Performance Cost Function Research Articles

PaDIS emulator

We present PaDIS Emulator, a fully automated platform to evaluate CDN-ISP collaboration for better content delivery, traffic engineering, and cost reduction. The PaDIS Emulator enables researchers as well as CDN and ISP operators to evaluate the benefits of collaboration using their own operational networks, configuration, and cost functions. The PaDIS Emulator consists of three components: the network emulation, the collaboration mechanism, and the performance monitor. These layers provide scalable emulation of the interaction between an ISP or a number of ISPs with multiple CDNs and vice versa. PaDIS Emulator design is flexible in order to implement a wide range of collaboration mechanisms on virtualized or real hardware, and evaluate them before introduction to operational networks.

화물열차의 경제속도 결정에 관한 연구

철도운송은 오랜 기간 동안 도로운송, 해상운송, 항공운송보다 친환경 운송수단으로 인식되어 왔다. 하지만 최근 디젤유를 사용하는 점화기관에서 배출되는 오염물질이 대기오염의 주범으로 밝혀지면서 사회적 문제로 인식되고 있으며 녹색성장을 위한 정부 정책으로 철도분야도 온실가스 배출의 감축 의무를 피할 수 없을 것으로 전망되고 있다. 또한, 지속적인 고유가는 운영비를 상승시켜 운영기관에 큰 부담이 되고 있다. 본 논문에서는 온실가스 배출 감소와 유류비 절감을 위해 운영기관 입장에서 화물열차의 경제속도를 결정하기 위한 방법론을 제안한다. 경제속도를 추정하기 위해 유류비, 화물열차의 시간가치, 탄소세를 고려하여 비용 함수를 추정하였다. 비용 함수를 바탕으로 화물열차의 경제속도를 도출하기 위한 식을 제안하고 유류비, 화물열차의 시간가치, 탄소세의 변화가 속도에 미치는 영향을 알아보기 위해 민감도 분석을 하였다. Rail transport has been considered an environmental-friendly transport mode compared with other transport modes such as ship, truck, and aircraft. However, air pollutions emitted by diesel locomotives have emerged as social issues. In addition, the railway industry may not be able to avoid a duty of alleviating greenhouse gases emission owing to the Korean government policies for green growth which is an economic paradigm that simultaneously pursues growth and environmental improvement. Moreover, rising oil prices has burdened a train operating company. The purpose of this paper is to develop a methodology of determining an economical speed of diesel freight locomotive from the viewpoint of the train operating company. In the methodology, we first define an operational cost function based on various cost factors and then suggest formula to calculate an economical speed of diesel freight locomotive. To estimate the influence of cost factors such as diesel price, carbon taxes, and time costs on the speed of diesel freight locomotive, sensitivity analysis was conducted.

RISK-CONSTRAINED OPTIMAL BIDDING STRATEGY FOR A GENERATION COMPANY USING SELF-ORGANIZING HIERARCHICAL PARTICLE SWARM OPTIMIZATION

This article proposes optimal bidding strategies for a generation company (GenCo) considering risk of profit variation by self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients (SPSO-TVAC). Based on a trade-off technique, the expected profit maximization and risk minimization are achieved. Nonconvex operating cost functions of thermal generation units and minimum up/down time constraints are cooperated to provide the optimal bid prices in a day-ahead uniform price spot market. The rivals’ bidding behavior is estimated by Monte Carlo simulation. Test results indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimization (IWAPSO) and genetic algorithm (GA) in searching the optimal bidding strategy solutions.

Adaptive Control with Guaranteed Contraction Rate for Systems with Actuator Saturation

The problem of adaptive control of linear discrete-time systems with actuator saturation and unknown parameters is investigated. A novel optimal control method is presented first for linear systems with known parameters and constant actuator saturation by introducing a Lyapunov function and a performance cost function that are both dependent on a contraction rate parameter. Based on the obtained guaranteed contraction-rate control method, an adaptive control algorithm is derived for systems containing unknown system parameters and time-varying actuator saturation. To show that the closed-loop system is stable and that the adaptive control algorithm is convergent, the Lyapunov function is supplemented by an additional part defined by the trace of a quadratic function of the controller gain. The effectiveness and potential of the presented method is demonstrated by a numerical example.

