Real-time Allocation Research Articles

Research on Real-time Intelligent Load Control Technology among Giant Hydropower Station Group under High-intensity Peak-load and Frequency Regulation Demand

The paper conducts an in-depth study on the real-time dispatching involved in joint operation among giant cascade hydropower stations with high-intensity peak-load and frequency regulation demand, and proposes anintelligent load control technology for cascade hydropower stations in the coordination mode of station and power grid. Aiming to water level safety control of runoff power stations and rapid response to load regulation requirements of the power grid, taking 10 types of constraints such as output, water volume and flow rate into consideration, a model cluster is established through the layered control principle to realize real-time intelligent load allocation and economic operation among Pubugou, Shenxigou and Zhentouba stations. Dadu River has become the first large-scale river basin in China to realize “one-key dispatch” of multiple stations, and has achieved good demonstration effect.

Actuator allocation for integrated control in tokamaks: architectural design and a mixed-integer programming algorithm

Plasma control systems (PCS) in tokamaks need to fulfill a number of control tasks to achieve the desired physics goals. In present-day devices, actuators are usually assigned to a single control task. However, in future tokamaks, only a limited set of actuators is available for multiple control tasks at the same time. The priority to perform specific control tasks may change in real-time due to unforeseen plasma events and actuator availability may change due to failure. This requires the real-time allocation of available actuators to realize the requests by the control tasks, also known as actuator management.In this paper, we analyze possible architectures to interface the control tasks with the allocation of actuators inside the PCS. Additionally, we present an efficient actuator allocation algorithm for Heating and Current Drive (H&CD) actuators. The actuator allocation problem is formulated as a Mixed-Integer Quadratic Programming optimization problem, allowing to quickly search for the best allocation option without the need to compute all allocation options. The algorithms performance is demonstrated in examples involving the full proposed ITER H&CD system, where the desired allocation behavior is successfully achieved. This work contributes to establishing integrated control routines with shared actuators on existing and future tokamaks.

Assessing the up-peak performance of destination elevator group control systems using real time allocation of landing calls

The boost in the handling capacity of destination elevator group control systems has been previously calculated under idealised conditions, assuming simultaneously minimum values of the highest reversal floor and of the average number of stops in a round trip. The boost has also been calculated under conditions of allocating the landing calls with full advanced knowledge of the passenger destinations (offline allocation). This paper re-calculates the boost in handling capacity under real time allocation conditions of the landing calls as they are registered. The landing calls are revealed to the controller one at a time, and the controller has to make an irrevocable allocation of the landing call to an elevator. The efficiency drops under real time allocation conditions compared to the other two conditions as expected. However, it still reveals a significant boost in handling capacity that increases with the number of elevators in the group.

Assessing the up-peak performance of destination elevator group control systems using real time allocation of landing calls

The boost in the handling capacity of destination elevator group control systems has been previously calculated under idealised conditions, assuming simultaneously minimum values of the highest reversal floor and of the average number of stops in a round trip. The boost has also been calculated under conditions of allocating the landing calls with full advanced knowledge of the passenger destinations (offline allocation). This paper re-calculates the boost in handling capacity under real time allocation conditions of the landing calls as they are registered. The landing calls are revealed to the controller one at a time, and the controller has to make an irrevocable allocation of the landing call to an elevator. The efficiency drops under real time allocation conditions compared to the other two conditions as expected. However, it still reveals a significant boost in handling capacity that increases with the number of elevators in the group.

Diffusion Limit of Fair Resource Control—Stationarity and Interchange of Limits

We study a resource-sharing network where each job requires the concurrent occupancy of a subset of links (servers/resources), and each link’s capacity is shared among job classes that require its service. The real-time allocation of the service capacity among job classes is determined by the so-called “proportional fair” scheme, which allocates the capacity among job classes taking into account the queue lengths and the shadow prices of link capacity. We show that the usual traffic condition is necessary and sufficient for the diffusion limit to have a stationary distribution. We also establish the uniform stability of the prelimit networks, and hence the existence of their stationary distributions. To justify the interchange of two limits, the limit in time and limit in diffusion scaling, we identify a bounded workload condition, and show it is a sufficient condition to justify the interchange for the stationary distributions and their moments. This last result is essential for the validity of the diffusion limit as an approximation to the stationary performance of the original network. We present a set of examples to illustrate justifying the validity of diffusion approximation in resource-sharing networks, and also discuss extensions to other multiclass networks via the well-known Kumar-Seidman/Rybko-Stolyar model.

Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access

Dynamic spectrum access is a new paradigm of secondary spectrum utilization and sharing. It allows unlicensed secondary users (SUs) to exploit the opportunistically underutilized licensed spectrum. Market mechanism is a widely used promising means to regulate the consuming behaviours of users and, hence, achieve the efficient allocation and consumption of limited resources. In this paper, we propose and study a hybrid secondary spectrum market consisting of both the futures market and the spot market, in which SUs (buyers) purchase underutilized licensed spectrum from a spectrum regulator (SR), either through predefined contracts via the futures market, or through spot transactions via the spot market. We focus on the optimal spectrum allocation among SUs in an exogenous hybrid market that maximizes the secondary spectrum utilization efficiency. The problem is challenging because of the stochasticity and asymmetry of network information. To solve this problem, we first derive an off-line optimal allocation policy that maximizes the ex ante expected spectrum utilization efficiency based on the stochastic distribution of network information. We then propose an online Vickrey-Clarke-Groves (VCG) auction that determines the real-time allocation and pricing of every spectrum based on the realized network information and the prederived off-line policy. We further show that with the spatial frequency reuse, the proposed VCG auction is NP-hard; hence, it is not suitable for online implementation, especially in a large-scale market. To this end, we propose a heuristics approach based on an online VCG-like mechanism with polynomial-time complexity, and further characterize the corresponding performance loss bound analytically. We finally provide extensive numerical results to evaluate the performance of the proposed solutions.

Energy Balancing of a Heuristic Algorithm for the Path Planning of Mobile Sensor Nodes

A wireless sensor network (WSN) is one of the core technologies of the Internet of things. It is an important means to realize a real-time geographic information system. Related research has shown that in the future, tens of billions of sensors and intelligent terminal equipment will be connected to WSNs based on the establishment of the function of the Internet of things. This study presents a heuristic algorithm to balance the energy consumption of each sensor node. It proposes a new real-time dynamic allocation algorithm for sensor tasks based on the concept of this heuristic algorithm and by considering that a multisensory system is composed of a phased-array radar. This allocation algorithm can dynamically assign tasks to the most suitable sensor before tasks fail to arrive, which ensures that the sensor can achieve a good load balance and extend network lifetime. A simulation experiment is conducted, and results validate the proposed algorithm. The energy consumption of mobile sensor nodes is effectively balanced. The path-planning algorithm standardizes the energy consumption of each mobile sensor node across the network , thereby effectively prolonging network lifetime.

Allocation Concealment Methods of Alternative Vaccines in Double-blinded Vaccine Plants and Foods Trials

In this study, specific steps are described of concealment allocation methods in double-blinded vaccine plants and foods trials. What is more, comparison among them is also conducted. Damage of injected vaccines during the transportation and storage is common which affects the food vaccination program in plants and foods trials. Thus, there is necessary to prepare a number of alternative vaccines before plants and foods trials. Appropriate amount and method of concealment allocation is important to assure the efficacy of blind and control the cost in double-blinded plants and foods trials. Although two direct allocation methods have been generally used, indirect and online real-time concealment allocation methods are proposed for well comprehensive performance.

Business integration unit (BIU) adapter for industrial global value chain on the web

Today's manufacturing enterprises rarely live in isolation. They need to be connected in order to create products from which a group of enterprises, called global-value-chain (GVC), can derive value. Service-oriented architecture (SOA) and event-driven architecture (EDA) are two different paradigms that address complex integration challenges. Enterprise service bus (ESB) allows for the implementation of both the SOA and the EDA concepts. This paper addresses the development of an enterprise service bus (ESB) to grant the operation of GVC. A proposed business-integrator-unit (BIU) is designed to be plugged in each enterprise system. The BIU contains a “business collaboration map configurator” that allows real time allocation of roles to members’ enterprises.

Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters

Truck-mounted mobile emergency generators (MEGs) are critical flexibility resources of distribution systems (DSs) for resilient emergency response to natural disasters. However, they are currently under-utilized. For better utilization, this paper proposes dispatching MEGs as distributed generators in DSs to restore critical loads by forming multiple microgrids (MGs). As the travel time of MEGs on road networks (RNs) can greatly influence the outage duration of critical loads, a two-stage dispatch framework consisting of pre-positioning and real-time allocation is introduced, and the traffic issue is considered via the vehicle routing problem. Pre-positioning places MEGs in staging locations prior to a natural disaster, while real-time allocation sends MEGs from staging locations to restore critical loads by forming MGs in DSs after the natural disaster strikes. Specifically, with the objective of minimizing the expected outage duration of loads considering their priorities and demand sizes, pre-positioning is done via a scenario-based two-stage stochastic optimization problem, in which the first-stage pre-positioning decisions are evaluated by numbers of second-stage real-time allocation problems corresponding to considered scenarios of DS damage and RN damage/congestion. A scenario decomposition algorithm is applied to solve this problem. Illustrative cases demonstrate the effectiveness of the proposed dispatch scheme and algorithm.

Clinching auctions with online supply

Auctions for perishable goods such as Internet ad inventory need to make real-time allocation and pricing decisions as the supply of the good arrives in an online manner, without knowing the entire supply in advance. In this work, we consider a multi-unit model where buyers have global budget constraints, and the supply arrives in an online manner. Our main contribution is to show that for this setting there is an individually-rational, incentive-compatible and Pareto-optimal auction that allocates these units and calculates prices on the fly, without knowledge of the total supply. We do so by showing that the Adaptive Clinching Auction satisfies a supply-monotonicity property.Finally, while our main technical result concerns multi-unit supply, we propose a formal model of online supply that captures scenarios beyond multi-unit supply and has applications to sponsored search.

Research on Allocation Method of Maintenance Material for Complex Mission

From the analysis on the demand characteristic of maintenance material in wartime, take the maintenance mate- rial allocation of tactical single depot and multi-demand troops without supporting as the research object, according to de- scribing the combat mission, put forward the real time allocation method of maintenance material for complex mission. Then, under the hypothesis that the material demand of ever demand troop is empirical distribution variables in ever phase mission, take the utility value of all maintenance material maximum as the optimization goal. The stochastic optimization model is built to get the material allocation up bound value of ever demand troop in ever phase mission. At last, use the relative model and algorithm given in this paper to an application example, the example shows the availability of the model built.

A discrete-event simulation tool for real-time management of pre-production development fleets in a block-caving project

The economic risk of the construction stage of a mining project results from the uncertainty in its duration and capital expenditure. Block-caving mining projects are characterised by long lead time to production, often longer than 15 years, and very high pre-production capital expenditure, resulting from large pre-production development requirements. As a result, the size and performance of mine development system are the parameters bearing most of the economic risk in the construction stage of block-caving projects. This study summarises the results of a research aiming to develop a management tool for real-time face allocation of equipment and operation performance modelling. The tool combines stochastic discrete-event simulation (DES) and Programme Evaluation Review Technique to optimise the size and performance of mine development system, thus minimising the duration of pre-production development, the highest risk parameter in block-cave projects. Furthermore, the model has an ample application potential in mining operations as a tool to assist management decisions in short-term planning and equipment dispatching system. The model was validated through its application in the Chuquicamata Underground Mining Project, in Chile.

A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System

Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

Diffusion Limit of Fair Resource Control -- Stationarity and Interchange of Limits

We study a resource-sharing network where each job requires the concurrent occupancy of a subset of links (servers/resources), and each link's capacity is shared among job classes that require its service. The real-time allocation of the service capacity among job classes is determined by the so-called "proportional fair" scheme, which allocates the capacity among job classes taking into account both the queue lengths and the shadow prices of link capacity. We show that the usual traffic condition is necessary and sufficient for the diffusion limit to have a stationary distribution. We also establish the uniform stability of the pre-limit networks; and hence, the existence of their stationary distributions. To justify the interchange of two limits, the limit in time and the limit in diffusion scaling, we identify a bounded-workload condition, and show it is a sufficient condition to justify the interchange for both the stationary distributions and their moments. This last result is essential for the validity of the diffusion limit as an approximation to the stationary performance of the original network. We present a set of examples to illustrate justifying the validity of diffusion approximation in resource-sharing networks, and also in a well-known multi-class model, the Kumar-Seidman network.

