Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA

Rabiei, Peyman; Aranda, Daniel Arias; Stantchev, Vladimir

doi:10.1016/j.eswa.2023.120142

Cited by 17 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a solution approach, they proposed a memetic algorithm and a recursive granular algorithm to find the most feasible solutions within a reasonable time. Rabiei et al [25] focused on the assignment of volunteers in the post-disaster phase. Unmet triage needs and unsatisfied preferences of volunteers are formulated and considered as objective functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated System Design for Post-Disaster Management: Multi-Facility, Multi-Period, and Bi-Objective Optimization Approach

Song,

Jun,

Lee

2024

Systems

View full text Add to dashboard Cite

Disaster management requires efficient allocation of essential facilities with consideration of various objectives. During the response and recovery phase of disaster management (RRDM), various types of missions occur in multiple periods, and each of them needs different support from facilities. In this study, a bi-objective mathematical model was derived to support multi-period RRDM by optimal allocation of required facilities such as drone stations, shelters, emergency medical facilities, and warehouses according to the mission life cycle. Connectivity between facilities was considered to ensure inter-facility complementarity. For efficient derivation of Pareto solutions, a modified epsilon-constraint algorithm for bi-objective optimization was developed. The algorithm was tested with a realistic disaster simulation scenario using HAZUS 4.0 as a demonstration of the benefits of the proposed approach. With the simulation experiments, the proposed approach was expected to provide efficient operational plans and guidelines to decision makers for the bi-objective optimization problem in RRDM systems. In addition, the consideration of inter-facility connectivity can play an important role in the RRDM, especially for robustness and readiness.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Integrated System Design for Post-Disaster Management: Multi-Facility, Multi-Period, and Bi-Objective Optimization Approach

Song,

Jun,

Lee

2024

Systems

View full text Add to dashboard Cite

show abstract

“…It has been observed that systems that are well-designed provide better results. The technology might potentially be seen as a viable substitute for traditional monitoring methods in the context of guiding coronary artery bypass grafting [17]. The model provides an interpretation of received frequency value and thereafter plays the corresponding MP3 format file.…”

Section: Fuzzy Systems In Medicinementioning

confidence: 99%

A Review of Basic Analytics, Medical Data Sources, and Advanced Data Analytics in the Medical Sector

Suzuki,

Negishi

2024

JBSHA

View full text Add to dashboard Cite

This article provides a thorough examination of healthcare data analytics and identifies various unresolved questions that necessitate further investigation. The implementation of healthcare analytics has the potential to generate two supplementary advantages for healthcare providers, specifically heightened patient contentment and ameliorated health consequences. The field of data analytics has been propelled forward by the facilitation of healthcare data acquisition through technological and software advancements. The lack of a predetermined framework in the data, along with the constraints imposed by privacy considerations in data gathering and dissemination methods, have presented significant obstacles to the progression of the field. The expeditious handling and comprehension of data requires prompt decision-making in the presence of substantial information quantities. In specific situations, the retrieval and analysis of data may necessitate the utilisation of more advanced techniques owing to the intricacy of the data. The emergence of data collection technologies that facilitate analytics has presented new obstacles, despite their effectiveness in accumulating vast quantities of data. The healthcare industry employs a wide array of methodologies, which can be attributed to the inherent distinctions in the fundamental data types.

show abstract

“…Multi objective solving will screen out a relatively optimal set of solutions, in which Pareto is used to find the relatively optimal solution or optimal solution. The optimized influence parameters can be obtained from the Pareto frontier [25]. The high degree of similarity between the Pareto frontier value and the true value indicates that the method has ideal distribution and convergence [26].…”

Section: Performance Evaluation Of Engineering Project Mop Model Base...mentioning

confidence: 99%

Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction

Zhang

2023

Decis. Mak. Appl. Manag. Eng.

View full text Add to dashboard Cite

In the context of Sustainability Development (SD), Green Construction (GC) has become a key direction for optimizing engineering project objectives. In order to improve the management ability of project engineering in GC, an improved NSGA - II algorithm was used in this study to establish a multi-optimization model for engineering projects. In this process, the hill climbing is introduced to improve the search ability of NSGA - Ⅱ algorithm. Finally, a Multi-Objective Optimization (MOP) model with strong convergence and distribution was obtained. In subsequent validation experiments, the total construction period of the engineering project MOP model based on the improved NSGA - II algorithm was between 190 and 234days. The total cost ranges from 171,473 to 20,461,800 yuan. Its total mass ranges from 90.41% to 92.19%. Its total safety is between 91.30% and 99.32%. The total environment is between 144.54 and 193.58. Its total resources range from 86.21% to 99.91%. The cost of improving the NSGA-II algorithm is 500300 yuan lower than that of the NSGA-II algorithm, with a resource target increase of 0.4% and an environmental target increase of 4.33%. The iteration curves of the improved NSGA - II algorithm in terms of duration, cost, and environmental objective function are lower than those of the NSGA - II algorithm. Overall, the improved NSGA - II algorithm has better MOP performance, can obtain better Pareto solutions, and has better performance.

show abstract

Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA

Cited by 17 publications

References 25 publications

Integrated System Design for Post-Disaster Management: Multi-Facility, Multi-Period, and Bi-Objective Optimization Approach

Integrated System Design for Post-Disaster Management: Multi-Facility, Multi-Period, and Bi-Objective Optimization Approach

A Review of Basic Analytics, Medical Data Sources, and Advanced Data Analytics in the Medical Sector

Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction

Contact Info

Product

Resources

About