Heuristics for Solving Problem of Evacuating Non-Ambulatory People in a Short-Notice Disaster

Pico, Andres; Tan, Yong Hui Ronny

doi:10.21236/ada576323

Cited by 3 publications

(3 citation statements)

References 209 publications

(216 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each of them corresponds to creating upper and lower bounds for the pheromones, providing an emphasis on a strong solution from the start of the search serving as a good initial solution. Because there is no available and existing standard benchmark for validating the model and solution, newly generated instances are derived from [132]. The obtained results highlighted the importance of proper parameter selection for the algorithm.…”

Section: Metaheuristics In Evacuation Vrpsmentioning

confidence: 99%

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

et al. 2021

View full text Add to dashboard Cite

The growing field of humanitarian operations is driven by frequent events of disasters seen in the world today. Within this field, Operations Research (OR) plays a critical role in alleviating the suffering of victims that are impacted by disasters. This paper focuses on the branch of a well-known OR problem, known as the Vehicle Routing Problem (VRP), within the selected scope of humanitarian operations. A total of 123 papers of the last decade are reviewed and classified under the humanitarian operations of supply and delivery, evacuation as well as rescue operations. Besides specific disaster management phases and disaster types, various modelling challenges are highlighted, hinting towards a richer and more complex VRP seen under selected model characteristic classifications. Furthermore, established solution approaches, including hybrid solutions, are highlighted and classified, discussing how they are applied in the context of these humanitarian operations. The inclusion of a machine learning solution approach under the same classification is proposed. Finally, the trend and future outlook of VRP for the suggested humanitarian operations are discussed and highlighted.

show abstract

Section: Metaheuristics In Evacuation Vrpsmentioning

confidence: 99%

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Problems of this nature have been extensively studied in the literature. There are studies exploring the problem in a broad range of applications that include military operations [13], humanitarian logistics [14], and disaster recovery [15]. In the field of operations research, it falls under the class of problems known Facility Location Problem [16].…”

Section: Patrol Car Pre-positioningmentioning

confidence: 99%

Efficient Real-Time Allocation of Patrol Cars in Traffic Management

Al-Harthei¹,

Toth²,

Garib³

et al. 2017

Proceedings of the 2nd World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

Efficient allocation and dispatch of emergency vehicles is a common problem encountered by law enforcement and public safety agencies. An example of such agencies is the traffic patrol department, which has the responsibility of regulating and managing the movement of road traffic as well as responding to road incidents in a timely manner. A brief analysis of the patrol car allocation problem is presented. The problem involves determining the minimum number of patrol cars required to maintain a required service standard and the optimum location of patrol cars to achieve the best response times. An algorithm for determining the optimal number of patrol cars and the prepositioning of the vehicles is presented. The algorithm combines an analytical model with a Monte Carlo simulation, keeping execution times manageable. Even when applied to large geographic areas, the computational requirements of the algorithm remain modest.

show abstract

“…There are no readily available standard datasets in literature to validate or benchmark, the solution of our OBVRP. Hence, five stylized datasets constructed by Pico and Tan () were used in this study. The main characteristics of the datasets are summarized in Table .…”

Section: Computational Experimentsmentioning

confidence: 99%

Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster

et al. 2015

Self Cite

View full text Add to dashboard Cite

In past disasters, arrangements have been made to evacuate people without their own transportation, requiring them to gather at select locations to be evacuated. Unfortunately, this type of plan does not help those people who are unable to move themselves to the designated meeting locations. In the United States, according to the Post‐Katrina Emergency Management Reform Act of 2006, state or local governments have the responsibility to coordinate evacuation plans for all populations. These include those with disabilities. However, few, if any, have plans in place for those who are mobility‐challenged. The problem of evacuating mobility‐challenged people from their individual locations in a short‐notice disaster is a challenging combinatorial optimization problem. In order to develop the model and select a solution approach, we surveyed related literature. Based on our review, we formulate the problem and develop an Ant Colony Optimization (ACO) algorithm to solve it. We then test two different versions of the ACO algorithm on five stylized datasets with several different parameter settings.

show abstract

Heuristics for Solving Problem of Evacuating Non-Ambulatory People in a Short-Notice Disaster

Cited by 3 publications

References 209 publications

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

Efficient Real-Time Allocation of Patrol Cars in Traffic Management

Evacuating People with Mobility‐Challenges in a Short‐Notice Disaster

Contact Info

Product

Resources

About