Sabeur Elkosantini

Artificial immunity to control disturbances in public transportation systems: Concepts, mechanisms and a prototype implementation of a knowledge based decision support system

In public transportation, the occurrence of unpredictable disturbances (e.g. accidents, delays, traffic congestion, etc.) may affect the expected execution of preset organization and pre-established timetables of transportation resources (buses, trains, metros, trams, etc.). Affected timetables may become useless, or at least deviate from expected behavior and/or performance. Unfortunately, existing literature suffers limitations with respect to the development of decision support approaches and tools that are able to help decision makers in monitoring and controlling public transportation systems, particularly at the occurrence of disturbances. Existing works are still limited with respect to dealing with several types of disturbances, and suggesting reactive decisions at execution time in such a way to maintain the performance of pre-established timetables and provide users with high quality of services (in terms of punctuality, frequency of programmed shuttles, etc.). In this paper, we show that biological immunity can provide useful principles and mechanisms that are pertinent for the management of disturbances in public transportation systems. We highlight these principles and mechanisms, associate them with application components and fully document them. To show their feasibility, we develop a prototype artificial immune system able to assist decision makers in performing several disturbance management functions, such as detection of disturbances, construction of reaction strategies, supervised learning and memory of previous experiences with disturbances. Through experimental validation, we show that immune concepts and mechanisms can yield to the design and implementation of knowledge based decision support tools that are able to deal with different kinds of disturbances, and to assist decision makers through the disturbance management process.

Toward a new generic behavior model for human centered system simulation

Abstract The simulation of Human Centered Systems (HCS) has attracted increasingly attention in recent research. These systems involve individuals playing key roles, such as workers in manufacturing systems, soldiers in military operations, and investors in stock markets. The complexity simulating such systems is due to the need for modeling individual and group behavior and the integration of psychological and socio-technical aspects that can affect individual and HCS global performance. Although several models have been proposed to simulate such systems, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed and elaborated on in this paper. The current study presents a new model for HCS simulation based on recent social and psychological theories. A model implementation example involving the simulation of a manufacturing system, considered as a HCS, is presented.

A system for the traffic control in signposted junctions

The traffic congestion has become a serious problem in a city. The traffic congestion had important consequences in terms of social, economic and environmental preoccupations. For this reason, several ITS was proposed and their role is to manage the existing highway, public transportation and railroad infrastructure to ease congestion and respond to crises. Developer of such system seek to have a system that insure a safer and more convenient travel for people. In this paper, we propose a system for junctions traffic lights control based on case based reasoning (CBR) approach and fuzzy sets theory. In fact, the CBR is always considered as a cyclic paradigm of Artificial Intelligence and that is used to learning and problem solving based on past experience. The developed system is tested with on a virtual junction and the obtained results are discussed.

An urban traffic controller for signposted road-rail intersections

The main focus of this research is on the development of bimodal traffic controller for signposted road-rail junctions. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The system seek to have a system that insure a safer and more convenient travel for people. In this paper, the proposed controller is based on case based reasoning (CBR) approach. In fact, the CBR is always considered as a cyclic paradigm of Artificial Intelligence and that is used to learning and problem solving based on past experience. The developed system is tested with on a virtual junction and the obtained results are discussed.

A multiobjective simulation optimization approach to define teams of workers in stochastic production systems

In this paper, we address team configuration problems in manufacturing systems, which consist in defining the number of workers to be assigned to a production system, as well as the skills that each worker must have in order to meet several performance measures. This problem is studied in a stochastic production context. A multi-objective evolutionary algorithm is connected to a simulation model to deal with this problem. Two objectives are considered. The first one is the minimization of the expected manpower cost associated to manufacturing team and the second one is the minimization of the expected mean flow time of jobs. Machines redundancy and workers multi-functionality are considered, when defining workers skills, to cope with possible random events such as workers unavailability and bottlenecks. Since the way workers are assigned to work centers strongly impact the results, a recent adaptive assignment heuristic is embedded in the simulation model and its parameters are also optimized. The proposed multi-objective simulation optimization approach is applied to design manufacturing teams, of a job shop production system, using the Nondominated Sorting Genetic Algorithm II (NSGA-II) connected to a simulation model developed using Arena. The set of non dominated solutions is found, so that an additional multi-criteria analysis can be performed.

