Project Goal Balanced Optimization Research Based on Generalized Cost

2012 ◽  
Vol 174-177 ◽  
pp. 2966-2969
Author(s):  
Wen Ping Wu ◽  
Jian Xin Liu ◽  
Yang Zhang
Keyword(s):  
Optimization Model ◽  
Cost Category ◽  
Incentive Mechanisms ◽  
Integrated Optimization ◽  
The Cost ◽  
Generalized Cost ◽  
Balanced Optimization

Project’s duration - expense exchange question focus on how to optimize the goals by least expenses or by shortest duration limit for a project, and did not consider the influence of such optimization and incentive mechanisms for receipts and quality. This article first proposed the theory of generalized cost, brings the duration, the cost and the quality into line with the cost category, and constructed integrated optimization model of Duration, cost and quality based on the theory, with practical examples, the theory is proved feasibility, scientific and economic.

A generalized fuzzy cost efficiency model

2020 ◽  
Vol 54 (6) ◽  
pp. 1775-1791
Author(s):  
Nazila Aghayi ◽  
Samira Salehpour
Keyword(s):  
Cost Efficiency ◽  
Minimum Cost ◽  
Extensive Literature ◽  
Data Envelopment ◽  
Fuzzy Environment ◽  
Decision Making Unit ◽  
Production Possibility Set ◽  
Fuzzy Cost ◽  
The Cost ◽  
Generalized Cost

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

At what cost is performance-based financing implemented? Novel evidence from Malawi

2019 ◽  
Vol 34 (4) ◽  
pp. 282-288 ◽  
Author(s):  
Manuela De Allegri ◽  
Chris Makwero ◽  
Aleksandra Torbica
Keyword(s):  
Low Income ◽  
Economic Cost ◽  
Personnel Cost ◽  
Cost Category ◽  
Implementation Phase ◽  
Personnel Costs ◽  
Performance Based Financing ◽  
Implementation Costs ◽  
The Cost ◽  
Low Income Setting

Abstract Our study estimated the full economic cost of implementing performance-based financing [PBF, the Support for Service Delivery Integration Performance-Based Incentives (SSDI-PBI) programme], as a means of first introducing strategic purchasing in a low-income setting, Malawi. Our analysis distinguished design from implementation costs and traces costs across personnel and non-personnel cost categories over the 2012–15 period. The full cost of the SSDI-PBI programme amounted to USD 3 402 187, equivalent to USD 6.46 per targeted beneficiary. The design phase accounted for about one-third (USD 1 161 332) of the total costs, while the incentives (USD 1 140 436) represented about one-third of the total cost of the intervention and about half the cost of the implementation phase. With a cost of USD 1 605 178, personnel costs represented the dominant cost category. Our study indicated that the introduction of PBF entailed consumption of a substantial amount of resources, hence representing an important opportunity cost for the health system.

The Optimization Model of Wind Power and Thermal Power Jointly Run under the TOU Price

2013 ◽  
Vol 772 ◽  
pp. 705-710
Author(s):  
Li Wei Ju ◽  
Zhong Fu Tan ◽  
He Yin ◽  
Zhi Hong Chen
Keyword(s):  
Wind Power ◽  
Optimization Model ◽  
Unit Commitment ◽  
Thermal Power ◽  
Peak Load ◽  
Electricity Demand ◽  
Environmental Benefits ◽  
Power Company ◽  
The Cost ◽  
Pollution Emissions

In order to be able to absorb the abandoned wind, increasing wind-connect amount, the paper study the way of wind power, thermal power joint run and puts forward wind power, thermal power joint run optimization model based on the energy-saving generation dispatching way under the environment of TOU price and the target of minimizing the cost of coal-fired cost, unit commitment and pollution emissions. The numerical example finds, the TOU price can realize the goal of peak load shifting, increasing the electricity demand in the low load and reducing electricity demand in the peak load. The model can increase the amount of wind-connect grid, absorb the abandoned wind, reduce the use of coal-fired units under the environment, increase the average electricity sales price and profit of Power Company. Therefore, the model has significant economical environmental benefits

