scholarly journals Dynamic real-time capacity constrained routing algorithm for evacuation planning problem

Author(s):  
Jawad Abusalama ◽  
Sazalinsyah Razali ◽  
Yun-Huoy Choo ◽  
Lina Momani ◽  
Abdelrahman Alkharabsheh

<span>Usually, disasters occurred over a relatively short time in anytime and anywhere. Most occupancies haven’t absolute knowledge about the prevention or safety consciousness to deal with disasters. During disaster occurred, evacuation processes are conducted to save people life, and if there is no appropriate evacuation plan, the situation will become worse. Thus, finding optimal planning to evacuate the occupancy people is critical in many cases i.e. emergency evacuation. In this paper, a Dynamic Real-Time Capacity Constrained Routing (DRTCCR) Algorithm has been proposed and analyzed. Such algorithm will investigate the capacity constraints of the evacuation network in real-time by modelling the capacities at the time of series to improve current solutions of the evacuation planning problem.  Such algorithm will produce an optimal solution for evacuation planning problem. Performance evaluation on many network models illustrates that the DRTCCR algorithm improves the previous evacuation planning by reducing the evacuation time as well as the computational cost. In addition, DRTCCR algorithm has the ability to recalculate and find out the optimal path dynamically in real-time irrespective the number of trapped people as well as the transportation network size. Analytical experiments have been done and illustrate the efficiency of the proposed algorithm.</span>

Author(s):  
Yunjun Xu ◽  
Gareth Basset

Coherent phantom track generation through controlling a group of electronic combat air vehicles is currently an area of great interest to the defense agency for the purpose of deceiving a radar network. However, generating an optimal or even feasible coherent phantom trajectory in real-time is challenging due to the high dimensionality of the problem and severe geometric, as well as state, control, and control rate constraints. In this paper, the bio-inspired virtual motion camouflage based methodology, augmented with the derived early termination condition, is investigated to solve this constrained collaborative trajectory planning problem in two approaches: centralized (one optimization loop) and decentralized (two optimization loops). Specifically, in the decentralized approach, the first loop finds feasible phantom tracks based on the early termination condition and the equality and inequality constraints of the phantom track. The second loop uses the virtual motion camouflage method to solve for the optimal electronic combat air vehicle trajectories based on the feasible phantom tracks obtained in the first loop. Necessary conditions are proposed for both approaches so that the initial and final velocities of the phantom and electronic combat air vehicles are coherent. It is shown that the decentralized approach can solve the problem much faster than the centralized one, and when the decentralized approach is applied, the computational cost remains roughly the same for the cases when the number of nodes and/or the number of electronic combat air vehicles increases. It is concluded that the virtual motion camouflage based decentralized approach has promising potential for usage in real-time implementation.


Author(s):  
Vivek Srivastava ◽  
Ravi Shankar Pandey

Background & Objective: Software-Defined Networks (SDN) decouple the responsibility of data plane, control plane and aggregates responsibilities at the controller. The controller manages all the requests generated from distributed switches to get the optimal path for sending data from source to destination using load balancing algorithms. The guarantee of packet reachability is a major challenge in real time scenario of a SDN which depends on components of network infrastructure as switches, a central controller, channel capacity and server load. The success of this aggregation and packet reachability demand is a high Quality of Service (QoS) requirement in terms of throughput, delay and packet loss due to high traffic volume and network size. This QoS has two perspectives one is required other is a computation of real QoS value. Methods: In this paper, we have presented the QoS based formal model of SDN to compute and to investigate the role of the real QoS value. This formal model includes QoS on the basis of packet movement hop by hop which is a real-time QoS. The hop by hop packet movement reliability has been computed using channel capacity and server load which is an abstraction of throughput, delay, and packet loss. The effect of channel capacity and server load can be varying using different values of the weight factor. We have also considered an equal role of channel capacity and server load to compute reliability. This QoS helps to the controller to match with required QoS to decide the better path. Conclusion: Our results finds the reliable path based on channel capacity and server load of the network. Also, results showed that the reliability of the network and controller which are based on the reliability of the packet delivery between two nodes.


