Multiobjective evacuation route assignment model based on genetic algorithm

An effective evacuation system can help people escape from building fire. Most evacuation systems consist of an indoor positioning system, a back-end database, and a display device with calculation and display software. However, very few of them can smartly determine which evacuation route is the best decision. If all the locations of the evacuating people can be simultaneously determined, the best evacuation routes can be decided to avoid congestion, and survival rate can increase. The previous radio frequency identification (RFID) based evacuation system focused on detecting the RFID tags using a mobile phone in order to determine the location of the mobile phone user so that an evacuation route can be displayed. However, the system is available for one person regardless of the number of evacuating people or exits. This study is based on the previous RFID based evacuation system investigating the best evacuation routes. The system introduces cloud computing that calculates for positioning the evacuating people and determining the optimum evacuation routes for each of them. The system will be implemented at Tamkang University on Lanyang campus.

With the increasing number or scale of large-scale assembly activities, emergency evacuation has become increasingly important. In order to understand the evacuation behavior of people, this paper uses European distance, simulation and other methods to optimize the research and design of evacuation in emergency. The main contents of this paper are as follows: First, based on the principle and update rules of cellular automata, determine the three key factors of gender, age and emotional intensity, and the evacuation model based on cellular automata, and then use MATLAB software to simulate the change of people flow under different anxiety conditions, and output relevant data and visual images. Finally, the differences of the experimental results caused by the key factors are discussed. Secondly, study the evacuation rate change curve, as well as the key factors and their effects. Based on the average speed and average flow model of the system, simulate and output the corresponding visual images. Thirdly, in order to study and determine the best simulated evacuation route and the dynamic process of personnel evacuation in the case of different door widths, based on the evacuation route selection model with the shortest time to reach the two doors, the best pedestrian evacuation route is selected according to the shortest time rule. Fourth, in order to study the impact of reduced visibility in the hall on the whole process. In this paper, the regional discretization method is used to establish the perception range model under visibility and analyze the factor changes in the whole process.

Exact and heuristic solution algorithms for efficient emergency evacuation in areas with vulnerable populations

This paper proposes an implementation of linear programming in tsunami horizontal evacuation routes decision making. A tsunami is a huge wave that travels with high speed towards a coastal area. Padang is the capital city of West Sumatera Province that is located in the coastal area as well as have a history of the tsunami disaster. The main problem is only a few minutes of the interval for the tsunami waves arrive at the coast after tsunami hazard warning. In the evacuation concept, the evacuees from the high-risk zone are moving to the shelter through a safe evacuation route. This research aims to determine the optimal evacuation routes with the constraints of a few minutes of the interval of evacuation time, the capacity of each shelter, and the number of evacuees. The evacuation time is an objective function that depends on the number of population, people density, road width, distance from the high-risk zone to the shelter, and velocity. The numerical simulation is done using linear programming with Matlab. The results show that all evacuees in each high-risk region are proportionally evacuated to available shelter areas within allocated evacuation time. The results of this research can be used for supporting tsunami evacuation plans and disaster resilience management by the authorities in Padang.

Evacuation dynamics of pedestrians passing through a low-boundary exit in the context of sound signal

ABSTRACTHuman factors methods, as a systems discipline, can be applied across various areas of working systems. Risk assessment methods are particularly useful for identifying risks that may impact the performance of overall working systems, groups, and individuals. The emergency evacuation process in hospitals involves multiple risks that can significantly affect its performance. This study applied a systems thinking‐based risk assessment method to identify risks associated with hospital emergency evacuations. The Networked Hazard Analysis and Risk Management System (Net‐HARMS) method was utilized to identify all credible risks in the hospital evacuation process during emergencies that could degrade optimal performance. Some of the key risks identified in the hospital emergency evacuation process included delays in assessing risks associated with evacuation procedures, failures or delays in forming and appointing an emergency evacuation and command team, and inadequate intra‐ and interorganizational coordination. Additionally, emerging risks were identified, such as delays in the evacuation process due to staff lacking sufficient information about the evacuation and incident command team members, as well as delays in receiving assistance from external organizations like the fire department and Red Crescent due to inadequate interorganizational coordination processes. These risks arose from the interactions between activities. The study concludes that the Net‐HARMS method is effective in forecasting systemic and emergent risks in the hospital evacuation process, as well as identifying risks associated with specific activities and emergent risks in this critical process.

Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building’s electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins’ body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins’ positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.

Routes planning is one of the most important aspects in emergency evacuation planning. A multi-objective emergency evacuation model using ant colony optimization was proposed for routes planning in complex multi-exit evacuation environment. The two objectives of the model are to minimize the total evacuation time of all evacuees and to minimize the total path crowding degree respectively. A multi-objective ant colony optimization algorithm was presented to solve the proposed model. The results of the study show that the proposed model can make rapid, efficient and reasonable plans for complex evacuation routing problems.