Knowledge and Expectations of Hearing Aid Applications: A Cross-sectional Survey of Smartphone Users and Hearing Professionals (Preprint)

2021 ◽  
Author(s):  
Jae Sang Han ◽  
Yong-Ho Park ◽  
Jae-Jun Song ◽  
Il Joon Moon ◽  
Woojoo Lee ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Personal Information ◽  
Hearing Aid ◽  
Assistive Device ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Cost Category ◽  
Grade Point ◽  
The Cost

BACKGROUND Despite the increasing prevalence of hearing loss, the cost and psychological barriers to use of hearing aids may prevent individuals with hearing loss from using these aids. Hearing loss patients can benefit from smartphone-based hearing aid applications (SHAAs), which are smartphone applications that use a mobile device as sound amplifier. OBJECTIVE The aims of this study were to determine how ear, nose and throat (ENT) outpatients perceived SHAAs, analyze factors that affected this, and estimate costs of annual subscription to an application through a self-administered questionnaire survey of smartphone users and hearing specialists. METHODS The study employed cross-sectional, multi-center survey of both ENT outpatients and hearing specialists. The questionnaire was designed to collect personal information about the respondents as well as responses to 18 questions concerning SHAAs in 5 domains: knowledge, needs, cost, expectations, and information. Questions about the expected cost of SHAAs were included in the questionnaire distributed to hearing experts. RESULTS Among 219 smartphone users and 42 hearing specialists, only eight respondents (3.7%) recognized SHAAs, while 47 of 261 respondents (21.5%) reported considering using an assistive device to improve their hearing capacities. Average perception score was 2.81 (95% CI 2.65-2.97), lower than the grade point average of 3. Among factors that shaped perceptions of SHAAs, the needs category received the lowest scores (2.02, 95% CI 1.83-2.20) whereas the cost category received the highest scores (3.29, 95% CI 3.14-3.44). Age was correlated with the information domain (P = .000) and an increased level of hearing impairment resulted in significantly higher points in the needs category (P = .000). Patients expected the cost of an annual application subscription to an SHAA to be about 86 USD, and predicted cost was associated with economic status (P = .200) and was noticeably higher than the prices expected by hearing specialists (P < .001). CONCLUSIONS Outpatients expected SHAAs to cost more than hearing specialists. However, SHAA perception was relatively low. In this regard, enhanced awareness of SHAAs is required to popularize SHAAs. CLINICALTRIAL None

Resource Distribution Strategies for Mitigation of Cross-Regional Influenza Pandemics

2013 ◽  
pp. 83-105
Author(s):  
Andres Uribe-Sanchez ◽  
Alex Savachkin
Keyword(s):  
Optimization Model ◽  
Resource Distribution ◽  
Institute Of Medicine ◽  
Policy Makers ◽  
Social Distancing ◽  
Influenza Pandemics ◽  
Lost Productivity ◽  
Dynamic Resource ◽  
The Cost ◽  
The Impact

As recently acknowledged by the Institute of Medicine, the existing pandemic mitigation models lack dynamic decision support capabilities. This paper develops a simulation optimization model for generating dynamic resource distribution strategies over a network of regions exposed to a pandemic. While the underlying simulation mimics the disease and population dynamics of the affected regions, the optimization model generates progressive allocations of mitigation resources, including vaccines, antivirals, healthcare capacities, and social distancing enforcement measures. The model strives to minimize the impact of ongoing outbreaks and the expected impact of the potential outbreaks, considering measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The model was implemented on a simulated outbreak involving four million inhabitants. The strategy was compared to pro-rata and myopic strategies. The model is intended to assist public health policy makers in developing effective distribution policies during influenza pandemics.

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