A methodology for optimal voxel size computation in collision detection algorithms for virtual reality

Real-time Virtual Reality applications require accuracy but are also time dependent; therefore, in these environments, the time consumption is particularly important. For that reason, when facing the problem of Collision Detection for a Virtual Reality application, we firstly focus our attention on optimizing time performance for collisions among objects. Spatial Partitioning algorithms have been broadly used in Collision Detection. In particular, voxel-based methods are simple and quick, but finding the optimum voxel size is not trivial. We propose a methodology to easily determine the optimal voxel size for Collision Detection algorithms. Using an algorithm which represents volumetric objects with tetrahedra as an example, a performance cost function is defined in order to analytically bound the voxel size that gives the best computation times. This is made by inferring and estimating all the parameters involved. Thus, the cost function is delimited to depend only on geometric data. By doing so, it is possible to determine the optimal voxelization for any algorithm and scenario. Several solutions have been researched and compared. Experimental results with theoretical and real 3D models have validated the methodology. The reliability of our research has also been compared with traditional experimental solutions given by previous works.

A design algorithm using external perturbation to improve Iterative Feedback Tuning convergence

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of process insight. It is a purely data driven approach for optimization of the loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient, which is used in a search algorithm for minimizing the performance cost. A slow rate of convergence of the tuning method is often experienced when tuning for disturbance rejection. This is due to a poor signal to noise ratio in the process data. A method is proposed for increasing the data information content by introducing an optimal perturbation signal in the tuning algorithm. The theoretical analysis is supported by a simulation example where the proposed method is compared to an existing method for acceleration of the convergence by use of optimal prefilters.

Equipment selection with heterogeneous fleets for multiple-period schedules

In a simple surface mining scenario we consider a mine to have one mining location and dumpsite connected by a truck route. We determine a purchase and salvage policy for trucks and loaders, which minimises the cost of materials handling over a multiple period schedule. This problem becomes large scale when we consider large sets of equipment and long schedules. Pre-existing equipment can lead to heterogeneous fleets; non-uniformity in the operating cost function coupled with compatibility issues also adds to the complexity of the problem. We present an integer programme for this problem, where we introduce a specialised linear constraint set to ensure satisfaction of production requirements while accounting for equipment compatibility. Many aspects of the presented model, such as the consideration of multiple periods and pre-existing equipment, are novel for the mining industry and ensure that the model is both a new and advanced equipment selection tool.

Weighted Centroid Modified Simplex and Linear Constrained Response Surface Optimization Methods for the Xbar-R Chart Variable Determination

Problem statement: Although economic Xbar-R chart designs do guarantee the minimal operating cost, they typically have poor levels of statistical performance measures. The obvious limitation of the economic design is that the Type I error rate seems to be very high for many situations and will cause a large number of false alarms. This situation leads to an investigation of appropriate levels of control chart variables which consist of a sample size, an interval between samples or sampling frequency and the control chart limits. Approach: Evolutionary operations via the weighed centroid modified simplex, WCMSM and linear constrained response surface optimization, LCRSOM, methods are applied to optimize the Xbar-R chart variables in the analytical model called as the operating cost function. WCMSM allows the simplex to converge more rapidly towards an optimum via the weighted centroid of the hyperface by expansion and multiple ways of simplex contraction along the line of conventional reflection in order to speed up the convergence. LCRSOM is a combination of the principles of experimental designs, least squares and related mathematical programming models to approach the optimum. Results: The computational results for economic Xbar-R charts via both methods reveal that the process quality level affects the total cost more than relevant errors from selecting and inspecting samples including identifying assignable causes. However, total costs obtained from the LCRSOM seem to be better for all process scenarios. Conclusions: On Xbar-R charts, if a process is at the high quality level the total cost is better than a process with a low quality level without a consideration of errors from selecting/inspecting samples and identifying assignable causes.

에어노드 기반 무선센서네트워크 구축을 위한 적응형 오르막경사법 기반의 자율무인비행로봇제어

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.

Nested Switching Control Design for Fast PZT Actuated Nanopositioning

AbstractThis paper studies a new nested switching control (NSC) scheme for piezoelectric (PZT) actuators to achieve fast nanopositioning. Conventional PZT controllers typically neglect the actuator saturation nonlinearity for design simplicity but at the cost of reduced performance. By optimizing a quadratic performance cost function that explicitly involves the actuator saturation, the NSC can not only guarantee the system stability in the presence of saturation but also improve the tracking speed. The experimental results on an actual PZT nanopositioner show that the new control scheme has outperformed the conventional control by more than 12% in settling time within the full PZT operational range and with nanoscale precision.

Controlling for quality in the hospital cost function

This paper explores the relationship between the cost and quality of hospital care from the perspective of applied microeconomics. It addresses both theoretical and practical complexities entailed in incorporating hospital quality into the estimation of hospital cost functions. That literature is extended with an empirical analysis that examines the use of 15 Patient Safety Indicators (PSIs) as measures of hospital quality. A total operating cost function is estimated on 2,848 observations from five states drawn from the period 2001 to 2007. In general, findings indicate that the PSIs are successful in capturing variation in hospital cost due to adverse patient safety events. Measures that rely on the aggregate number of adverse events summed over PSIs are found to be superior to risk-adjusted rates for individual PSIs. The marginal cost of an adverse event is estimated to be $22,413. The results contribute to a growing business case for inpatient safety in hospital services.

Cost modelling for wastewater treatment processes

The aim of this paper is to provide a methodology for a better understanding of the cost structure of wastewater treatment processes. This methodology may be useful in the planning of new facilities. The existing models in the literature are focused on the influence of the capacity of plant, expressed as inhabitants or flow rate, on the cost of treatment. We propose a new approach for the operating cost function that includes the most representative variables in the process. The idea is that the modelling of treatment costs enables us to understand the key role of the economies of scale in this context; and also the influence of other variables such as contaminants removed, or the age of the facility. The calculation of these extended cost functions also enables a detailed comparison to be made of the various treatment technologies from an economic point of view. In this sense, an extensive knowledge of the structure of the costs associated with each of the available treatment technologies is a basic issue in the planning of treatment processes and water reuse projects. This research applies a cost modelling methodology using statistical information from a sample of 341 wastewater treatment plants in Spain.

Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO–TVAC) is proposed. A significant risk index based on mean–standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals’ behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO–TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.

Iterative feedback tuning of uncertain state space systems

Iterative Feedback Tuning is a purely data driven tuning algorithm for optimizing control parameters based on closed loop data. The algorithm is designed to produce an unbiased estimate of the performance cost function gradient for iteratively improving the control parameters to achieve optimal loop performance. This tuning method has been developed for systems based on a transfer function representation. This paper presents a state feedback control system with a state observer and its transfer function equivalent in terms of input output dynamics. It is shown how the parameters in the closed loop state space system can be tuned by Iterative Feedback Tuning utilizing this equivalent representation. A simulation example illustrates that the tuning converges to the known analytical solution for the feedback control gain and to the Kalman gain in the state observer. In case of parametric uncertainty, different choices of tuning parameters are investigated. It is shown that the data driven tuning method produces optimal performance for convex problems when it is the model parameter estimates in the observer that are tuned.

Analytical Design of Multi-Loop PI Controllers for Interactive Multivariable Processes

In this paper, a simple and efficient design method of multi-loop PI controllers is proposed by extending the Maclaurin series approach to a multi-loop control system. The controller parameter of a multi-loop system is related to that of a SISO non-interacting system with an interaction factor in the simple multiplication form. Based on this relation, analytical tuning rules for a multi-loop PI controller are derived for several representative process models. In order to improve both performance and robustness of the multi-loop control system, the multi-loop Ms criterion is utilized as a performance cost function. The simulation studies confirm the effectiveness of the proposed method.

Investment optimization in oil and gas plays

Abstract A model was established to illustrate the relation between cumulative exploration investment and growth of total proved reserves on the basis of growth model of total proved reserves. The Data Envelopment Analysis (DEA) theory was used to give an optimal solution of the function of constructed productivity and development investment, and, together with an exponential oil & gas operational cost function, a model of the relation between development investment and oil & gas output was deduced. Based on the models of investment-exploration-development relations, and considering maximum profit, yield component, reserves-to-output ratio and production rate, an optimal function of multi-stage investment was established, which can result in multi-stage investment structure under given conditions with genetic algorithms. The method can provide scientific basis for oil companies to make a long-term strategic program and investment plan in oil and gas exploration and development, decrease the subjective blindness in the investment and bring about a reasonable and orderly exploration and development of oil and gas resources

Economic and environmental assessment of small and decentralized wastewater treatment systems

The aim of the present work was the assessment of economic and environmental aspects of decentralized energy-saving wastewater treatment systems. The formulated investment and operation cost functions were adjusted by a power law function. The different wastewater systems serving population settlements between 50 p.e. and 250 p.e., presented associated investment costs varying from €400/p.e. to €200/p.e. and annual operation costs in the range of €70/p.e.– €20/ p.e., respectively. A life cycle analysis approach was used to compare the environmental impact of alternative wastewater treatment systems. The assessment was focused on two energy-saving systems (constructed wetland and slow rate infiltration) and a conventional one (activated sludge process). Low environmental impact of energy-saving wastewater treatment systems was demonstrated, being the most relevant the global warming indicator. Options for reduction of life cycle impacts were assessed including materials used in construction and operation l...

Management of logistics operations in intermodal terminals by using dynamic modelling and nonlinear programming

The increase in efficiency of container terminals is addressed via an ap- proach based on the optimisation of logistics operations. Toward this end, a discrete-time dynamic model of the various flows of containers that are inter-modally routed from ar- riving carriers to carriers ready for departure is proposed. On the basis of such a model, the decisions on the allocation of the available handling resources inside a container terminal are made according to the predictive-control approach by minimising a performance cost function over a forward horizon from the current time instant. Since both the dynamic equations and the cost function are in general nonlinear and since binary variables are used to model the departure or stay of a carrier, such decisions result from the on-line solution of a mixed-integer nonlinear programming problem at each time step. To solve this problem, two techniques are proposed that have to deal explicitly with the binary variables and with the nonlinearities of the model and the cost function. The first relies on the application of a standard branch-and-bound algorithm. The second is based on the idea of treating the decisions associated with the binary variables as step functions. Simulation results are reported to illustrate the pros and cons of such methodologies in a case study.

Application of a Transition Graph-Based Predictive Algorithm to a Solar Air Conditioning Plant

This brief presents the development of a transition graph-based predictive algorithm and its application to a solar air conditioning plant. The process considered is a hybrid system of variable configuration subject to disturbances in its main energy source, namely solar radiation. The predictive algorithm switches between the operating modes of the plant on the basis of minimizing the performance cost function associated to each operating mode and the switching cost function associated to the transition between operating modes. The cost functions depend on the predicted states of the plant. A transition graph is used to reduce the computational cost of the optimization problem; the graph is plotted using the concept of reachability. The main objective of the predictive algorithm is to find the optimal solution to the configuration problem and the optimal control signal of the plant throughout the day. Experimental results show that solar energy was used during most of the test and cooling demand was satisfied.

Modeling and Feedback Control for Resource Allocation and Performance Analysis in Container Terminals

A dynamic discrete-time model of container flows in maritime terminals is proposed as a system of queues. Such queues are controlled via input variables that account for the use of the available resources given by the capacities of the handling machines used to move containers inside a terminal. Two feedback control strategies for the allocation of such resources are described. The first consists of a resource assignment that is proportional to the corresponding queue lengths; in the second, the assignment is obtained by the one-step-ahead optimization of a performance cost function according to a myopic approach. Simulation results are reported to compare such methodologies for the purpose of sensitivity and scenario analyses in the management of a maritime terminal.