A survey of simulation platforms for the assessment of public transport control systems

In public transportation, simulation provides capabilities of investigation of complex interactions between the components of the transportation system, including infrastructure, vehicles, passengers and intelligent transportation system. Although many simulation platforms (either commercial or open source) exist to test, validate and evaluate the performance of control systems, the development of platforms that integrate specific requirements of public transport systems still requires much investigation and effort. This paper reviews the main features of traditionally used simulation platforms. A comparative analysis is provided based on different criteria related to infrastructure, vehicles or the ability to implement and test intelligent and distributed control architectures. Several research directions are also pointed out and discussed.

A case based reasoning based multi-agent system for the reactive container stacking in seaport terminals

Abstract With the continuous development of seaports, problems related to the storage of containers in terminals have emerged. Unfortunately, existing systems suffer limitations related to the distributed monitoring and control, real-time stacking strategies efficiency and their ability to handle dangerous containers. In this paper, we suggest a multi-agent architecture based on a set of knowledge models and learning mechanisms for disturbance and reactive decision making management. The suggested system is able to capture, store and reuse knowledge in order to detect disturbances and select the most appropriate container location by using a Case Based Reasoning (CBR) approach. The proposed system takes into account the storage of dangerous containers and combines Multi-Agent Systems (MAS) and case based reasoning to handle different types of containers.

An Immune Multi-agent Based Decision Support System for the Control of Public Transportation Systems

Public Transportation Systems (PTSs) are always subjected to disturbances and need a real time monitoring and control to maintain its performance at acceptable levels. In PTS, several types of disturbances can affect buses such as accidents, delays and traffic jams that can also affect schedules so dramatically that these schedules could become useless. Consequently, it becomes a necessity to develop a Decision Support System (DSS) able to help human regulator in managing PTS efficiently, and to provide users with high quality services, in terms of punctuality, frequency and productivity. In this paper, a reactive and decentralized DSS is developed for the control of PTS based on the biological immune theory. This DSS is an artificial immune system, which presents many interesting capabilities, including identification, learning, memory and distributed parallel processing. Through experimental validation, we show that this exploratory approach seems to be promising.

Toward a Knowledge Based Multi-agent Architecture for the Reactive Container Stacking in Seaport Terminals

In container seaport terminals, one of the most important problems is the one related to the storage of containers. Seaport authorities have invested in means and decision support systems to solve such problems, referred to in this paper as the Container Storage Problem (CSP). Moreover, many unexpected events may occur during the container storage process and, consequently, scheduled position of containers must be modified. Although the number of developed Decision Support System (DSS) for the management of container storage, there is still a need for DSS able to deal different type of disturbance simultaneously. In this paper, we suggest a set of knowledge based DSS for the distributed control of container storage process in an uncertain and disturbed environment. The suggested system is based on a set of knowledge models and learning mechanisms which are integrated in a multi-agent system. Numerical experiments show that knowledge based systems combined with Multi-Agent Systems seems be effective for the real time Container Storage in seaport terminals.

A new framework for the computer modelling and simulation of car driver behavior

In recent years, the simulation of personal car driver behavior has attracted increasing attention in recent research works. Such works are based on models and systems derived from social and psychological studies. The complexity of the simulation of such systems is due to the need for modeling driver behavior and the integration of psychological and physiological factors that can affect driver performance. Although there is only a limited number of models that have been proposed to simulate driver behavior, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed in this paper. This investigation work focuses on the development of a new model for driver behavior simulation based on recent physiological and psychological theories. The model aims to reproduce the driver behavior with respect to some psychological factors. An experimental framework is also presented to build the simulation model. This article concludes by describing some examples of use or application of the suggested model.