Ultrasonic Welding Control System Based on Embedded Multitasking Research

To realize RTX, a 32-bit ARM processor platform and Keil was used to provide a graphical interface. Through real-time task allocation and Keil provided by TCP/IP interface, an embedded ultrasonic welding control system was realized. The ultrasonic welding control system designed for the testing of software and hardware, and by comparison with field work, proved that the application of multitasking can obviously improve the welding effect through welding control system, friendly man-machine interface, convenient communication and monitoring.

Airline new customer tier level forecasting for real-time resource allocation of a miles program

This is a case study on an airline’s miles program resource optimization. The airline had a large miles loyalty program but was not taking advantage of recent data mining techniques. As an example, to predict whether in the coming month(s), a new passenger would become a privileged frequent flyer or not, a linear extrapolation of the miles earned during the past months was used. This information was then used in CRM interactions between the airline and the passenger. The correlation of extrapolation with whether a new user would attain a privileged miles status was 39% when one month of data was used to make a prediction. In contrast, when GBM and other blending techniques were used, a correlation of 70% was achieved. This corresponded to a prediction accuracy of 87% with less than 3% false positives. The accuracy reached 97% if three months of data instead of one were used. An application that ranks users according to their probability to become part of privileged miles-tier was proposed. The application performs real time allocation of limited resources such as available upgrades on a given flight. Moreover, the airline can assign now those resources to the passengers with the highest revenue potential thus increasing the perceived value of the program at no extra cost.

Yield and allocation management in a continuous make-to-stock system with demand upgrade substitution

Yield management is crucial for high-tech industries to reduce over-production. However, it is very challenging for a manufacturer to make a yield decision under the circumstance of uncertain demand and stochastic product quality. For example, in semiconductor manufacturing industry, products are produced in batches within certain production cycle time. Even if the same raw material is put into the production system, the product qualities (measured by performance) usually are not identical due to random yield in the manufacturing process. Since products have overlapped performance specifications, products of the same batch are classified into different classes according to their qualities at the allocation stage. Products with higher performance can substitute for demand for products with lower performance. This type of production and allocation system can be specified as a make-to-stock system with single material, multi-products, multi-periods, upward demand substitution under random yield and uncertain demands. In order to maximize profit, the manufacturer needs to decide on the optimal input material quantity and the optimal allocation policy. The problem is formulated as a stochastic dynamic programming model. Because manufacturer׳s profit is affected by the allocation policies, ATP-r allocation policy is proposed to help manufacturer make real time allocation decisions. A numerical experiment shows that ATP-r allocation policy performs better than the traditional Myopic allocation policy. To reduce the computation burden involved in ATP-r allocation policy, we further propose a simplified algorithm and our experimental studies provide proof of the effectiveness of the proposed simple algorithm.

A direct non-buffer rate control algorithm for real time video compression

Rate control (RC) is crucial in controlling compression bitrates and encoding qualities for networked video applications. In this paper, we propose a direct non-buffer real-time rate control algorithm for video encoding, which has two unique features. First, unlike traditional algorithms which adopt buffers in rate control, the proposed algorithm does not use a buffer in rate regulation which can reduce the delay and improve real-time response. Second, we propose a new Proportional-Integral-Derivative (PID) bit controller to directly control encoding bitrates. In addition, we also develop a simple but effective method for real-time target bit allocation. To the best of our knowledge, this is the first work that conducts video rate control without using a buffer. Our extensive experimental results have demonstrated that the proposed algorithm outperforms the MPEG-4 rate control algorithm by achieving more accurate rate regulation and improving overall coding quality.

An Online Load Balancing Algorithm for Virtual Machine Allocation with Fixed Process Intervals

To improve the performance and reliability of resource in Cloud data centers, it is significant to apply load balancing strategy for resource scheduling. In a dynamic changing traffic environment, workload of a Cloud data center has real-time and fixed process time characteristic. One of the challenging scheduling problems in Cloud datacenters is to take the real-time allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. We introduce an Online Load-balance Resource Scheduling Algorithm (OLRSA) for Cloud datacenters considering realtime and multi-dimensional resources. Unlike traditional load balance scheduling algorithms which often do not consider lifecycle and fixed interval constraints, OLRSA treats life cycles and fixed intervals for both physical machines and virtual machines. We develop and apply integrated measurement for each server and a Cloud datacenter. New metrics such as integrated imbalance level, capacity makespan, capacity skew are defined for Cloud data centers. Simulation results show that OLRSA has better performance than a few related load balancing algorithms with regard to total imbalance level, makespan, overall load efficiency as well as capacity makespan.