Effective Disaster Evacuation by Solving the Distributed Constraint Optimization Problem

Kinoshita, Katsuya; Iizuka, Kayo; Iizuka, Yasuki

doi:10.1109/iiai-aai.2013.40

Cited by 10 publications

(7 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a disaster scenario, moving evacuees to the closest refuge shelter can quickly overwhelm shelter capacities. A number of researchers have proposed a DCOP model for disaster evacuation, in which several groups of evacuees have to be led to available shelters (Carpenter, Dugan, Kopena, Lass, Naik, Nguyen, Sultanik, Modi, & Regli, 2007;Kopena, Sultanik, Lass, Nguyen, Dugan, Modi, & Regli, 2008;Lass, Kopena, Sultanik, Nguyen, Dugan, Modi, & Regli, 2008a;Lass, Regli, Kaplan, Mitkus, & Sim, 2008b;Kinoshita, Iizuka, & Iizuka, 2013). Group leaders can communicate via mobile devices to monitor and coordinate actions.…”

Section: Disaster Management and Coordination Problemsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

136

119

View full text Add to dashboard Cite

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments.This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

show abstract

Section: Disaster Management and Coordination Problemsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

136

119

View full text Add to dashboard Cite

show abstract

“…An analytic system-optimal dynamic traffic-assignment model with probabilistic demand and capacity constraints was proposed in [17] to capture the evacuation process, while stochastic programming was used as the solution method. Following a similar philosophy, a distributed-constraint optimization algorithm was introduced in [18] to optimize human evacuation timing by estimating their location. A centralized optimization problem was formulated in [19] aiming at minimizing total clearance time of the disaster area and the travel time of each evacuee under the constraints of avoiding traffic congestion and balancing traffic loads between different evacuation routes.…”

Section: Related Workmentioning

confidence: 99%

ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems

2019

View full text Add to dashboard Cite

Natural disasters and terrorist attacks pose a significant threat to human society, and have stressed an urgent need for the development of comprehensive and efficient evacuation strategies. In this paper, a novel evacuation-planning mechanism is introduced to support the distributed and autonomous evacuation process within the operation of a public safety system, where the evacuees exploit the capabilities of the proposed ESCAPE service, towards making the most beneficial actions for themselves. The ESCAPE service was developed based on the principles of reinforcement learning and game theory, and is executed at two decision-making layers. Initially, evacuees are modeled as stochastic learning automata that select an evacuation route that they want to go based on its physical characteristics and past decisions during the current evacuation. Consequently, a cluster of evacuees is created per evacuation route, and the evacuees decide if they will finally evacuate through the specific evacuation route at the current time slot or not. The evacuees’ competitive behavior is modeled as a non-co-operative minority game per each specific evacuation route. A distributed and low-complexity evacuation-planning algorithm (i.e., ESCAPE) is introduced to implement both the aforementioned evacuee decision-making layers. Finally, the proposed framework is evaluated through modeling and simulation under several scenarios, and its superiority and benefits are revealed and demonstrated.

show abstract

“…Random-Initial-Solution (); (6) Assign-Initial-Weights (); (7) while (! ) do (8) Best = 0; (9) = 0; (10)…”

Section: Variable Depth Search Algorithmmentioning

confidence: 99%

“…Examples include the APACHE system [1] used at Roissy Airport in Paris, PLAN system [2] which is a medium-long term scheduling system for aircraft assembly line scheduling, the COBRA system [3] that generates work plans for train drivers and conductors of North Western Trains in the UK, and TAP-AI which is a planning system for crew assignment in the airline SAS [4]. Disasters which have long impacted world nations, resulting in mass casualties and huge financial tolls where technology and humans have to work together hand-in-hand without fault, with every single step of a mission meticulously planned out, are another research area where solutions based on constraint technology have received a great attention lately [5,6]. The handbook of Constraint Programming [7] lists example applications of several areas modeled as CSPs.…”

Section: Introductionmentioning

confidence: 99%

A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems

Bouhmala

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the past is the update ofkvariables during each iteration when moving from one assignment of values to another. The benchmark is based on hard random constraint satisfaction problems enjoying several features that make them of a great theoretical and practical interest. The results show that the proposed metaheuristic is capable of solving hard unsolved problems that still remain a challenge for both complete and incomplete methods. In addition, the proposed metaheuristic is remarkably faster than all existing solvers when tested on previously solved instances. Finally, its distinctive feature contrary to other metaheuristics is the absence of parameter tuning making it highly suitable in practical scenarios.

show abstract

Effective Disaster Evacuation by Solving the Distributed Constraint Optimization Problem

Cited by 10 publications

References 2 publications

Distributed Constraint Optimization Problems and Applications: A Survey

Distributed Constraint Optimization Problems and Applications: A Survey

ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems

A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems

Contact Info

Product

Resources

About