Inverse Optimization Model and Its Application for Safety Resource Allocation in High-Risk Industry Enterprises-Taking Mining Enterprise as an Example

Background Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. Methods Pertussis data covering the time period 2010–2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods 2010–2012, 2013–2015, 2016–2018 and 2010–2018. Tango’s flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation. Results County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010–2012 to 2013–2015, risk tended to be lower during the 2016–2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001). Conclusion There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.

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Success probability orientated optimization model for resource allocation of the technological innovation multi-project system

Journal of Systems Engineering and Electronics ◽

10.21629/jsee.2016.06.11 ◽

2016 ◽

Vol 27 (6) ◽

pp. 1227-1237 ◽

Cited By ~ 1

Author(s):

Weixu Dai ◽

◽

Weiwei Wu ◽

Bo Yu ◽

Yunhao Zhu ◽

...

Keyword(s):

Resource Allocation ◽

Technological Innovation ◽

Optimization Model ◽

Success Probability

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Inverse Optimization Method of Resource Allocation with Consideration of Advantage Structure

International Journal of Business Administration ◽

10.5430/ijba.v8n2p86 ◽

2017 ◽

Vol 8 (2) ◽

pp. 86

Author(s):

Lili Zhang ◽

Wenwen Yang ◽

Dejun Teng

Keyword(s):

Optimization Model ◽

Optimization Method ◽

Indicator System ◽

Inverse Optimization ◽

Competence Model ◽

Interview Method ◽

Expert Interview ◽

Principle Of Duality ◽

Conventional Optimization ◽

Programming Analysis

The research collects competence items of informal questionnaire by expert interview method, analyzes the results qualitatively and quantitatively and forms formal questionnaire. Through statistical analysis and AMOS modeling, the research obtains workers’ competence model and validates competence model. An identification method of individual advantage characters according competency indicator system is built up relying on programming analysis and parameter optimization. We build the optimization model and its inverse optimization model, starting from the original optimization model, adjusting parameter value as small as possible, the conventional optimization model translated into the inverse model by the principle of duality. A calculation example is used to make sure the method is reliability and feasibility.

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Optimizing Bus Line Based on Metro-Bus Integration

Sustainability ◽

10.3390/su12041493 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1493 ◽

Cited By ~ 1

Author(s):

Junjun Wei ◽

Kejun Long ◽

Jian Gu ◽

Qingling Ju ◽

Piao Zhu

Keyword(s):

Resource Allocation ◽

Optimization Model ◽

Geographical Location ◽

Optimization Method ◽

Line Number ◽

Competition And Cooperation ◽

Travel Efficiency ◽

Geographical Space ◽

Metro System

Metros are usually built and added on the basis of a completed bus network in Chinese cities. After the metro construction, it is faced with the problem of how to adjust and optimize the original bus lines based on the new metro system. This research mainly proposes a bus line optimization method based on bus and metro integration. In the consideration of the geographical space, the cooperation and competition relationship between bus and metro lines is qualitatively introduced according to the geographical location and service range of metro (800 m radius) and bus (500 m radius) stations. The competition and cooperation indexes are applied to define the co-opetition relationship between bus and metro lines. The bus line optimization model is constructed based on the co-opetition coefficient and Changsha Metro Line Number 2 is chosen as a case study to verify the optimization model. The results show that the positive competition, efficient cooperation, and travel efficiency between metro and bus has been significantly enhanced after optimization. Moreover, this paper provides a reasonable reference for public transport network planning and resource allocation.

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Optimization Analysis of Supply Chain Resource Allocation in Customized Online Shopping Service Mode

Mathematical Problems in Engineering ◽

10.1155/2015/519125 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jianming Yao ◽

Mengjie Gu

Keyword(s):

Resource Allocation ◽

Supply Chain ◽

Optimization Model ◽

Online Shopping ◽

Optimization Method ◽

Ant Algorithm ◽

Operational Characteristics ◽

Service Patterns ◽

Shopping Environments ◽

Service Mode

For an online-shopping company, whether it can provide its customers with customized service is the key to enhance its customers’ experience value and its own competence. A good customized service requires effective integration and reasonable allocation of the company’s supply chain resources running in the background. Based on the analysis of the allocation of supply chain resources in the customized online shopping service mode and its operational characteristics, this paper puts forward an optimization model for the resource allocation and builds an improved ant algorithm to solve it. Finally, the effectiveness and feasibility of the optimization method and algorithm are demonstrated by a numerical simulation. This paper finds that the special online shopping environments lead to many dynamic and uncertain characters of the service demands. Different customized service patterns and their combination patterns should match with different supply chain resource allocations. The optimization model not only reflects the required service cost and delivery time in the objective function, but also considers the service scale effect optimization and the relations of integration benefits and risks. The improved ant algorithm has obvious advantages in flexibly balancing the multiobjective optimizations, adjusting the convergence speed, and adjusting the operation parameters.

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