Research on Emergency Material Allocation Mode of Sudden Disaster with Improved NSGA-II

2021 ◽  
Author(s):  
Ning Tao ◽  
Wang Jiayu ◽  
Han Yumeng
Keyword(s):  
Big Data ◽  
Dynamic Change ◽  
Demand Forecasting ◽  
Economic Cost ◽  
Sample Average Approximation ◽  
Nsga Ii ◽  
Emergency Relief ◽  
Disaster Victims ◽  
Sample Average ◽  
Design Scheduling

Abstract Background:In order to solve the problems of redundancy, unfairness, low satisfaction and high cost of emergency material allocation caused by unreasonable allocation effectively in the case of sudden disasters, and minimize the economic cost, punishment cost and maximizing the satisfaction rate of disaster victims, a 3-level network emergency material allocation mode based on big data is proposed in this paper.Methods:Taking the loss degree and the dynamic change of material demand in the disaster stricken areas as constraints, the demand forecasting, scheduling optimization, targeted allocation and disaster victims' satisfaction model based on emergency relief materials is constructed. The Sample Average Approximation method and improved NSGA-II algorithm are designed to solve the problem.Results:Compared with the results obtained by the improved NSGA-II, the value is significantly reduced. From the fairness evaluation results of the two model distribution schemes, the model obtained by the improved NSGA-II is more suitable for the distribution of emergency supplies with fair distribution requirements.Conclusions:It can be concluded that the 3-level network allocation mode and improved NSGA-II can solve emergency relief materials allocation based on big data effectively. The next step is to design scheduling model with all feasible medical supplies allocation route to improve the practicability of the model.

Sample Average Approximation

2017 ◽  
pp. 75-84
Author(s):  
Tingsong Wang ◽  
Shuaian Wang ◽  
Qiang Meng
Keyword(s):  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation

Improving the approaches of traffic demand forecasting in the big data era

Cities ◽  
2018 ◽  
Vol 82 ◽  
pp. 19-26 ◽  
Author(s):  
Yongmei Zhao ◽  
Hongmei Zhang ◽  
Li An ◽  
Quan Liu
Keyword(s):  
Big Data ◽  
Demand Forecasting ◽  
Traffic Demand

SAMPLE AVERAGE APPROXIMATION METHOD FOR SOLVING A DETERMINISTIC FORMULATION FOR BOX CONSTRAINED STOCHASTIC VARIATIONAL INEQUALITY PROBLEMS

2012 ◽  
Vol 29 (02) ◽  
pp. 1250014
Author(s):  
MEI-JU LUO ◽  
GUI-HUA LIN
Keyword(s):  
Variational Inequality ◽  
Approximation Method ◽  
Deterministic Model ◽  
Random Parameter ◽  
Sample Average Approximation ◽  
Variational Inequality Problems ◽  
Stochastic Variational Inequality ◽  
Sample Average ◽  
Sample Average Approximation Method ◽  
Average Approximation

In this paper, we discuss the Expected Residual Minimization (ERM) method, which is to minimize the expected residue of some merit function for box constrained stochastic variational inequality problems (BSVIPs). This method provides a deterministic model, which formulates BSVIPs as an optimization problem. We first study the conditions under which the level sets of the ERM problem are bounded. Then, we show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in BSVIPs. Since the integrality involved in the ERM problem is difficult to compute generally, we then employ sample average approximation method to solve it. Finally, we show that the global optimal solutions and generalized KKT points of the approximate problems converge to their counterparts of the ERM problem. On the other hand, as an application, we consider the model of European natural gas market under price uncertainty. Preliminary numerical experiments indicate that the proposed approach is applicable.

scholarly journals Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

2017 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Abhith Pallegar
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Rare Diseases ◽  
Network Effects ◽  
Medical Knowledge ◽  
Economic Cost ◽  
Medical Data ◽  
Data Sets ◽  
Leading Role ◽  
Diverse Data

The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor. 

scholarly journals Sample average approximation of stochastic dominance constrained programs

2010 ◽  
Vol 133 (1-2) ◽  
pp. 171-201 ◽  
Author(s):  
Jian Hu ◽  
Tito Homem-de-Mello ◽  
Sanjay Mehrotra
Keyword(s):  
Stochastic Dominance ◽  
Sample Average Approximation ◽  
Sample Average ◽  
Average Approximation
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