therefore, to travel from Casablanca to Agadir, we will seek to minimize the cost; the time spent and maximize comfort. The plane will be cheap, fast, and polluting while the car will be economic, long and low in pollutants. Finally, the train will be a compromise solution. As there are no better in every way than another solution, a different compromise according to persons must be chosen. The choice is subjective, and it is essential to provide the set of choices so do not exclude a possibility. Multi objective optimization is primarily a tool for decision support, and it is a person who will make the final decision.

Outline of the paper

This article will focus on the application of optimization algorithms evolutionary (EA) using air routes scheduling problems and provide a brief overview on how airlines use these techniques to develop and assess the decisions of schedule amendments required at the last minute. Section 2 of this document provides a mathematical formulation of the problem of adding new lines in an existing program, under specific constraints, cost and profitability associated with a given calendar change. Section 3 explains the optimization process to fix the problem. Section 4 will provide experimental results that explain more and detailed model and the optimization process, discuss possible extensions of the other sketches and model of research. Section 5 concludes the paper and specifies certain points of view. At the end of the paper, we provide a complete list of references highlighted in the document.

Methods

Problem Formulation

A wide variety of problems in industry, and many other fields, involve the simultaneous optimization of several objectives. In many cases, the objectives are defined in incomparable units, and they present some degree of conflict among them (i.e., one objective cannot be improved without deterioration of at least another objective). These problems are called Multiobjective Optimization Problems (MOPs). Let us consider our problem of new route insertion in an existing flight program, where the air transport company is interested in minimizing the total duration of the generated delays over the hub to improve customer service. On the other hand, the company also wants to maximize the number captured seats in order to improve the market benefits. Clearly, these objectives are in conflict since capturing more seats increases benefits, but increases generated delays. In addition, the objectives of this problem are expressed in different measurement units. The method most commonly adopted in Multiobjective optimization to compare solutions is the one called Pareto dominance relation [1], which, instead of a single optimal solution, leads to a set of alternatives with different trade-offs among the objectives. These solutions are called Pareto optimal solutions or nondominated solutions. Although there are multiple Pareto optimal solutions, in practice, only one solution has to be selected for implementation. Therefore, in the Multiobjective optimization process we can distinguish two tasks, namely: i) find a set of Pareto optimal solutions, and ii) choose the most preferred solution out of this set. Since Pareto optimal solutions are mathematically equivalent, the latter task requires a Decision Maker (DM) who can provide subjective preference information to choose the best solution in a particular instance of the Multiobjective optimization problem. In this paper, the company wants to open a new destination, and the purpose is of course the conquest of new markets. Indeed, after the opening of a new air route on departure from its air hub, the company will seek to connect this new destination with other airports served by the same company. A study of the market is developed beforehand to set among airports served by the company, those that present important potential of passengers wishing to go to the new destination via a connecting flight through the hub of the company. Let us note by x the departure time toward the new destination. Actually, x is the decision variable of the problem, which will affect the two criteria considered in the problem. We will also note by "t" the total duration of the flight to the new destination, and of course the value x +t is the scheduled return hour from the new destination.

We will limit our study to the 20 first airports ranked by origin-and-destination passenger volume and the busiest one is airport k where k = 1. However, decision-makers may extend targets considered according to demand and importance of the proposed destination. Let Ak (resp. Dk) the set of the weekly arrivals time (resp. departures time) from an airport k ϵ K (K = {1,2,..,20) to the hub, where K is the set of twenty airports served weekly by RAM company and ranked by origin-and-destination passenger volume. aki the ith arrival time (resp. dkj the jth departure time) with i,j ϵ {1,2,..,Nk}. aki and dkj are ranked in ascending order), and Nk is the weekly frequency of routes between hub and given airport k ϵ K, we take the minute as unit of time. We note that, arrivals and departures time during the week are included in the interval [0, 10080].

𝛼 𝑘𝑖 = { 1 𝑂𝑢𝑡𝑏𝑜𝑢𝑛𝑑 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑠 𝑟𝑒𝑎𝑙𝑖𝑧𝑒𝑑 𝑓𝑜𝑟 𝑎 𝑘𝑖 0 𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝛿 𝑘𝑗 = { 1 𝐼𝑛𝑡𝑏𝑜𝑢𝑛𝑑 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑠 𝑟𝑒𝑎𝑙𝑖𝑧𝑒𝑑 𝑓𝑜𝑟 𝑑 𝑘𝑗 0 𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
wk are the weight vectors given by: is the sum of the passenger volume of the twenty busiest air routes served by RAM, ranked by origin-and-destination passenger volume. This parameter is introduced to encourage the connection of airports with important passenger volume. x ϵ [x + t + tmin,10080-tmax], where tmin is the minimum connecting time, and tmax is the maximum allowed connecting time.

