scholarly journals Dynamic Matching in Cloud Manufacturing considering Matching Costs

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qi Chen ◽  
Qi Xu ◽  
Cui Wu
Keyword(s):  
Resource Sharing ◽  
Cost Sharing ◽  
Supply And Demand ◽  
Cloud Manufacturing ◽  
Feasible Region ◽  
Market Demand ◽  
Control Approach ◽  
Dynamic Matching ◽  
The Impact ◽  
Resource Service

As a service-oriented business platform model, the nature of cloud manufacturing is to realise the manufacturing resources’ sharing, which will largely benefit resources supplier, resources demander, and platform operator. However, it also faces some new problems. One of the most critical issues is how to dynamically match resources of supply and demand to maximise profits of all parties while considering matching costs. This paper investigates the resources’ dynamic matching in a manufacturing supply chain that operates under a cost-sharing contract and consists of two independent and competing manufacturers and a resource-service platform. We first use differential equation to model the evolution of resource-sharing and capture the effect of matching service efforts on market demand. Next, we study the optimal matching strategies by a two-stage differential game based on the dynamic control approach. Then, we design a cost-sharing contract to coordinate and improve the supply chain’s performance. Finally, a numerical example is provided to illustrate the impact of platform transaction fees and matching costs on the feasible region of the corresponding contract.

Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments

2016 ◽  
Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm ◽  
Philip Moore
Keyword(s):  
Resource Sharing ◽  
Service Management ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Manufacturing Equipment ◽  
Control Approach ◽  
Function Block ◽  
Feature Based ◽  
Cloud Environments ◽  
Manufacturing Resources

The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing-as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemized virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

Adaptive Robot Control as a Service in Cloud Manufacturing

2015 ◽  
Author(s):  
Göran Adamson ◽  
Lihui Wang ◽  
Magnus Holm ◽  
Philip Moore
Keyword(s):  
Information And Communication Technologies ◽  
Resource Sharing ◽  
Robot Control ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Robotic Assembly ◽  
Service Robot ◽  
Shop Floor ◽  
Control Approach ◽  
Functional Perspective

The interest for implementing the concept of Manufacturing-as-a-Service is increasing as concepts for letting the manufacturing shop-floor domain take advantage of the cloud appear. Combining technologies such as Internet of Things, Cloud Computing, Semantic Web, virtualisation and service-oriented technologies with advanced manufacturing models, information and communication technologies, Cloud Manufacturing is emerging as a new manufacturing paradigm. The ideas of on-demand, scalable and pay-for-usage resource-sharing in this concept will move manufacturing towards distributed and collaborative missions in volatile partnerships. This will require a control approach for distributed planning and execution of cooperating manufacturing activities. Without control based on both global and local environmental conditions, the advantages of Cloud Manufacturing will not be fulfilled. By utilising smart and distributable decision modules such as event-driven Function Blocks, run-time manufacturing operations in a distributed environment may be adjusted to prevailing manufacturing conditions. Packaged in a cloud service for manufacturing equipment control, they will satisfy the control needs. By combining different resource types, such as hard, soft and capability resources, the cloud service Robot Control-as-a-Service can be realised. This paper describes the functional perspective and enabling technologies for a distributed control approach for robotic assembly tasks in Cloud Manufacturing.

scholarly journals Modelling of cross organizational manufacturing resource service chain based on service supply-demand dynamic matching network

2019 ◽  
Vol 277 ◽  
pp. 01005
Author(s):  
Qingqing Yang ◽  
Jia Liu ◽  
Kewei Yang
Keyword(s):  
Network Theory ◽  
Service Composition ◽  
Manufacturing Systems ◽  
Dynamic Scheduling ◽  
Cloud Manufacturing ◽  
Cloud Services ◽  
Matching Network ◽  
Dynamic Matching ◽  
Composition Problem ◽  
Resource Service

In the cloud manufacturing systems, both manufacturing tasks and manufacturing services are in a dynamic environment. How could cloud manufacturing platform optimizes manufacturing cloud services based on QoS, matching an optimal service composition for manufacturing tasks has become an urgent problem at present. In view of this problem, we study the matching of manufacturing tasks and manufacturing services from the perspective of complex network theory. On the basis of manufacturing task network and manufacturing service network, a dynamic matching network theory model of manufacturing task-service is constructed. And then, we take a dynamic assessment of QoS. Finally, we use load and dynamic QoS as the optimization objectivities, transform the optimal manufacturing service composition problem into the shortest path problem, and the dynamic scheduling of manufacturing services is realized.

The impact of regulations on labor markets in the U.S.

2019 ◽  
Vol 1 (1) ◽  
pp. 36-40
Author(s):  
Souad Adnane
Keyword(s):  
Supply And Demand ◽  
Demand Model ◽  
Lead Teachers ◽  
Associate's Degree ◽  
Childcare Workers ◽  
Educational Requirements ◽  
The Cost ◽  
Women's Labor Force Participation ◽  
The Impact ◽  
The U.S

The District of Columbia (DC) Office of the Superintendent of Education (OSSE) issued in December 2016 new educational requirements for childcare workers, according to which, all childcare center directors in the District must earn a bachelor’s degree by December 2022 and all lead teachers an associate’s degree by December 2020 (Institute for Justice, 2018). Moreover, DC has one of the lowest staff-child ratios in the country. How are regulations pertaining to childcare workers’ qualifications and staff-child ratio affecting the childcare market in DC? The present paper is an attempt to answer this question first by analyzing the effects of more stringent regulations on the cost and availability of childcare in the U.S based on existing studies. It also uses the basic supply and demand model to examine the possible impact of the new DC policy on the cost, quality and supply of childcare in the District and how it will affect working parents, especially mothers. Next, the paper discusses the impact of deregulation based on simulations and regressions conducted by studies covering the U.S., and implications for quality. It concludes that more stringent childcare regulations, regarding educational requirements and staff-child ratios, are associated with a reduced number of childcare centers and a higher cost, and eventually affects women’s labor force participation.

scholarly journals Is there an imbalance in the supply and demand for universal accessibility knowledge? Twenty years of UAIS papers viewed through the lens of WCAG

2021 ◽  
Author(s):  
Frode Eika Sandnes
Keyword(s):  
Latin America ◽  
North America ◽  
Supply And Demand ◽  
Research Focus ◽  
Universal Accessibility ◽  
Geographical Regions ◽  
Research Profile ◽  
The Impact ◽  
Over Time

AbstractPurpose: Some universal accessibility practitioners have voiced that they experience a mismatch in the research focus and the need for knowledge within specialized problem domains. This study thus set out to identify the balance of research into the main areas of accessibility, the impact of this research, and how the research profile varies over time and across geographical regions. Method: All UAIS papers indexed in Scopus were analysed using bibliometric methods. The WCAG taxonomy of accessibility was used for the analysis, namely perceivable, operable, and understandable. Results: The results confirm the expectation that research into visual impairment has received more attention than papers addressing operable and understandable. Although papers focussing on understandable made up the smallest group, papers in this group attracted more citations. Funded research attracted fewer citations than research without funding. The breakdown of research efforts appears consistent over time and across different geographical regions. Researchers in Europe and North America have been active throughout the last two decades, while Southeast Asia, Latin America, and Middle East became active in during the last five years. There is also seemingly a growing trend of out-of-scope papers. Conclusions: Based on the findings, several recommendations are proposed to the UAIS editorial board.

scholarly journals Overcapacity Risk of China’s Coal Power Industry: A Comprehensive Assessment and Driving Factors

2021 ◽  
Vol 13 (3) ◽  
pp. 1426
Author(s):  
Delu Wang ◽  
Xun Xue ◽  
Yadong Wang
Keyword(s):  
Evaluation Model ◽  
Supply And Demand ◽  
Correlation Effect ◽  
Power Industry ◽  
Environmental Benefits ◽  
Risk Level ◽  
Correlation Based Feature Selection ◽  
Coal Power ◽  
And Control ◽  
The Impact

The comprehensive and accurate monitoring of coal power overcapacity is the key link and an important foundation for the prevention and control of overcapacity. The previous research fails to fully consider the impact of the industry correlation effect; making it difficult to reflect the state of overcapacity accurately. In this paper; we comprehensively consider the fundamentals; supply; demand; economic and environmental performance of the coal power industry and its upstream; downstream; competitive; and complementary industries to construct an index system for assessing coal power overcapacity risk. Besides; a new evaluation model based on a correlation-based feature selection-association rules-data envelopment analysis (CFS-ARs-DEA) integrated algorithm is proposed by using a data-driven model. The results show that from 2008 to 2017; the risk of coal power overcapacity in China presented a cyclical feature of “decline-rise-decline”, and the risk level has remained high in recent years. In addition to the impact of supply and demand; the environmental benefits and fundamentals of related industries also have a significant impact on coal power overcapacity. Therefore; it is necessary to monitor and govern coal power overcapacity from the overall perspective of the industrial network, and coordinate the advancement of environmental protection and overcapacity control.

scholarly journals The impact of predictability on dual-task performance and implications for resource-sharing accounts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Laura Broeker ◽  
Harald Ewolds ◽  
Rita F. de Oliveira ◽  
Stefan Künzell ◽  
Markus Raab
Keyword(s):  
Resource Allocation ◽  
Task Performance ◽  
Dual Task ◽  
Resource Sharing ◽  
Visual Information ◽  
Allocation Policy ◽  
Capacity Sharing ◽  
Dual Task Performance ◽  
Sound Sequence ◽  
The Impact

AbstractThe aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

scholarly journals Access to MRI for patients with cardiac pacemakers and implantable cardioverter defibrillators

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001598
Author(s):  
Christopher Pieri ◽  
Anish Bhuva ◽  
Russell Moralee ◽  
Aderonke Abiodun ◽  
Deepa Gopalan ◽  
...  
Keyword(s):  
Supply And Demand ◽  
Complication Rates ◽  
Similar Data ◽  
Implantable Cardioverter Defibrillators ◽  
Cardiac Pacemakers ◽  
Survey Tool ◽  
Mr Conditional ◽  
Mri Scans ◽  
Implantation Rates ◽  
The Impact

ObjectiveTo determine provision of MRI for patients with cardiac implantable electronic devices (CIEDs; pacemakers and defibrillators) in England, to understand regional variation and assess the impact of guideline changes.MethodsRetrospective data related to MRI scans performed in patients with CIED over the preceding 12 months was collected using a structured survey tool distributed to every National Health Service Trust MRI unit in England. Data were compared with similar data from 2014/2015 and with demand (estimated from local CIED implantation rates and regional population data by sustainability and transformation partnerships (STPs)).ResultsResponses were received from 212 of 223 (95%) hospitals in England. 112 (53%) MRI units’ scan patients with MR-conditional CIEDs (10% also scan non-MR conditional devices), compared with 46% of sites in 2014/2015. Total annual scan volume increased over fourfold between 2014 and 2019 (1090 to 4896 scans). There was widespread geographical variation, with five STPs (total population >3·5 million representing approximately 25 000 patients with CIED) with no local provision. There was no correlation between local demand (CIED implantation rates) and MRI provision (scan volume). Complication rates were extremely low with three events nationally in 12 months (0·06% CIED–MRI scans).ConclusionsProvision of MRI for patients with CIEDs in England increased over fourfold in 4 years, but an estimated 10-fold care gap remains. Almost half of hospitals and 1 in 10 STPs have no service, with no relationship between local supply and demand. Availability of MRI for patients with non-MR conditional devices, although demonstrably safe, remains limited.

scholarly journals The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Chenlu Tao ◽  
Gang Diao ◽  
Baodong Cheng
Keyword(s):  
Exchange Rate ◽  
Structural Changes ◽  
Raw Materials ◽  
Supply And Demand ◽  
Oil Prices ◽  
Control Measures ◽  
Var Model ◽  
Time Varying ◽  
Wood Industry ◽  
The Impact

China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”.

scholarly journals A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

2008 ◽  
Author(s):  
Scott J. Moura ◽  
Hosam K. Fathy ◽  
Duncan S. Callaway ◽  
Jeffrey L. Stein
Keyword(s):  
Power Management ◽  
Hybrid Electric Vehicle ◽  
Single Mode ◽  
Stochastic Dynamic ◽  
Electricity Pricing ◽  
Control Approach ◽  
Potential Benefits ◽  
Hybrid Electric ◽  
The Impact ◽  
Charge Depletion

This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode powersplit PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

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