Optimal Scheduling for Wireless On-Demand Data Packet Delivery to High-Speed Trains

Test scheduling is a major problem in system-on-a-chip (SOC) test automation. We present an integrated framework that addresses several important test scheduling problems. We first present efficient techniques to determine optimal SOC test schedules with precedence constraints, i.e., schedules that preserve desirable orderings among tests. We then present a new algorithm that uses preemption to obtain optimal test schedules in polynomial time. Finally, we present a new method for determining optimal power-constrained schedules. Experimental results for a representative SOC show that test schedules can be obtained in reasonable CPU time for all cases.

We present a jointly optimal selection and scheduling scheme for the lossy transmission of frames governed by a dependency relation and a delay constraint over a link with limited capacity. A main application for this is scalable video streaming. Our objective is to select a subset of frames and decide their transmission schedule such that the overall video quality at the receiver is maximized. The problem is solved for two of the most common classes of dependency structures for video encoding, which include as a special case the popular hierarchical dyadic structure. We formally characterize the structural properties of an optimal transmission schedule in terms of frame dependency. It is shown that regardless of the subset of frames selected for transmission, any optimal schedule has an equivalent canonical form that is a subsequence of a unique universal sequence containing all frames. The canonical form can be computed efficiently through the construction of a dependency tree. This leads to separable but jointly optimal frame selection and scheduling algorithms that have quadratic computational complexity in the number of frames. Simulation with video traces demonstrates that the optimal scheme can substantially outperform existing suboptimal alternatives.

In the Digital Age, many new tools have emerged through the use of advanced monitoring systems and relatively low cost, powerful computation. In particular, system Reliability increases have been achieved through various applications of remote monitoring. Mathematical optimization applied to scheduling has been used in many industries to lower costs successfully, generally as a planning tool. More recently, it has found application in day-to-day operational planning for the heavy industries of power generation and oil and gas, since computation time has decreased down to minutes instead of days. Combining this with the powerful improvements in statistical and machine learning forecasting, the intersection of these domains has resulted in operational optimization. Combining maintenance planning models into the operational optimization framework provides operators with the opportunity to extend maintenance intervals purely based on optimized scheduling, which costs nearly nothing compared with having to purchase new parts to achieve extended time between maintenance. When the application involves drivers utilizing carbon based fuels, it is often true that the optimized schedule results in reduced emissions compared with typical base-case operation, which in many locations can result in further cost reductions. In this paper we propose a framework for scheduling machines at a particular location such that no machine exceeds a certain threshold, t¯, in running time. In particular, the solution attempts to push the facility-wide time between maintenance sessions to some operator defined limit higher than any individual unit’s required time between maintenance. The framework utilizes Mixed Integer Programming with partially stochastic demand forecasts to solve for optimal running schedules through Monte Carlo simulation. Optimality of a running schedule is based on multiple factors including ability to meet the demand forecast, number of starts required, start-reliability and possible over-fire of engines. The resulting set of solutions provide a likelihood of success in reaching a given site level extended time between maintenance. Operators can use this framework to determine how far the time between maintenance can be extended and determine the probability that a given extension can actually be realized at the physical site. This framework can also be used at managerial levels that consider personnel planning and future capital expenditures.

Freshness of information is of critical importance for a host of applications of wireless communications. In order to deliver information from multiple sources in a timely and fair fashion through a wireless channel, we propose optimizing the link scheduling strategy in respect of age of information, which is a newly introduced metric that measures how fresh information is. Specifically, we consider a set of co-channel links, each having a number of packets to be delivered, and address the problem that aims to find the optimal scheduling solution, such that the maximum peak age of information is minimized. We mathematically formulate this so-called min-max peak age scheduling problem (MPASP), and prove it is NP-hard. Theoretical insights including tractable cases and optimality properties are derived. For problem solution, an integer linear programming (ILP) formulation is proposed. We also develop a sub-optimal, but fast, algorithm to solve the problem with better scalability. Numerical study shows that, by employing the optimal schedule, the maximum peak age is significantly reduced in comparison to other classic scheduling strategies such as minimum-time scheduling.