𝑤 𝑘 = 𝑃 𝑘 ∑ 𝑃 𝑖 20

Multiobjective functions

In order to find the best departure time x, we maximize the objective function f1 and minimise f2 :

𝑓 1 (𝑥) = ∑ 𝑤 𝑘 × 𝑋 𝑖𝑗 𝑘 𝑘𝑖𝑗 (1) 𝑓 2 (𝑥) = ∑[ 𝐶 𝑘𝑖 𝑋 𝑖𝑗 𝑘 (𝑆𝑇𝑎𝑘𝑖 -𝑎𝑘𝑖 -∆𝜏) 𝑘𝑖𝑗 (2)
+ 𝐶 𝑘𝑗 𝑋 𝑖𝑗

𝑘 (𝑆𝑇𝑑𝑘𝑗 -𝑑𝑘𝑗 -∆𝜏 ) ]

A good choice of x, will enhance the value of the Objective function f1 will be maximized with guaranteeing the connection in both directions, Outbound and Inbound through the factors 𝛼 𝑘𝑖 multiplied by 𝛿 𝑘𝑗 , and by promoting the best passenger volume through wk. f1 is expressed through scalarization method, the weighted sum (WS) method. The WS method is the commonly used scalarization method because of its simplicity, ease of use, and direct translation of weight into the relative importance of the objectives [START_REF] Marler | The weighted sum method for multi-objective ptimization: new insights[END_REF].

Its drawbacks are also well known and discussed in literature [START_REF] Das | A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems[END_REF]. These include the followings:

 It misses solution points on the non-convex part of the Pareto surface;  Its diversity cannot be controlled, therefore even distribution of weights does not translate to uniform distribution of the solution points;  The distribution of solution points is highly dependent on the relative scaling of the objective.

For efficient results, the objective function 𝑓 2 calculates only the flight Delay Losses of connected flights. Thus the found results will help to quantify the impact of cumulated delays caused by these connections, and then will give us an idea about the resulting costs if we opt for this strategy. Finally, the minimization of 𝑓 2 will involve the reduction of the generated delays and will make subsequently the correspondence more comfortable for passengers, and therefore, will automatically reduce the costs related to passenger accommodation and all other related charges.

𝑓 2 is the Objective Function of Flight Delay Losses. In most cases, either the arrival or departure flights, the actual arrival time is almost impossible to be consistent with the estimated time, so we make the following provision. The following two situations are not treated as delay: (1) the actual time is earlier or later than the estimated time within Δ𝞽:

(2) all the flights, which are in advance of the estimated time landing or taking off.

Numerical Implementation

NSGA-II Algorithm

The evolutionary optimization algorithm used to validate numerically the theoretical framework described above is called the Non-dominated Sorting Genetic Algorithm (NSGA-II) [START_REF] Kalyanmoy | Multi-Objective Optimization using Evolutionary Algorithms[END_REF][START_REF] Zitzler | Evolutionary algorithms for multiobjective optimization: Methods and applications[END_REF]. NSGA-II uses a fast non-dominated sorting approach, with complexity O(MN 2 ), an elitism approach, and uses the scaled crowding distance as a secondary diversity criterion, that requires no additional parameter, and figure 2 describes the NSGA-II procedure.

All design variables here are continuous, and the variation operators have been chosen accordingly, as in described by Deb [START_REF] Deb | A fast and elitist multiobjective genetic algorithm: NSGA-II[END_REF] . More precisely, all experiments use the simulated binary crossover operator (SBX) and the polynomial mutation, which can handle directly the box constraints.

The other parameters of the algorithm are the population size, which is equal to the archive size, the probability of crossover, the probability of mutation, the dispersion coefficients, and the number of generations before termination. NSGA-II has been chosen here for its long record of successes, demonstrating its robustness to find a good approximation of the Pareto front. Further work will investigate whether other MOEAs outperform NSGA-II on this problem. A good candidate is SMPSO [START_REF] Kennedy | Particle swarm optimization[END_REF] that has excellent performances on continuous problems.

