Swarm intelligence in evacuation problems: A review

In this paper authors introduce swarm intelligence’s algorithms (ACO and PSO) to determine the optimum path during an evacuation process. Different PSO algorithms are compared when applied to an evacuation process and results reveal important aspects, as following detaile

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Modelling human network behaviour using simulation and optimization tools: the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models.Peer Reviewe

An Aircraft Evacuation Simulation Baseline Using DES for Passenger Path Planning

This paper introduced a Discrete Event Simulation (DES) model that simulates passengers’ evacuation paths and decision-making processes during aircraft certification. The model was built using ARENA® 14, which is a DES simulation tool. This model used A380 cabin configuration with capacity of 538 passengers. Each passenger was considered as an independent human being with variations in walking speed, decision-making processes, and evacuation path. This model generated total evacuation time and presented total congestion conditions of each gate. Federal Regulation has suggested that all passengers in the airplane should finish the evacuation within 90 seconds. The model was validated with the A380 certification evacuation, which was 78.2 sec. This model was tested and statistically validated for aircraft evacuation. However, the validation model has limitations in passengers’ freedom of choosing a gate. To advance the simulation, an experiment was conducted based on the modification of the validation model to simulate the effect on total evacuation time of passengers switching gates while waiting to exit. At the end of this paper, future study directions were suggested to innovate the baseline by adding human interactions and advanced methods in dynamic simulation technology

Path Planning for Robot and Pedestrian Simulations

The thesis is divided into two parts. The first part presents a new proposed method for solving the path planning problem to find an optimal collision-free path between the starting and the goal points in a static environment. Initially, the grid model of the robot's working environment is constructed. Next, each grid cell's potential value in the working environment is calculated based on the proposed potential function. This function guides the robot to move toward the desired goal location, it has the lowest value at the goal location, and the value increase as the robot moves further away. Next, a new method, called Boundary Node Method (BNM), is proposed to find the initial feasible path. In this method, the robot is simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves in the working environment toward the goal point with eight-boundary nodes based on the boundary nodes' characteristics. In the BNM method, the initial feasible path is generated from the sequence of the waypoints that the robot has to traverse as it moves toward the goal point without colliding with obstacles. The BNM method can generate the path safely and efficiently. However, the path is not optimal in terms of the total path length. An additional method, called Path Enhancement Method (PEM), is proposed to construct an optimal or near-optimal collision-free path. The generated path obtained by BNM and PEM may contain sharp turns. Therefore, the cubic spline interpolation is used to create a continuous smooth path that connects the starting point to the goal point. The performance of the proposed method is compared with the other path planning methods in terms of path length and computational time. Moreover, the multi-goal path planning problem is investigated to find the shortest collision-free path connecting a given set of goal points in the robot working environment. Furthermore, to verify the performance of the proposed method, several experimental tests have been performed on the e-puck robot with different obstacle configurations and various positions of goal points. The experimental results showed that the proposed method could construct the shortest collision-free path and direct the real physical robot to the final destination point. At the end of the first part of the thesis, we investigate the multi-goal path planning problem for the multi-robot system such that several robots reach each goal. In the second part of this thesis, we proposed a new method for simulating pedestrian crowd movement in a virtual environment. The first part of this thesis concerning the generation of the shortest collision-free path is used. In this method, we assumed that the crowd consists of multiple groups with a different number and various types of pedestrians. In this scenario, each group's intention is different for visiting several goal points with varying sequences of the visit. The proposed method uses the multi-group microscopic model to generate a real-time trajectory for each pedestrian navigating in the pedestrianized area of the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian' behaviours. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes, such as position, velocity, etc. Moreover, pedestrians optimize their path independently toward the desired goal points while avoiding obstacles and other pedestrians in the scene. At the end of this part of the thesis, a statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. The proposed method implemented for several simulation scenarios under a variety of conditions for a wide range of different parameters. The results showed that the proposed method is capable of describing pedestrian' behaviours in the virtual environment

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Towards a resilient networked service system

Large service systems today are of highly network structures. In this thesis, these large service systems are called networked service systems. The network nature of these systems has no doubt brought mass customized services but has also created challenges in the management of their safety. The safety of service systems is an important issue due to their critical influences on the functioning of society. Traditional safety engineering methods focus on maintaining service systems in a safe state, in particular aiming to maintain systems to be reliable and robust. However, resilience cannot be absent from safety out of many recent disasters that occur in society. The goal of this thesis is to improve the resilience of networked service systems. Four major works have been performed to achieve this goal. First, a unified definition of service systems was proposed and its relationship to other system concepts was unfolded. Upon the new definition, a domain model of service systems was established by a FCBPSS framework, followed by developing a computational model. Second, a definition of resilience for service systems was proposed, based on which the relationship among three safety properties (i.e., reliability, robustness and resilience) was clarified, followed by developing a framework for resilience analysis. Third, a methodology of resilience measurement for service systems was proposed by four measurement axioms along with corresponding mathematical models. The methodology focused on the potential ability of a service system to create optimal rebalancing solutions. Two typical service systems, transportation system and enterprise information system, were employed to validate the methodology. Fourth, a methodology of enhancing resilience for service systems was proposed by integrating three types of reconfigurations of systems, namely design, planning and management, along with the corresponding mathematical model. This methodology was validated by an example of transportation system. Several conclusions can be drawn from the work above: (1) a service system has a unique characteristic that it meets humans' demand directly, and its safety relies on the balance between the supplies and demands; (2) different from reliability and robustness, the resilience of a service system focuses on the rebalancing ability from imbalanced situations; (3) it makes sense to measure the resilience of a service system only for a particular imbalanced situation and based on evaluation of rebalancing solutions; and (4) integration of design, planning and management is an effective approach for improvement of the resilience for a service system. The contributions of this thesis can be summarized. Scientifically, this thesis work has improved our understanding of service systems and their resilience property; furthermore, this work has advanced the state of knowledge of safety science in particular having successfully responded to two questions: is a service system safe and how to make a service system safer? Technologically or methodologically, the work has advanced the knowledge for modeling and optimization of networked service systems in particular with multiple layer models along with the algorithms for integrated decision making on design, planning, and management

Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review

In the past decade the humanitarian supply chain (HSC) has attracted the attention of researchers due to the increasing frequency of disasters. The uncertainty in time, location, and severity of disaster during predisaster phase and poor conditions of available infrastructure during postdisaster phase make HSC operations difficult to handle. In order to overcome the difficulties during these phases, we need to assure that HSC operations are designed in an efficient manner to minimize human and economic losses. In the recent times, several mathematical optimization techniques and algorithms have been developed to increase the efficiency of HSC operations. These techniques and algorithms developed for the field of HSC motivate the need of a systematic literature review. Owing to the importance of mathematical modelling techniques, this paper presents the review of the mathematical contributions made in the last decade in the field of HSC. A systematic literature review methodology is used for this paper due to its transparent procedure. There are two objectives of this study: the first one is to conduct an up-to-date survey of mathematical models developed in HSC area and the second one is to highlight the potential research areas which require attention of the researchers

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner