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Performance Comparing and Analysis for Slot Allocation Model

Performance Comparing and Analysis for Slot Allocation Model

of the airline’s network or the commercial viability of the flight. As a result, certain slots may not be attractive enough to be actually operated by the assigned airport users, a fact that may lead to waste of a really scarce resource[16]. At present, this situation is very common in China's airports. Especially, the current schedule does not take into account the actual implementation difficulties. Although the scheduled slot is constrained by capacity, it cannot be implemented in actual operation, resulting in the accumulation and propagation of delays[27]. Acquiring the appropriate slots at two congested airports like PEK and PVG is extraordinarily difficult given the scarce capacity at both airports. This difficulty is common, as Debbage [25]has pointed out for many years, but surprisingly it has not yet been clearly integrated into any slot allocation model. Although the network-based slot allocation model has emerged in recent years [15, 19], which explicitly considers the problem of flight time matching at hinge airports, it is still subject to priority constraints. It is conceivable that if a new entrant is to operate such a competitive route, the priority of his application will be less than that of other applications with grandfather's rights when the application slot is limited coincidentally by capacity. This application may be adjusted or rejected, and other applications with grandfather's rights may not have this difficulty but still occupy a scarce slot. This is obviously unreasonable, because it will increase the cost of new entrants on this route, which in turn affects the welfare of passengers on this route, although the new entrant may obtain this slot through secondary market transactions. Therefore, incorporating implement difficulties into CAA rules seems to be one of the promising solutions to the over-concentration of departure

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A resource allocation model for admission into nigerian universities

A resource allocation model for admission into nigerian universities

A Resource Allocation Model (RAM) is a methodology that ensures that available resources are used judiciously to achieve the objectives of an institution to a high level of satisfaction (Solanke and Olatunji, 2015). RAM provides incentives to academic units using an approach that enables those units to contribute to the success of the institution as a whole (Morgan, and Prowle, 2005; Shattock and Rigby, 1983). It incorporates a decision support system into its functionality to aid decision making. It has the potential of improving decisions by suggesting solutions that are better than those made by human alone which is prone to bias. This methodology also helps to settle trade-offs and prioritize information in situations where precision and optimality is important even if the inflow of data is high (Druzdzeland Flynn, 2002). Hence, RAMs are the means by which an organization’s resources are distributed (Field and Klingert, 2001).According to Engineering Ethics, available resources may be shared by merit, social worth, need, and random or equal assignment. In Nigeria, offered degrees are allocated to candidates basically by merit which is based on demonstrated ability by the candidate, and in special circumstances by random or equal assignment. For randomallocation, a RAM will assist in decision making for recommendation and allocation as substitute for human judgment which is prone to bias when the individual is stressed or emotional. RAM is a form of decision support system. A Decision Support System (DSS) is a tool that aids the process of decision making in intricate systems, predominantly where information is indecisive or partial (Michael, 2005). DSS is the area of Information Systems (IS) discipline that focuses on civilizing managerial decision-making (Arnott, 2005). It makes decisions based on algorithms derived from an understanding of the application domain (Ajayi et al., 2014).

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Multi Reservoir Water Price and Allocation Model

Multi Reservoir Water Price and Allocation Model

concerning Water Resources, also states the need for a new paradigm in water resources management with adaptive modern management behavior. Water is an economic commodity [3]. The importance of optimizing water prices based on needs and regulating water requirements affecting the amount of water prices [4]. Water resources management must go through the Hydro Politic approach where the policy of water management is not just setting the amount or volume of water, but financed is a factor that must not be ignored [5]. Water is an economic commodity which cost of water consisted of distribution, production and transmission [6], [7]. Water cost optimization is based on the demand and regulation of water need allocation [8], [9]. Many research was conducted about water allocation optimization without water cost [10], [11], [12].

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Review of the Primary Schools’ General Allocation Model

Review of the Primary Schools’ General Allocation Model

The GAM built on the work in Circular 24/03 (Allocation of Resources for Pupils with Special Educational Needs in National Schools) which provided clarification in relation to the flexible deployment of these resources in primary schools. It pointed to the need for pupils with special educational needs to belong to a peer group and to mix with pupils of different levels of ability in a variety of situations. It maintained that an exclusive reliance on using resource teaching hours for individual tuition only was contrary to the principle of integration in learning and teaching and advised that primary schools should deploy their allocated special education resources in a way that best accommodates the special educational needs of pupils. It recommended that, wherever possible, schools should provide additional teaching support for pupils in the mainstream classroom or in small groups. It anticipated that such an approach would help to maximise effective and efficient teaching and learning and to minimise disruptions to the class programme. Up until the introduction of the GAM, schools had to apply for additional teaching resources in respect of each child with a higher incidence special need.

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A Role Allocation Model For IT Controls In A Cloud Environment

A Role Allocation Model For IT Controls In A Cloud Environment

A review of literature in the Association of Information Systems’ (AIS) database also showed a considerable gap in research in this domain. A title search using the words ‘roles and responsibilities’, ‘IT governance’, and ‘cloud computing’ was conducted in the (AIS) journals database (www.ais.com) spanning the years 2008 to 2013. The topics that relate to this domain, from these sources, mainly focus on the critical success factors for the service receiver of IT outsourcing (Hodosi & Rusu, 2013), organizational integration of green IS through specific roles and responsibilities (Loeser, 2013), suggestions to help IT practitioners in organizations look beyond SOX regulations at governance of end-user developed content (Leon, Abraham, & Kalbers, 2010), organizational change resulting from IT innovations (Suo, Techatassanasoontorn, & Purao, 2011), and the importance of adequate IT management capabilities, manifested in IT governance (Kim, Shin, Kim, & Lee, 2011). Thus, there is a lack of clarity in role allocation in a cloud environment. With cloud computing becoming a rapidly growing market (Repschlaeger et al., 2012), there is a need to explore this further through the research question, What is the criteria for allocating roles and responsibilities of IT controls in a cloud environment?

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THE TASK ALLOCATION MODEL IN COMMUNICATION CHANNEL DELAY

THE TASK ALLOCATION MODEL IN COMMUNICATION CHANNEL DELAY

A Distributed Computing System (DCS) consists of any number of possible configurations, such as mainframes, personal computers, workstations, minicomputers, and so on. The goal of distributed computing is to the transparent data distribution within a local network. A user-oriented definition of distributed computing is"Multiple Computers, utilized cooperatively to solve problems" has been reported by {Sita [04], Chia [03], and Bhut [02]}. Distributed computing systems are of current interest for the researchersdue to the advancement of microprocessor technology and computer networks {Till [05], Aror [01]}. In a DCS, the execution of a program may be distributed among several processing elements to reduce the overall cost of execution by taking advantage of inhomogeneous computational capabilities and other resources within the system. The task allocation in a DCS finds extensive applications in the faculties, where large amount of data is to be processed in relatively short period of time, or where real-time computations are required such as, Meteorology, Cryptography, Image Analysis, Signal Processing, Solar and Radar Surveillance, Simulation of VLSI circuits and Industrial process monitoring are areas of such applications.

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A glass bottle allocation model for Heineken

A glass bottle allocation model for Heineken

The previous chapters described the current  allocation  processes at  Heineken  and solutions to  allocation  problems found in the literature. This  chapter discusses a solution to the allocation problem  at Heineken.  An  objective  of  this  research  is  to  improve  the  insight  into  the  quality  of  the  solution.  This  can  be  accomplished  by the use of a goal programming approach (Figure  4). In this  approach, the allocations are  characterized  by  their  values  on  the  objectives costs and  security  of  supply. Every  solution  is a  trade‐off  between  optimizing  for costs and  optimizing  for security  of  supply.  The  boundaries  to the solution  range  are the best possible cost and the best possible security of supply solutions. In Figure 4, these are indicated 

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Cost allocation in the operating toom department: the consumer pays

Cost allocation in the operating toom department: the consumer pays

We found that a cost allocation model should satisfy two main needs: It should give incentives that promote efficient and effective decisions, and it should be practical. We define criteria – along these two needs – to score the current cost allocation model and possible alternatives. Our criteria are information efficiencies, evaluation of divisions, behavior congruence, communual resource pooling, effort, complexity, and infrastructural change. The scores for ‘evalua- tion of divisions’ and ‘behavior congruence’ are based on financial estimates. With the current cost allocation model, 74,9% of OR costs are ‘directly’ charged to the decision maker. This means that for about 25% of the costs, someone else is responsible. As a result, 44,3% of the costs of the OR depart- ment can not be influenced by this department. For this reason, the managers of the OR department can not be held fully responsible for budget overruns. Our approach to find better alternatives results in two classes of solutions: solutions for the long term, and solutions for the short term. These short-term solutions should be considered as a first implementation phase, it is a step to the desirable situation.

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Optimization Model For Fighter Squadron Scheduling And Sortie Allocation

Optimization Model For Fighter Squadron Scheduling And Sortie Allocation

The total sortie allocation easily met the minimums required for each pilot, as all pilots are flying at least ten times, which is the highest minimum. The sum of sorties allocated to wingmen is 108, exactly half of the 216 total in the month, which indicates the objective was successful in ensuring that wingman flying is maximized. By comparing the “Red This Year” column with the new “Red Total” column, one can see the second model made a significant impact on the red-sortie inequity among pilots that had been present so far this year. Pilots that had logged well above the average of 28.8 were recommended to get significantly more blue-sorties than red. Pilot 7 and Pilot 8’s requirement of 8 blue-sorties due to their syllabus events was preserved. The decision was made using the second model’s sortie allocation as a recommendation rather than a constraint to feed back into the first model due to the first model’s susceptibility to infeasibility. Though the correct number of blue- and red-sorties are allocated for both wingmen and flight leads or instructors to fit the schedule, the additional complexities of crew rest, pilots on leave, and scheduled upgrade sorties could not be accommodated in a feasible schedule. However, the scheduler may use the solution report of the second model as a tool to determine which recommended sortie allocations are the most critical to reducing inequity through the use of sensitivity analysis. That is, the variable with the largest reduced cost is Pilot 10’s red sortie allocation. Therefore, increasing Pilot 10’s red-sortie allocation will increase the total variance the most, and so is the most critical to adhere to. Alternatively, many pilots have a red-sortie allocation variable whose value of reduced cost is near zero, so deviating from the recommended number for any of those pilots in order to achieve feasibility will not significantly impact the objective function value. By analyzing the dual prices, the scheduler can see which constraints are nonbinding, and therefore eligible for deviation from the recommendation. In this case, the sortie allocation constraints for Pilots 9, 10, 12, 13, and 15 have negative dual prices and are thus nonbinding. Therefore, the scheduler can reallocate sorties for those pilots and still achieve a feasible total allocation.

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Model for allocation of medical specialists in a hospital network

Model for allocation of medical specialists in a hospital network

Efficient allocation of medical specialists across hos- pitals in a network requires not only demand information (eg, patient complexity and hospital expansion), but also physicians’ willingness to be placed at unanticipated sites. Although this scenario is rather common in a public-dom- inated health care system, evidence on how to promote an efficient allocation of medical specialists has been lacking. In general, a hospital that encounters complicated and resource- intensive patients requires a specialist. To address this, the case mix index (CMI) was developed to reflect the diversity, clinical complexity, and resource needs of all patients in a hospital. CMI for a hospital is the average relative diagnosis- related group weight of a hospital’s inpatient discharges, calculated by summing the Medicare severity-diagnosis- related group weight for each discharge and dividing the total by the number of discharges. For example, a hospital with a CMI of 2.0 for patient care per 1000 patients compared to a hospital with a CMI of 1.0 for patient care per 1000 patients is twice as likely to use more resources and would have a higher incidence of sicker patients. CMI is one of the factors that influences resource management, as reported by Kalisch et al. 12

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Improving Competitiveness For A Rubberised Coir Mattress Company Through Cost Optimization

Improving Competitiveness For A Rubberised Coir Mattress Company Through Cost Optimization

The new costing model has been developed using Microsoft Office Excel. The spreadsheet has four tabs representing stages of cost allocation and a final report tab. The Activity based costing principles are used to establish relations in every stage of cost allocation and uses the actual cost and resource consumption data collected from the production department. The procedure can be summarized as follows:-

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Social Networks Based Model for Leveraging Infrastructure Resource Allocation in Cloud Computing

Social Networks Based Model for Leveraging Infrastructure Resource Allocation in Cloud Computing

Cloud computing is an emerging technology of new model in computing arena which realizes the dream of commoditizing computing resources in pay per use fashion. As envisaged in 1960s, the cloud computing became a reality now which helps individuals and organizations to gain access to huge amount of computing resources without the need for capital investment. This lets a common man or small organization to leverage businesses to higher level by utilizing cloud services. The conceptual overview of cloud computing is as shown in Figure 1. The cloud is dynamically scalable and can provide various kinds of services in pay as you use fashion with affordable pricing.

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Measuring Strategic Communications

Measuring Strategic Communications

In conclusion, the bandwidth achieved from this model does indeed scale with center frequency and frequency span. Obviously designing the model around a center frequency of a few terahertz would provide the bandwidth, 512 GHz, sought after by the computer architecture team of the WiNoC project. However, the actual design of the hardware and components remains very challenging. It should also be noted that choosing a lower insertion loss variation to define bandwidth (we used 2 dB) would naturally decrease the amount of bandwidth that we can achieve from any model shown above. Even though the delay spread corresponding to the insertion loss variation of 2 dB results from a straight line approximation to the actual insertion loss curve, delay spreads in nanoseconds would be severely performance limiting and almost certainly would require us to utilize equalization at the receiver side Also, the aspect of perfect filtering is unrealistic and we would envision allocating guard bands between channels to prevent interference in our FDM scheme. This would decrease both the spectral efficiency and

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Robust asset allocation under model ambiguity

Robust asset allocation under model ambiguity

The different factor models will be evaluated with European stock returns data collected from 2000 to 2010. In contrast to numerous reference papers focusing on portfolio analysis (for instance Fama & French (1998), who test high price to book ratio portfolios against low price to book ratio portfolios, or Jensen (1969), who test mutual funds performance) a single stock approach is adopted here, rather than a portfolio approach. Our aim is not to evaluate precisely some stand alone portfolio strategies, but to compare different factor models using some common criteria, such as the Jensen Alpha (Jensen (1969)) described in Chapter 2, Section 3. Also, daily data for a large number of European stocks are retrieved, allowing better granularity in the data than the two monthly studies cited above. In particular, daily fundamental data are used so that a pure fundamental factor model is built where stock returns are directly explained by fundamental factors.

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Skill investment, farm size distribution and agricultural productivity

Skill investment, farm size distribution and agricultural productivity

distribution of skills and the size distribution of farms. Although the former is not observable, the latter is. Moreover, more skilled farmers operate a bigger farm, other things equal. Hence, two observations should follow if farmers in poor countries indeed have lower skill. First, mean farm size should be smaller in poor countries. Indeed, it is. A typical farm in Burkina Faso is only 1/20 the size of a typical farm in the US. Figure 2 plots mean farm size in the model and in the data. The model successfully reproduces the positive correlation between output per worker in agriculture and mean farm size. In the Appendix, the size distributions of some selected countries are plotted against their empirical counterparts. Although the model is not expected to replicate the empirical distribution exactly, it does reproduce one salient feature of the data, namely, the size distribution of farms in low income countries is more skewed to the left. This comes from the fact the there is a larger share of low skill farmers in these countries. The discrepancy between the model distribution and the empirical distribution - the part that a model of skill investment is unable to explain - might imply farm level distortions that are prevalent in developing countries 8 and highlighted in

