This paper is an attempt to provide an accreditation training process model by the criteria established by the National Agency for Evaluation and Quality Assurance of Higher Education and Training. The aim is to minimize rejection returns or revisions of the training record. The main feature of our contribution is the use of multi-criteria decision making (MCDM) approaches for calculating the suitability of proposed courses. Therefore, our contribution will is concretized by the analysis of various decision support multi-criteria methods, the modeling of the general accreditation process of university courses, the development of a risk management matrix concerning the launch of new courses and the application of TOPSIS (technique for order of preference by similarity to ideal solution) on a sample of courses according to internal and external criteria, collected during interviews in Moroccan universities. Result analysis shows that the proposed model allows a better prioritization of training and thus avoids the abrupt closure of courses because of lack of material or human resources.
<p>Model-Driven Engineering (MDE) plays a very important role in improving the development of complex systems. It focuses more on modeling than on classical programming. In this sense, model transformation is at the heart of the Model Driven Architecture (MDA) approach which advocates the use of models throughout the software development cycle on two levels. The first being the transformation of the Computing Independent Model (CIM) into the Platform Independent Model (PIM) and the second concerning the transformation of PIM into Platform Specific Model (PSM).</p><p>The latter has been dealt with in the majority of research works while the transformation from CIM to PIM which represents the highest level is rarely discussed in research topics. It is for this reason and in the spirit of improving the process of transforming the CIM model into PIM according to the MDA approach, that we have developed this research work in order to propose a new method and new transformation rules for optimization of the business process "COVID-19 patient management". Our contribution consists of the semi-automatic transformation of the CIM model presented by the BPMN (Business Process Model and Notation) source model into a PIM target model presented by a class diagram by using a set of transformation and correspondence rules that we performed manually using the language ATL (Atlas Transformation Language). This automatic transformation of the two source and target models is provided by the Eclipse Modeling Framework (EMF) which executes the transformation rules described manually in ATL and generates the PIM target model as an XMI (XML Metadata Interchange) file representing the target class diagram.</p>
Information system is a guarantee of the universities' ability to anticipate the essential functions to their development and durability. The alignment of information system, one of the pillars of IT governance, has become a necessity. In this paper, we consider the problem of strategic alignment model implementation in Moroccan universities. Literature revealed that few studies have examined strategic alignment in the public sector, particularly in higher education institutions. Hence we opted for an exploratory approach that aims to better understanding the strategic alignment and to evaluate the degree of its use within Moroccan universities. The data gained primarily through interviews with top managers and IT managers reveal that the alignment is not formalized and that it would be appropriate to implement an alignment model. It is found that the implementation of our proposed model can help managers to maximize returns of IT investment and to increase their efficiency.
Performance optimization has become a necessity for the survival of enterprises as emerging technologies continue to impact them. To achieve this goal, decision making, a complex process which depends on big data and human issues, must be effective. As enterprises are being subjected to a multi-faceted pressure, they must ensure the optimization of their performance. This article investigates an intelligent decision support system (IDSS) based on multi agent systems (MAS). Our contribution consists in developing an intelligent model with an IDSS MAS approach that can detect and evaluate changes in both the external environment and the enterprise itself. This model is an adequate management tool for optimal and sustainable performance and offers real-time analytical, prospecting and optimization methods.
Abstract-In this paper, we consider a complex garbage collection problem, where the residents of a particular area dispose of recyclable garbage, which is collected and managed using a fleet of trucks with different weight capacities and volume. This tour is characterized by a set of constraints such as the maximum tour duration (in term of distance and the timing) consumed to collect wastes from several locations. This problem is modeled as a garbage collection vehicle routing problem, which aims to minimize the cost of traveling routes (minimizing the distance traveled) by finding optimal routes for vehicles such that all waste bins are emptied and the waste is driven towards the disposal locations. We propose a distributed technique based on the Ant Colony system Algorithm to find optimal routes that help vehicles to visit all the wastes bins using interactive agents consumed based on the behavior of real ants. The designed solution will try to create a set of layers to control and manage the waste collection, each layer will be handled by an intelligent agent which is characterized by a specific behavior, in this architecture a set of behaviors have been designed to optimizing routes and control the real time capacity of vehicles. Finally, manage the traffic messages between the different agents to select the best solutions that will be assigned to each vehicle. The developed solution performs well compared to the traditional solution on small cases.
Multi-agent systems MASs have been widely used to interoperate hospital information systems (HISs). The use of MASs for HISs interoperability has become a central solution, especially in the field of emergency medicine. In emergencies, the notion of delay is relative, because responders only have a few minutes to react. This emergency response time has an important role in the event that an accident occurs on the road. Existing procedures for the emergency ambulance (EA) dispatch strategy are based on manual dispatch. In this work, we are introducing a distributed emergency ambulance (DEA) routing system to control emergency latency time, which includes driving route planning to guide emergency vehicles and the allocation of distributed emergency resources (emergency ambulances and hospitals) to reduce the EA response time caused by traffic or the wrong human decision to transport ambulance to the accident site. The allocation of resources (hospitals) is ensured through a recommendation system based on the interoperability of several interconnected HISs using a multi-agent system. The proposed solution takes into consideration dynamic traffic flow information during the day to build dynamic paths for EA. The improved method is based on a distributed architecture to calculate and find the optimal pathway for a set of emergency vehicles based on ACO ant colony optimization techniques. The results of the simulation show that the proposed method can decrease the total travel time of the ambulance to reach the accident position compared to conventional methods that use lights and sirens to warn other vehicles to free up the road for the ambulance or use a traditional approach based on the vision/reflection of the driver to choose in a random way the paths to take. Based on such a solution, ambulance staff will be able to save lives by optimizing the total journey with the minimum travel.
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