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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. [ABSTRACT FROM AUTHOR]

In public bus transportation systems, several types of disturbances, such as accidents and congestion, may affect preestablished timetables and visiting hours at stations. Disturbances result in detrimental consequences, degraded performance, and poor quality of service in terms of extended delays and waiting times, punctuality, frequency, and efficiency of shuttles. Despite numerous research on the monitoring and control of public transportation systems by means of buses, distributed monitoring and control architectures that confer intelligence and decision‐making autonomy to buses to react to disturbances are still missing. This article addresses this gap by designing and developing a distributed architecture to monitor and control public transportation buses using multiagent systems. The design relies on biological immunity as a methodological framework that guides the development of knowledge models and decision‐making mechanisms. Knowledge models structure knowledge about disturbances and control decisions, whereas decision‐making mechanisms implement control and reaction strategies. Through experimental validation based on simulation, we show that the suggested immune multiagent distributed control architecture is not only able to maintain performance (average delay/earliness, average total time in simulated network) at acceptable levels but also to improve quality of service in terms of number of served passengers and stations by at least 15% in case of disturbances. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we suggest a Case Based heuristic for the online container stacking management system in seaport terminals. The main objectives of the system are to determine the exact position of each import container in the storage area and to control container allocation and react to unexpected events and disturbances in an intelligent, self-organizing and real-time manner. First, we propose learning mechanisms and knowledge models for a better management of knowledge related to disturbances and container environment. This system takes into account different types of containers especially the storage of dangerous containers. For assessment of the suggested system, real data are collected from King Abdul Aziz Dammam seaport terminal (Saudi Arabia). The performance of the developed heuristic is assessed with different scenarios and compared to three other stacking strategies studied in the scientific literature. The obtained results are promising and show that the developed CBR (Case Based Reasoning) based heuristic can be efficient or similar problems, i.e. online container staking. [ABSTRACT FROM AUTHOR]

Urban traffic is subject to disturbances that cause long queues and extended waiting times at signalized intersections. Although Multi-Agent Systems (MAS) were considered to control traffic at signalized intersections in a distributed way, their generic conceptual framework and lack of built-in adaptation mechanisms prevent them from achieving specific disturbance management capabilities. The traffic signal control problem is still a challenging open-ended problem for which learning and adaptation mechanisms need to be developed to deal with disturbances in an intelligent way. In this article, we rely on concepts and mechanisms inspired by biological immunity to design a distributed, intelligent and adaptive traffic signal control system. We suggest a heterarchical multi-agent architecture, where each agent represents a traffic signal controller assigned to a signalized intersection. Each agent communicates and coordinates with neighboring agents, and achieves learning and adaptation to disturbances based on an artificial immune network. The suggested Immune Network Algorithm based Multi-Agent System (INAMAS) provides intelligent mechanisms that capture disturbance-related knowledge explicitly and take advantage of previous successes and failures in dealing with disturbances through an adaptation of the reinforcement principle. To demonstrate the efficiency of the suggested control architecture, we assess its performance against two control strategies from literature, namely fixed-time control and a distributed adaptation of the Longest Queue First – Maximal Weight Matching (LQF-MWM) algorithm. Agents are developed using SPADE platform and used to control a network of signalized intersections simulated with VISSIM, a state-of-the-art traffic simulation software. The results show that INAMAS is able to handle different traffic scenarios with competitive performance (in terms of vehicle queue lengths and waiting times), and that it is particularly more successful than the other controllers in dealing with extreme situations involving blocked approaches and high traffic volumes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is extensive literature on the reconfiguration of manufacturing systems; however, there are only a few decision support approaches that allow full advantage to be taken of the flexibilities introduced by this paradigm. Existing approaches do not consider expert knowledge to deal with new occurrences of similar, previously encountered disturbances. Most approaches are preventive and off-line planning and scheduling approaches, thus missing updated accurate data about plant activities that may trigger reconfiguration decisions and make such decisions worth consideration. In this article, we design a decision support system to suggest candidate configurations and select a suitable configuration considering a knowledge-based multi-criteria decision making approach. Expert knowledge is captured using an ontology, which is used both to monitor the manufacturing system and to make configuration recommendations. A multi-criteria decision-making approach based on TOPSIS relies on the recommended configurations to select a suitable configuration. An industrial case study shows how the suggested approach can be used to reconfigure the system at the execution stage to cope with disturbances in a reactive manner. [ABSTRACT FROM AUTHOR]