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Agricultural productivity, skill investment, farm size distribution, income differences

Agricultural productivity, skill investment, farm size distribution, income differences

The intensive margin is particularly interesting. In a standard human capital model with only time input in the human capital technology, it is generally the case the differences in lev- els of TFP have no impact on the optimal allocation of time. Recent advances in the literature stress the key role of resources input in the human capital technology. Manuelli and Seshadri (2005), Cordoba and Ripoll (2007), and Erosa, Koreshkova, and Restuccia (2010) are excel- lent examples along this line. In these papers, optimal investment bundle varies with levels of TFP, and offers an additional source of differences in human capital. In the current pa- per, resources input is absent in the skill accumulation technology. Nonetheless, optimal time investment varies with levels of TFP. This is because low TFP is accompanied by high equilibrium interest rate, which renders skill investment less profitable.

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The Allocation of Power in the Enlarged ECB Governing Council: An Assessment of the ECB Rotation Model. CEPS Report in Finance and Banking No. 35, 1 November 2004

The Allocation of Power in the Enlarged ECB Governing Council: An Assessment of the ECB Rotation Model. CEPS Report in Finance and Banking No. 35, 1 November 2004

It proves to be more difficult, however, to explain the surprising rise of the Shapley index after the accession of the 20 th euro-area member state. At this step of enlargement, power even exceeds the relative voting weight. As the votes of the Executive Board and the considered group do not change at this point, the shift in the distribution of power can only be explained through the decrease of the voting weights of the members of group 2. This shift in the allocation of voting rights changes the number of possibilities that are decisive in voting situations for group 1 members and raises their power in this specific scenario. After the next accession, the power of group 1 returns to the initial level again. The division of the NCB governors into three different groups does not affect the power of group 1 to a greater extent – it stays at a relatively stable level.

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A Model of Vacancy Chains as a Mechanism for Resource Allocation

A Model of Vacancy Chains as a Mechanism for Resource Allocation

While not contradictory to one another, only the condition (c) among the eight listed above cares that vacancy chains allocate resources rather than just tracking the allocation of resources made by the market. The formulation adopted in this paper is more general, for it does not require that allocations “represent the out- comes of authority decisions” but simply that only one candidate is there for a certain role, as it happened in the experiments with hermit crabs. Furthermore, the alternatives to vacancy chains considered in this paper include all means of allocating scarce resources based on competition, including the market but not limiting to it.

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THE STRUCTURED MODEL FOR FUNCTION ALLOCATION ANALYSIS

THE STRUCTURED MODEL FOR FUNCTION ALLOCATION ANALYSIS

This view has also been presented in the same way in the system engineering domain. According to the “HSI” which is published by the NRC in the United States in connection with HSI activities that include the Function Allocation, it would be desirable to push forward with the design activities through system engineering framework applying various tools and methodologies used in HSI sector and it would be necessary to ensure the presentation of common modeling language supporting mutual communication of multidisciplinary HSI organization members. To summarize, multidisciplinary team would need to be formed to ensure approach from wider perspective for successful Function Allocation Analysis[Mavor (2007)].

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NEW MODEL TRANSFORMATION USING REQUIREMENT TRACEBILITY FROM REQUIREMENT TO UML 
BEHAVIORAL DESIGN

NEW MODEL TRANSFORMATION USING REQUIREMENT TRACEBILITY FROM REQUIREMENT TO UML BEHAVIORAL DESIGN

Accompanied China's rapid social and economic development, water shortages are exacerbated further highlight the competition for water, also facing severe challenges of floods, pollution, unreasonable allocation of water resources and water environment deterioration. In the development process, how to not only to maintain a harmonious relationship between man and water, but also take into account the social justice and economic efficiency of water resources between rich and poor regions, is an important issue to solve. Especially the rational allocation problem of water resources in the inter-regional, inter-sectoral, is already a focus of many of the contradictions to the dryland in the development process.

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