An emergency evacuation route is an important component of emergency rescue of for nuclear accidents. A reasonable evacuation route can reduce evacuation times and protect people’s life. The evacuation route of the nuclear power plant is abstracted into a network diagram and a mathematical model of evacuation optimization route based on the graph theory and the parity of spot diagram method in this paper. Road traffic capacity and other external factors that may affect emergency evacuation are considered in the time weight factor for each road. Finally, an example is given to verify the feasibility of the model.

Semantic computing addresses the transformation of data, both structured and unstructured into information that is useful in application domains. One domain where semantic computing would be extremely effective is evacuation route planning, an area of critical importance in disaster emergency management and homeland defense preparation. Evacuation route planning, which identifies paths in a given transportation network to minimize the time needed to move vulnerable populations to safe destinations, is computationally challenging because the number of evacuees often far exceeds the capacity, i.e. the number of people that can move along the road segments in a unit time. A semantic computing framework would help further the design and development of effective tools in this domain, by providing a better understanding of the underlying data and its interactions with various design techniques. Traditional Linear Programming(LP) based methods using time expanded networks can take hours to days of computation for metropolitan sized problems. In this paper, we propose a new approach, namely a capacity constrained routing planner for evacuation route planning which models capacity as a time series and generalizes shortest path algorithms to incorporate capacity constraints. We describe the building blocks and discuss the implementation of the system. Analytical and experimental evaluations that compare the performance of the proposed system with existing route planners show that the capacity constrained route planner produces solutions that are comparable to those produced by LP based algorithms while significantly reducing the computational cost.

Fires in under-construction threaten worker safety and have a lot of negative impacts on many aspects of construction that were reported the past few years. Due to the dynamic nature of construction, the fire safety plan in construction needed to be reviewed and updated according to construction progress. The conventional�approach or manual process to develop the fire safety plan for a construction project can be a time-consuming and enormous labor-intensive task. Furthermore, some of the emergency signs are unclear due to the nature of construction and some are not showing the correct evacuation route because of the structural design change based on the construction progress. BIM technology and the Game engine can present the object in 3D and provide programming for rule-based modeling. Therefore, this paper proposes a framework to develop the fire safety plan (such as fire extinguisher installation plan (FEIP) and evacuation route plan) and mobile application to assist the firefighters and people who are involved in construction during the rescue and evacuation�process by considering the dynamic nature of construction. This research develops the prototype that enables the users to define the parameters for the fire extinguisher installation plan and evaluation route such as finished and unfinished building objects (walls, stairs, and slabs), exists, the locations of fire extinguishers (FE), and flammable substances (FS). This prototype provides the appropriate locations of fire extinguishers, the unsafe area, and the fastest evacuation route for�a fire safety plan and the mobile application that�can show the fastest evacuation route, report safe arrival and�inform the location where the evacuee�is stuck. To validate�the proposed approach, the case study using the sample BIM model and real construction BIM model is�executed. Moreover, 4 safety engineers or officers�and 11 construction personnel participate in evaluating the proposed system.

In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM) formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.

Generation rights trading is one of the market mechanisms that can promote utilization of renewable energy. This paper proposes a new cross regional generation rights trading model with two objectives: maximization of social welfare and minimization of CO2 emission. Cross regional transmission constraint is modeled in terms of energy approximately by applying the transmission limitation to the historical renewable generation in the sending region. The genetic algorithm (GA) is applied to solve the proposed model. Case study using practical system data is performed to verify the effectiveness of the model. Study results indicate that compared with the single-objective models, the multi-objective model is more instructive to the actual power generation rights trading.

Emergency evacuations arising from hurricane disasters in the Gulf Coast region have aroused increasing attention of transportation agencies. These emergency evacuations caused significant congestions in the disastrous area, along the evacuation routes, and at the evacuation destinations. Although most studies focus on traffic problems near the disastrous area, this study investigates effective strategies of managing traffic at the evacuation destination. This study adopts the dynamic traffic assignment based mesoscopic traffic-network planning and simulation model DYNASMART-P and applies the model to a highway network on the route of the evacuation traffic. The research investigates the effectiveness of traffic control and management strategies including intelligent transportation systems (ITS) and contra-flow operations based on an assumed scenario in which evacuation traffic from southeastern Louisiana pours into the Greater Jackson Metropolitan area of Mississippi by interstate highway I-55 NB due to an approaching hurricane disaster. The study aims to help transportation agencies in improving emergency preparedness for their highway networks to receive possible emergency evacuation traffic.

Modeling route choice behavior of evacuees in highly urbanized area: A case study of Bagong Silangan, Quezon City, Philippines