Transport ◽  
2011 ◽  
Vol 26 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Arash Jahangiri ◽  
Shahriar Afandizadeh ◽  
Navid Kalantari

In recent years, natural and man-made disasters have increased and consequently put people's lives in danger more than before. Some of the crises are predictable. In these cases, there is a limited time for effective respond minimizing fatalities when people should be evacuated in a short time. Therefore, a transportation network plays a key role in evacuation. Hence, the outbound paths of urban networks are not sufficient from the viewpoint of number and capacity to encounter a huge amount of people; furthermore, it is costly to construct new routes or increase the capacity of the existing ones. Thus, a better utilization of the existing infrastructure should be considered. The article presents a model that determines optimum signal timing and increases the outbound capacity of the network. Moreover, in regard for the magnitude of the problem, an optimal solution could not be reached employing ordinary methods; therefore, the simulated annealing algorithm which is a meta-heuristic technique is used. The results of this study demonstrated that the objective function of the problem was greatly improved. Santrauka Pastaruoju metu gamtos ir žmonijos sukeltų nelaimių padaugėjo, todėl susiduriama su daugiau pavojų nei anksčiau. Kai kurių krizių ir nelaimių negalima numatyti. Tokiais atvejais veiksmingas reagavimo laikas yra ribotas, bet padeda sumažinti mirties atvejų, greitai evakuojant žmones. Šiuo atveju ypač svarbus yra transporto tinklas ir transportavimas. Iš miesto į užmiestį vedančios gatvės nėra pakankamai efektyvios, norint pervežti didelį žmonių skaičių. Be to, brangu pradėti rengti naujus maršrutus arba didinti jau esančių gatvių pralaidumą. Todėl turėtų būti apsvarstytas geresnis esančios transporto infrastruktūros panaudojimas. Straipsnyje nagrinėjamas modelis, nulemiantis optimalų šviesoforo signalo laiko nustatymą ir padidinantis išvykstančiųjų skaičių. Atsižvelgiant į problemos svarbą, optimalus sprendimas negali būti priimtas, naudojant įprastus metodus. Todėl naudojamas metaeuristinis metodas – modeliuojamasis atkaitinimo algoritmas (simulated annealing algorithm). Šio darbo rezultatai rodo, kad problemos tikslo funkcija labai pagerėjo. Резюме В настоящее время в мире увеличилось число стихийных бедствий и бедствий, связанных с неосторожной деятельностью людей. Значительную часть бедствий предсказать невозможно. В таких случаях эффективное время реагирования ограничено, что в свою очередь помогает уменьшить количество смертельных исходов и несчастных случаев при эвакуации населения. В этом случае транспортная сеть и сам процесс транспортирования играют решающую роль. Улицы, ведущие из города, становятся неэффективными из-за огромного количества людей. Кроме того, подготовка новых маршрутов или увеличение пропускной способности имеющихся улиц являются дорогостоящими мероприятиями. Поэтому следует проанализировать возможности более эффективного использования уже имеющейся транспортной инфраструктуры. В статье представлена модель, позволяющая определить выбор и установить оптимальное время сигнала светофора во время аварийной эвакуации с целью увеличить число эвакуируемых. Учитывая важность проблемы, оптимальное решение не может быть принято с использованием обычных методов. Поэтому используется метаэвристический метод – алгоритм имитации отжига (simulated annealing algorithm). Результаты, представленные в исследовании, показали, что целевая функция исследуемой проблемы значительно улучшилась


Author(s):  
Adel W. Sadek ◽  
Charles Mark

Because major capacity-expansion projects are very unlikely in the coming years, transportation planners need to view the existing infrastructure as fixed and to start thinking about how much development the current system can sustain. This line of thinking, which involves deriving land use limits from infrastructure capacity, requires solving the inverse of the typical transportation planning problem. Modular artificial neural networks (ANNs) were developed for solving the inverse transportation planning problem. ANNs were designed to predict zonal trip ends, given the traffic volumes on the links of the transportation network. Computational experiments were performed to study the effect on ANN accuracy of three factors: transportation network size, variability in training data, and ANN topology. ANNs were shown to be quite capable of capturing the relationship between link volumes and zonal trip ends for both small and medium-sized transportation networks and for degrees of variability in the training data. Modular ANNs with one or two hidden layers appeared to outperform other ANN topologies.


2007 ◽  
Vol 01 (02) ◽  
pp. 249-303 ◽  
Author(s):  
QINGSONG LU ◽  
BETSY GEORGE ◽  
SHASHI SHEKHAR

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.


2017 ◽  
Vol 14 (4) ◽  
pp. 297-306 ◽  
Author(s):  
B.B.V.L. Deepak ◽  
M.V.A. Raju Bahubalendruni

Purpose The purpose of this paper is to study the path-planning problem of an unmanned ground vehicle (UGV) in a predefined, structured environment. Design/methodology/approach In this investigation, the environment chosen was the roadmap of the National Institute of Technology, Rourkela, obtained from Google maps as reference. An UGV is developed and programmed so as to move autonomously from an indicated source location to the defined destination in the given map following the most optimal path. Findings An algorithm based on linear search is implemented to the autonomous robot to generate shortest paths in the environment. The developed algorithm is verified with the simulations as well as in experimental environments. Originality/value Unlike the past methodologies, the current investigation deals with the global path-planning strategy as the line following mechanism. Moreover, the proposed technique has been implemented in a real-time environment.