We consider a reader-writer system consisting of a single server and a fixed number of jobs (or customers) belonging to two classes. Class one jobs are called readers and any number of them can be processed simultaneously. Class two jobs are called writers and they have to be processed one at a time. When a writer is being processed no other writer or readers can be processed. A fixed number of readers and writers are ready for processing at time 0. Their processing times are independent random variables. Each reader and writer has a fixed waiting cost rate. We find optimal scheduling rules that minimize the expected total waiting cost (expected total weighted flowtime). We consider both nonpreemptive and preemptive scheduling. The optimal nonpreemptive schedule is derived by a variation of the usual interchange argument, while the optimal schedule in the preemptive case is given by a Gittins index policy. These index policies continue to be optimal for systems in which new writers enter the system in a Poisson fashion.

SummaryModern power systems are being shifted its attention towards distributed generations (DGs). DGs have allured more concern as an auspicious opportunity with considerable economic profits. To minimize the power generation costs of DGs, day‐to‐day operation scheduling is essential. The role of this study is to offer an optimal operation schedule for DG with several energy sources including renewable energy sources (RES), considering economic facets. In order to achieve the cost minimization along with optimal scheduling, an objection function has been formulated and solved using the optimization algorithms. This study aims to present the applications of differential evolution (DE) algorithm and its variants such as opposition‐based differential evolution (ODE), self‐adaptive differential evolution (SaDE), improved differential evolution (IDE), and cultivated differential evolution (CuDE) for scheduling DG optimally. A scheme for optimal scheduling of thermal, wind power, and solar PV generators has been evaluated. The simulations have been carried out on IEEE 14 bus system, IEEE 30 bus system, IEEE 57 bus system, and Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO), as a real part of 62 bus Indian utility system (IUS). The novelty of this study lies in simulating a real‐time system for solving optimal scheduling problem, in that way helping decision makers to choose the optimal operation points. The results indicate that the SaDE outperformed other DE variants by giving the best fitness value and convergence rate.

It is a great challenge to find an optimal one-wafer cyclic schedule for a single-arm multicluster tool that is widely adopted in semiconductor fabrication. Aiming to tackle this significant problem, an optimal scheduling strategy is determined first for each individual tool under the condition that the bottleneck individual tool is transport-bound. Then, by developing a Petri net model with robot waiting being explicitly described to reveal the properties of the entire system, this paper shows that to schedule such a tool optimally is to allocate the robot waiting time properly. Then, this paper presents the necessary and sufficient conditions for the existence of an optimal one-wafer cyclic schedule. Thereafter, an efficient algorithm is developed to check the given conditions and find such a schedule efficiently if existing. Finally, two industrial examples are used to verify that the proposed method is applicable and effective.

A set of tasks {T1, T2, ..., Tr] are to be scheduled on a two-processor computing system. The execution time of each of the tasks is given. Moreover, a partial ordering ≪ over the set of tasks is specified. If Ti ≪ Tj, it is required that the execution of Tj will not begin until the completion of the execution of Ti. Let ω denote the total elapsed time for the execution of all the tasks in the set when an optimal non-preemptive schedule is used. Let ω′ denote the total elapsed time for the execution of all the tasks in the set when an optimal preemptive schedule is used. Trivially, we know that ω ≥ ω′ However, it can be shown that ω′ ≥ 3/4 ω This result can be extended to an n-processor computing system. In this case, ω′ ≥ n+1/2n ω Moreover, these are best possible bounds. A possible interpretation of the results is: The installation of a high speed drum in a two-processor computing system will increase the operation speed of the system by at most 25%. For an n-processor system, the increment is at most 50%. (The installation of a high speed drum enables us to interrupt the execution of tasks at will.)

Phase Change Memory (PCM) is emerging as one of the most promising alternative technology to the Dynamic RAM (DRAM) when building large-scale main memory systems. Even though the PCM is easy to scale, it encounters serious endurance problems. Writes are the primary wear mechanism in the PCM. The PCM can perform 108 to 109 times of writes before it cannot be programmed reliably. In addition, the PCM has high write latency. To prolong the lifetime of the PCM as the main memory and enhance the performance, we propose a Scratch Pad Memory (SPM) based memory mechanism and an Integer Linear Programming (ILP) memory activities scheduling algorithm to reduce the redundant write operations in the PCM. The idea of our approach is to share the data copies among the SPMs, instead of writing back to the PCM main memory each time a modify occurs. Our experimental results show that the ILP scheduling can generate the optimal schedule of memory activities with minimum write operations, reducing the number of write by up to 61%.

