卫星数传接收规划模型与算法研究

作为对地观测卫星任务执行的两个重要阶段之一,数传接收的规划任务是一个具有多时间窗口、多优化目标和多资源约束的NP-Hard优化问题。中继星的引入为数据全天候近实时传输提供可能,同时也为数传规划提出新的问题。本文主要完成两项工作:第一,建立风险控制的卫星数传接收规划模型;第二,阐述基于遗传禁忌的模型求解方法,进一步采用分布式并行求解策略,改善了求解算法的收敛速度和鲁棒性。最后,通过STK提供基础仿真数据,验证了本文规划模型和求解算法的有效性。     

基于约束满足问题的中继卫星调度问题研究

中继卫星任务规划与调度是中继卫星系统应用中的重要问题。根据航天器的空间轨道参数，得到中继卫星与用户航天器之间的可见时间窗口。在此基础上，通过分析中继卫星系统中各种资源之间的约束关系、任务优先级与调度准则，建立中继卫星系统的任务调度模型。仿真结果表明，基于约束规划理论建立中继卫星调度模型是解决中继卫星调度问题的有效方法。     

RFID-enabled track and traceability in job-shop scheduling environment

This study presents an RFID-based traceability approach to improve production scheduling. An in-depth study for a manufacturer is conducted to explore scheduling options enabled by an RFID-based traceability system. We propose a novel information visibility-based scheduling (VBS) rule that utilizes information generated from the real-time traceability systems for tracking work in processes (WIPs), parts and components, and raw materials to adjust production schedules. We then evaluate the performance of this information visibility-based schedule against the classical scheduling rules. The results of the simulation suggest that an RFID-based scheduling rule generates better performance compared to traditional scheduling rules with regard to cycle time, machine utilizations, backlogs, and penalty costs. We also observe that the value of this information visibility is more relevant when the demand varies widely and/or operational disruptions occur.     

A simulation model for determining variable worker requirements in a service operation with time-dependent customer demand

In a service operation where worker requirements have to be determined for short scheduling time periods with nonstationary customer demand, the assumptions necessary for applying steady-state solutions to elementary queueing models are usually violated. This paper describes a simulation study of the behavior of such a service operation. The results are compared with the steady-state solutions to a queueing model where individual scheduling time periods are assumed to be independent. It is found that if the system utilization is below a derived maximum value (based on a service level criterion), then the steady-state solutions are robust enough to explain the behavior of the system and can be used to schedule worker requirements.     

Integration of production planning and scheduling using an expert system and a genetic algorithm

In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.     

A new approach for scheduling independent tasks with multiple modes

Heuristic algorithms for scheduling tasks with multiple modes and minimizing the schedule length involve in general two distinct phases, task mode assignment and then task scheduling. We propose a novel approach where these two features are managed in an integrated mechanism with mode assignment embedded in scheduling. The problem is first reformulated as a special single-mode task scheduling problem, and then is modeled as a graph interval T-coloring. Finally, a tabu-like metaheuristic is proposed for this latter graph coloring problem, and the performance of our approach is compared to known multi-mode scheduling heuristics.     

小批量随机块坐标下降算法

针对机器学习中广泛存在的一类问题：结构化随机优化问题（其中“结构化”是指问题的可行域具有块状结构,且目标函数的非光滑正则化部分在变量块之间是可分离的）,我们研究了小批量随机块坐标下降算法（mSBD）。按照求解非复合问题和复合问题分别给出了基本的mSBD和它的变体,对于非复合问题,分析了算法在没有一致有界梯度方差假设情况下的收敛性质。而对于复合问题,在不需要通常的Lipschitz梯度连续性假设条件下得到了算法的收敛性。最后通过数值实验验证了mSBD的有效性。     

Greedy scheduling with custom-made objectives

We present a methodology to automatically generate an online job scheduling method for a custom-made objective and real workloads. The scheduling problem comprises independent parallel jobs and parallel identical machines and occurs in Massively Parallel Processing systems and computational Grids. The system administrator defines the scheduling objective that may consider job properties and priorities of users or user groups. Our scheduling method combines a Greedy scheduling algorithm with the dynamic sorting of the waiting queue. This sorting algorithm uses a criterion that is modifiable by a set of parameters. Finding good parameter settings for the sorting criterion is viewed as a nonlinear optimization problem which is solved with the help of Evolution Strategies. We evaluate our scheduling method with real workload data and compare it to approximated optimal offline solutions and to the online results of the standard EASY backfill algorithm.     