2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wu Ni ◽  
Wenbo Li ◽  
Hailei Wang ◽  
Chengxin Xiong ◽  
Dan Guo

This paper presents a heuristic contraflow-based reconfiguration evacuation algorithm, which is named Capacity-Constrained Contraflow Adaption (CC-Adap). First, it effectively calculates optimal candidate routes for evacuation. Second, an evaluation method is proposed for estimating these candidate routes. Third, CC-Adap utilizes a contraflow-based method to reconfigure the evacuation routes to improve capacity constraints. Fourth, traffic conditions are updated in real time. Fifth, CC-Adap reuses historical evacuation routes to reduce the computational cost and accelerate the evacuation process. Experimental results show that CC-Adap generates high-performing evacuation strategies and can be used to tackle large-scale evacuation planning.


Author(s):  
Evangelia Baou ◽  
Vasilis P. Koutras ◽  
Vasileios Zeimpekis ◽  
Ioannis Minis

PurposeThe purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are evacuated from multiple pick-up locations and transported using a heterogeneous fleet of vehicles.Design/methodology/approachThe problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies.FindingsEvacuating people with disabilities has a significant impact on total evacuation time, due to increased loading/unloading times. Additionally, increasing the number of large capacity vehicles adapted to transport individuals with disabilities benefits total evacuation time.Research limitations/implicationsThe mathematical model is of high complexity and it is not possible to obtain exact solutions in reasonable computational times. The efficiency of the heuristic has not been analyzed with respect to optimality.Practical implicationsSolving the problem by a heuristic provides a fast solution, a requirement in emergency evacuation cases, especially when the state of the theater of the emergency changes dynamically. The parametric analysis of the heuristic provides valuable insights in improving an emergency evacuation system.Social implicationsEfficient population evacuation studied in this work may save lives. This is especially critical for disabled evacuees, the evacuation of whom requires longer operational times.Originality/valueThe authors consider a population that comprises able and disabled individuals, the latter with varying degrees of disability. The authors also consider a heterogeneous fleet of vehicles, which perform multiple trips during the evacuation process.


Author(s):  
A. Gasparetto ◽  
R. Vidoni ◽  
E. Saccavini ◽  
D. Pillan

In this work, a robotic painting task is addressed in order to automate and improve the efficiency of the process. Usually, path planning in robotic painting is done through self learning programming. Recently, different automated and semi-automated systems have been developed in order to avoid this procedure by using a CAD-drawing to create a CAD-guided trajectory for the paint gun, or by acquiring and recognizing the overall shape of the object to be painted within a library of prestored shapes with associated pre-defined paths. However, a general solution is still lacking, which enables one to overcome the need for a CAD-drawing and to deal with any kind of shapes. In this paper, graph theory and operative research techniques are applied to provide a general and optimal solution of the path planning problem for painting robots. The object to be painted is partitioned into primitives that can be represented by a graph. The Chinese Postman algorithm is then run on the graph in order to obtain a minimum length path covering all the arcs (Eulerian path). However, this path is not always optimal with respect to the constraints imposed by the painting process, hence dedicated algorithms have been developed in order to generate the optimal path in such cases. Based on the optimal path, the robot trajectories are planned by imposing a constant velocity motion of the spray gun, in order to ensure a uniform distribution of the paint over the object surface. The proposed system for optimal path planning has been implemented in a Matlab environment and extensively tested with excellent results in terms of time, costs and usability.


2017 ◽  
Vol 36 (4) ◽  
pp. 403-413 ◽  
Author(s):  
Wuchen Li ◽  
Shui-Nee Chow ◽  
Magnus Egerstedt ◽  
Jun Lu ◽  
Haomin Zho

We propose a novel algorithm to find the global optimal path in 2D environments with moving obstacles, where the optimality is understood relative to a general convex continuous running cost. By leveraging the geometric structures of optimal solutions and using gradient flows, we convert the path-planning problem into a system of finite dimensional ordinary differential equations, whose dimensions change dynamically. Then a stochastic differential equation based optimization method, called intermittent diffusion, is employed to obtain the global optimal solution. We demonstrate, via numerical examples, that the new algorithm can solve the problem efficiently.


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