This paper is motivated by existing architectures of field programmable gate arrays (FPGAs). To facilitate the design process we present an optimal scheduling algorithm using a very universal framework, where tasks are constrained by precedence delays and relative deadlines. The precedence relations are given by an oriented graph, where tasks are represented by nodes. Edges in the graph are related either to the minimum time or to the maximum time elapsed between the start times of the tasks. This framework is used to model the runtime dynamic reconfiguration, synchronization with an on-chip processor and simultaneous availability of arithmetic units and SRAM memory. The NP-hard problem of finding an optimal schedule satisfying the timing and resource constraints while minimizing the makespan C max , is solved using two approaches. The first one is based on Integer Linear Programming and the second one is implemented as a Branch and Bound algorithm. Experimental results show the efficiency comparison of the ILP and Branch and Bound solutions.

This paper is motivated by existing architectures of field programmable gate arrays (FPGAs). To facilitate the design process we present an optimal scheduling algorithm using a very universal framework, where tasks are constrained by precedence delays and relative deadlines. The precedence relations are given by an oriented graph, where tasks are represented by nodes. Edges in the graph are related either to the minimum time or to the maximum time elapsed between the start times of the tasks. This framework is used to model the runtime dynamic reconfiguration, synchronization with an on-chip processor and simultaneous availability of arithmetic units and SRAM memory. The NP-hard problem of finding an optimal schedule satisfying the timing and resource constraints while minimizing the makespan C max , is solved using two approaches. The first one is based on Integer Linear Programming and the second one is implemented as a Branch and Bound algorithm. Experimental results show the efficiency comparison of the ILP and Branch and Bound solutions.

Approximate optimal periodic scheduling of multiple sensors with constraints

We analyze a human and multi-robot collaboration system and propose a method to optimally schedule the human attention when a human operator receives collaboration requests from multiple robots at the same time. We formulate the human attention scheduling problem as a binary optimization problem which aims to maximize the overall performance among all the robots, under the constraint that a human has limited attention capacity. We first present the optimal schedule for the human to determine when to collaborate with a robot if there is no contention occurring among robots' collaboration requests. For the moments when contentions occur, we present a contention-resolving Model Predictive Control (MPC) method to dynamically schedule the human attention and determine which robot the human should collaborate with first. The optimal schedule can then be determined using a sampling based approach. The effectiveness of the proposed method is validated through simulation that shows improvements.

With increased uncertainties and depressed security margins of distribution networks, the risk of violating load or voltage constraints has increased. The usual risk-prevention method is to set an appropriate optimal schedule of active distribution networks to reserve enough margin without incurring extra operating costs. In this study, an optimal scheduling method for an active distribution network is proposed that allows for a low level of violation risk to minimize operating costs while maintaining good system security. Overload violation risk cost is a financial cost due to the cost of countermeasures needed to avoid overload violation. In this study, the N-0 security regions of overload are set as the constraint that provides the definition and calculation of the bidirectional violation distance of overloading for active distribution networks based on the N-0 security region. The relationship between the violation distance of overloading and the countermeasures against overload violation have been established based on the power flow equations, enabling the calculation of the countermeasures cost. The assessment model for the overload violation risk cost is derived from the cost of countermeasures and probability distributions of nodal power. The point estimation algorithm is introduced to simplify the calculation. The calculation of risk cost is fixed through the conditional value-at-risk. On this basis, a novel optimization scheduling model of active distribution networks that considers risk cost is constructed. Examples of the optimal schedule are designed and the validity of the proposed method is verified.

This paper proposes an optimal day-ahead optimization schedule for gas-electric integrated energy system (IES) considering the bi-directional energy flow. The hourly topology of electric power system (EPS), natural gas system (NGS), energy hubs (EH) integrated power to gas (P2G) unit, are modeled to minimize the day-ahead operation cost of IES. Then, a second-order cone programming (SOCP) method is utilized to solve the optimization problem, which is actually a mixed integer nonconvex and nonlinear programming issue. Besides, cutting planes are added to ensure the exactness of the global optimal solution. Finally, simulation results demonstrate that the proposed optimization schedule can provide a safe, effective and economical day-ahead scheduling scheme for gas-electric IES.