Learning heuristics for basic block instruction scheduling

Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block—a straight-line sequence of code with a single entry point and a single exit point—subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic. The heuristic is usually hand-crafted, a potentially time-consuming process. In contrast, we present a study on automatically learning good heuristics using techniques from machine learning. In our study, a recently proposed optimal basic block scheduler was used to generate the machine learning training data. A decision tree learning algorithm was then used to induce a simple heuristic from the training data. The automatically constructed decision tree heuristic was compared against a popular critical-path heuristic on the SPEC 2000 benchmarks. On this benchmark suite, the decision tree heuristic reduced the number of basic blocks that were not optimally scheduled by up to 55% compared to the critical-path heuristic, and gave improved performance guarantees in terms of the worst-case factor from optimality.     

Optimal coordination of directional over-current relays using informative differential evolution algorithm

Growing interconnection in distribution system creates new problem for protection engineers. Particularly the design of overcurrent relay coordination in such system is an independent area of research. With the availability of new artificial based optimization algorithm relay coordination research gain a new momentum. Well established artificial based optimization algorithm such as genetic and particle swam optimization are successfully applied for such applications. This paper discusses the application of informative differential evolution algorithm with self adaptive re-clustering technique for selection of TDS and PSM for optimal coordination of directional overcurrent relays. Both continuous as well as discrete version of informative differential evolution algorithm are used for optimization of relay setting. Proper combination of backup relays for each primary relay are identified by using LINKNET graph theory approach. Coordination of directional overcurrent is developed for 9 bus and IEEE 30 bus distribution systems. The aim of problem is to minimize the total operating time of primary relays and eliminate the miscoordination among the primary and backup relay pairs. Discrete types of settings for electromechanical types of relay are also discussed in this paper. Moreover, the relay coordination problem is modified for providing optimal coordination time interval between 0.2 and 0.8 s among all primary and backup relays pairs. The results are compared with hybrid of genetic algorithm – nonlinear programming and sequential quadratic programming. Digsilient power factory software is used for verification of result.     

An interactive,computer-aided ship scheduling system

This paper is concerned with a fleet scheduling and inventory resupply problem faced by an international chemical operation. The firm uses a fleet of small ocean-going tankers to deliver bulk fluid to warehouses all over the world. The scheduling problem centers around decisions on routes, arrival/departure times, and inventory replenishment quantities. An interactive computer system was developed and implemented at the firm, and was successfully used to address daily scheduling issues as well as longer range planning problems. The purpose of this paper is to first present how the underlying decision problem was analyzed using both a network flow model and a mixed integer programming model, and then to describe the components of the decision support system developed to generate schedules. The use of the system in various decision making applications is also described.     

A model of the relay valve used in an air brake system

The mathematical model governing the response of the relay valve in an air brake leads to a hybrid system in which different governing equations apply to different phases of the response of the air brake. To accurately describe the brake’s response characteristics it is imperative to take into account this hybrid structure, and it is to this aspect of the problem that this paper is addressed. The safe operation of any vehicle on the road depends, amongst other things, on a properly operating brake system. Most commercial vehicles such as trucks, tractor–trailers, buses, etc., are equipped with an air brake system. Any defect in a brake system can degrade its performance seriously and can lead to accidents. It is desirable and also important to develop systems that can control and diagnose air brake systems in order to both sustain and improve their performance. One approach to develop such systems is by obtaining a model of the air brake system and then using the same in the design process. The air brake system currently used in commercial vehicles can be broadly divided into a pneumatic subsystem and a mechanical subsystem. One of the main components in the pneumatic subsystem is the relay valve which operates the brakes on the rear axles of a tractor and the axles of a trailer. A relay valve has different modes of operation and the pressure response of the relay valve can be naturally described as the response of a hybrid system. In this article, we develop a hybrid dynamical model to predict the pressure response of the relay valve. An air brake testing facility has been set up at Texas A&M University and this model will be corroborated against experimental data obtained from the same.     

Architecture of manufacturing scheduling systems: Literature review and an integrated proposal

This paper deals with the development of customised and realistic manufacturing scheduling systems. More specifically, we focus onto a key element that may help driving their efficient design and implementation: i.e., the set of building blocks that should include a generic scheduling system and its interconnections, a set collectively known as the architecture of a system. To do so, we first analyse existing contributions on the topic together with papers describing different functional requirements of scheduling systems. These contributions are then discussed and classified, and a modular architecture for manufacturing scheduling systems is proposed. This proposal updates, extends and refines the well-known architecture proposed earlier by Pinedo and Yen’s [Pinedo, M.L., Yen, B.P.-C., 1997. On the design and development of object-oriented scheduling systems. Annals of Operations Research 70 (1), 359–378], and serves to integrate the different requirements identified in the literature review.     