Results and discussion

Following the air carrier demand, we focus on the test of 20 airports; obtained results are given by figure 3 showing Pareto's front solutions with the corresponding coordinates. We obtain six optimal solutions, offering the possibility to connect 20-targeted airports to the new opening, maintaining an interesting fill rate and minimizing the stopover time over the hub. Following the air carrier demand, we focus on the test of 20 airports; obtained results are given by figure 2 gives Pareto's front solutions with the corresponding coordinates. We obtain six optimal solutions, offering the possibility to connect 20-targeted airports to the new opening, maintaining an interesting fill rate and minimizing the effect of flight delays. Table 1

Conclusions and Future works

Airline managers and decision makers in general, are probably interested to examine the results of this paper, allowing them a dynamic planning of their fleets without need to review the existing flight schedule. Indeed, when we take the decision to open a new destination, the reason may be political or purely commercial; we will be able with this model to insert this new program without losing scheduled connections and without causing major delays during the correspondences. We are all aware that the delay is a boring parameter and causes additional and significant costs for the airlines. Then this solution will serve as a tool to support the decision for an air carrier to amend its program and even think about increasing the number of frequencies on a specific route due to an unforeseen event without constraint to bring changes to the initial program. The Pareto front has been achieved using the algorithm of NSGA-II and the numerical results are based on the real case of the program of the Moroccan airline Royal Air Morocco (RAM) on the hub of Casablanca. Finally, we are all convinced that the aerospace industry is one of the most affected sectors by the hourly disruptions and delays due to several parameters. Airlines put and after developing, a program will face delays that spread quickly to connected flights, which induces considerably important and unexpected costs for airlines [START_REF] Guimarans | Large Neighbourhood Search and Simulation for Disruption Management in the Airline Industry[END_REF]. The primary goal now is to seek an effective way to minimize the impact of disruptions, reducing delays and the number of flights affected. In addition and for the perspective of this paper [9, 10], we will include the criterion of disruptions to introduce the aspect of robustness.

1 𝑃

 1 𝑘 is the origin-and-destination passenger volume recorded in a year between a given airport k served by the RAM company and the new destination, ∑ 𝑃 𝑖 20 𝑖=1

Fig. 1 .

 1 Fig. 1. The Inbound step, pass through the hub to join the new destination, and return for the outbound step.

Figure 1

 1 Figure 1 illustrates the Outbound and Inbound connections, where the flight Inbound step (red color), pass through the hub to join the new destination, and return back again via the hub for the Outbound step (black color). During the optimization process, each random value x generates two intervals: [x -tmax , x -tmin] and [x + t + tmin , x + t + tmax]. An Outbound connection is realized when a given value 𝑎 𝑘𝑖 comes inside Arrival time window [x -

Fig. 2 .

 2 Fig. 2. NSGA-II procedure.

Fig. 3 .

 3 Fig. 3. Pareto optimals of twenty airports case.

  gives details of flight rotation corresponding to each solution from Pareto front. We notice that Airports ranked in following orders 15, 16, 17, and 18 are not connected by the obtained solutions. S4 and S5 are very interesting solutions, connecting 12 (respect 11) of the 20 tested airports. Solution S4 provides 10h35min maximum stopover time on the hub of Casablanca in outbound step, and less than one hour (55 minutes) as minimal stopover time. Departure time is Saturday at 06:30 UTC from Casablanca, the return is the same day at 23:00, and the inbound step is 1 hour 15 minutes stopover time in Casablanca and 08h20min maximum before continuing correspondences back to connected airports. Obtained Departures from Casablanca fits perfectly the fleet programming strategy adopted by Royal Air Maroc Company. Indeed, the company sends daily almost the two-thirds of its fleet on Africa within half an hour before midnight to return in the morning on the Casablanca hub before continuing to Europe, America and golf countries. Moreover, knowing that the best potentials of this example are African countries friends and allies of Morocco, the obtained arrival from the new opening around 23:00 (UTC) is perfectly convenient to the fleet departures to Africa starting around 23:30 (UTC). Similarly, a departure time toward the new opening at 06:30 (UTC) is a perfect knowing that the vast majority of those arrivals lands in Casablanca before 06:00 (UTC).

Table 1 .

 1 (𝑓 1 ; 𝑓 2 ) Solutions with correspondent Rotations