An integrated staff-sizing approach considering feasibility of scheduling decision

This paper presents an integrated staff-sizing system for analyzing and determining workforce management policies with consideration of staff flexibility in service organizations, which addresses and captures the integrated requirements between long-term manpower planning and short-term staff scheduling in the service sector. Multiple Objective Linear Programming (MOLP) is applied to optimize several diversified goals. Solution methods to the MOLP models for the staff planning and staff scheduling are developed respectively, then a solution approach is proposed to iteratively revise the unacceptable staff-sizing plan or scheduling plan. Finally, an example of nurse sizing is analyzed and computational studies are carried out to investigate managerial insights.     

On the evolution of an intelligent maintenance optimization system

In this paper the author reviews the development of an intelligent maintenance optimization system over the past 16 years. The paper starts with discussion of the initial motivation behind developing the system and the designs of the early versions of a computer program to access maintenance history data and provide an analysis. The concept behind this system was gradually developed to incorporate a rule base for the selection of a suitable model for preventive maintenance (PM) scheduling and then to a fully developed knowledge-based system for decision support. The need to incorporate case-based reasoning thus creating a hybrid system that can learn with use in addition to using elicited knowledge from experts is discussed. The experience with system validation with two versions of the system is analysed. The paper also reviews the extensive fundamental work on developing appropriate PM models that can deal with real data patterns. Finally, the scope for future development is presented.     

Leakage detection and location in gas pipelines through an LPV identification approach

A new approach to gas leakage detection in high pressure distribution networks is proposed, where two leakage detectors are modelled as a linear parameter varying (LPV) system whose scheduling signals are, respectively, intake and offtake pressures. Running the two detectors simultaneously allows for leakage location. First, the pipeline is identified from operational data, supplied by REN-Gasodutos and using an LPV systems identification algorithm proposed in [1]. Each leakage detector uses two Kalman filters where the fault is viewed as an augmented state. The first filter estimates the flow using a calculated scheduling signal, assuming that there is no leakage. Therefore it works as a reference. The second one uses a measured scheduling signal and the augmented state is compared with the reference value. Whenever there is a significant difference, a leakage is detected. The effectiveness of this method is illustrated with an example where a mixture of real and simulated data is used.     

Using a multi-agent approach to optimise the train coupling and sharing system

This paper discusses a multi-agent scheduling system using the example of the railway transport system train coupling and sharing (TCS). The scheduling system includes the planning of travel units and the optimisation of solutions. The planning process is implemented as an incremental anytime algorithm that is capable of integrating new task specifications into the ongoing planning process. The initial solution is then optimised by a simulated trading approach. Furthermore, the multi-agent approach is tested on a practical example. The operational parameters and boundaries of the TCS-system are changed to get a comparable rail operation schedule.     

An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem

The Distributed and Flexible Job-shop Scheduling problem (DFJS) considers the scheduling of distributed manufacturing environments, where jobs are processed by a system of several Flexible Manufacturing Units (FMUs). Distributed scheduling problems deal with the assignment of jobs to FMUs and with determining the scheduling of each FMU, in terms of assignment of each job operation to one of the machines able to work it (job-routing flexibility) and sequence of operations on each machine. The objective is to minimize the global makespan over all the FMUs. This paper proposes an Improved Genetic Algorithm to solve the Distributed and Flexible Job-shop Scheduling problem. With respect to the solution representation for non-distributed job-shop scheduling, gene encoding is extended to include information on job-to-FMU assignment, and a greedy decoding procedure exploits flexibility and determines the job routings. Besides traditional crossover and mutation operators, a new local search based operator is used to improve available solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms for distributed scheduling and evaluated with satisfactory results on a large set of distributed-and-flexible scheduling problems derived from classical job-shop scheduling benchmarks.     

Viewing scheduling as an opportunistic problem-solving process

In a search for more efficient yet effective ways of solving combinatorially complex problems such as jobshop scheduling, we move towards opportunistic approaches that attempt to exploit the structure of a given problem. Rather than adhere to a single problem-solving plan, such approaches are characterized by almost continual surveillance of the current problem-solving state to possibly modify plans so that activity is consistently directed toward those actions that currently seem most promising. Opportunistic behavior may occur in problem decomposition down to selective application of scheduling heuristics. We developed and performed limited testing of a scheduling system, called OPIS 0, that exhibits such behavior to some extent. The results are encouraging when compared to ISIS and a dispatching system. It is believed that such opportunistic views of scheduling would lead to systems that allow more flexibility in terms of designing scheduling procedures and supporting the scheduling function.This research is sponsored in part by the Air Force Office of Scientific Research under Contract No. F49620-82-K-0017 and the Westinghouse Corporation.     

Building Better Nurse Scheduling Algorithms

The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence build better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.