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Dispatching rule‐based scheduling is a kind of dynamic scheduling commonly used in real world applications. Because of the lack of scheduling objective, it cannot optimize the specific performances at which shop managers aim in the current production period. To overcome the limitations of the dispatching rule‐based scheduling, an iterative learning scheduling scheme is proposed in this paper. A scheduling objective function, which reflects the performance criteria in which the shop managers are most interested, is established and used to guide the optimization of the crucial performances. According to the value of the scheduling objective obtained from the last simulation period, the parameters are adjusted so as to decrease the objective during the next simulation period. Experimental results show that the iterative learning scheduling overcomes the limitations of the dispatching rule‐based scheduling and achieves higher performances.

Work undertaken in collaboration with BPICS, Cincom (UK) Ltd and ICI Engineering supported by funding from the EPSRC (CDP). The project reviewed planning and scheduling procedures in over 30 industrial companies over a two‐year period to establish best practice in shop‐floor scheduling and to identify the key factors for scheduling success. Outlines the research approach briefly to provide a framework for the analysis of scheduling performance by industrial sector and by scheduling tool. This includes a powerful method for the performance measurement of supply‐chain management systems which allows the comparison of effectiveness in different operating environments and when using a variety of scheduling approaches. Important elements of the project were the review and comparison of scheduling performance in conventional MRPII environments (usually a manual activity based on expediting or the use of shop floor control) with that of more recent finite capacity‐based tools and a classification of scheduling approaches. Some clear lessons have been learned. Concludes by presenting these along with an outline of the success factors which underpin effective scheduling performance in the range of best practice companies identified.

The purpose of this paper is to present a scheduling model, scheduling algorithms, and formal model and analysis techniques for concurrency transaction in grid database environment.

Classical transaction models and scheduling algorithms developed for homogeneous distributed architecture will not work in the grid architecture and should be revisited for this new and evolving architecture. The conventional model is improved by three‐level transaction scheduling model and the scheduling algorithms for concurrency transaction is improved by considering transmission time of a transaction, user's priority, and the number of database sites accessed by the transaction as a priority of the transaction. Aiming at the problems of analysis and modeling of the transaction scheduling in grid database, colored dynamic time Petri nets (CDTPN) model are proposed. Then the reachability of the transaction scheduling model is analyzed.

The three‐level transaction scheduling model not only supports the autonomy of grid but also lightens the pressure of communication. Compared with classical transaction scheduling algorithms, the algorithms not only support the correctness of the data but also improve the effectiveness of the system. The CDTPN model is convenient for modeling and analyzing dynamic performance of grid transaction. Some important results such as abort‐ratio and turnover‐time are gotten by analyzing reachability of CDTPN.

The three‐level transaction scheduling model and improved scheduling algorithms with more complex priority are presented in the paper. The paper gives a CDTPN model for modeling transaction scheduling in grid database. In CDTPN model, the time interval of a transition is a function of tokens in input places of the transition.

Machine scheduling plays an important role in most manufacturing industries and has received a great amount of attention from operation researchers. Production scheduling is concerned with the allocation of resources and the sequencing of tasks to produce goods and services. Dispatching rules help in the identification of efficient or optimized scheduling sequences. The purpose of this paper is to consider a data mining‐based approach to discover previously unknown priority dispatching rules for the single machine scheduling problem.

In this work, the supervised statistical data mining algorithm, namely Bayesian, is implemented for the single machine scheduling problem. Data mining techniques are used to find hidden patterns and rules through large amounts of structured or unstructured data. The constructed training set is analyzed using Bayesian method and an efficient production schedule is proposed for machine scheduling.

After integration of naive Bayesian classification, the proposed methodology suggests an optimized scheduling sequence.

This paper analyzes the progressive results of a supervised learning algorithm tested with the production data along with a few of the system attributes. The data are collected from the literature and converted into the training data set suitable for implementation. The supervised data mining algorithm has not previously been explored in production scheduling.

The purpose of this paper is to develop an attitude control strategy for the reusable boosted vehicle with large angle of attack, and to remove the cross coupling among roll, pitch and yaw channels.

The coordinated gain scheduling control strategy consists mainly of two parts. First, initially ignoring dynamic coupling, single channel gain scheduling controller is designed based on linearized models, respectively. Second, with respect to main channel gain scheduling controller, coordinated scheduling controller is used to generate intentionally cross coupling to partly cancel inter‐channel cross coupling of reusable boosted vehicle.

A coordinated gain scheduling control strategy is presented, and no such analytical solution can be found for the reusable boosted vehicle.

The design idea of coordinated gain scheduling strategy is straightforward in physical concepts and has great value for engineering applications.

Coordinated gain scheduling control strategy is novel in that single channel gain scheduling design does not involve small perturbation linearization and coordinated channel is scheduled.

Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates mathematical programming approaches for addressing the maintenance scheduling problem. Gives examples to demonstrate the utility of these approaches. Proposes expansion of the state‐of‐the‐art maintenance management information system to utilize the mathematical programming approaches and to have better control over the maintenance scheduling problem.

In this paper we consider the use of ontologies as the basis for structuring and simplifying the process of constructing real‐time problem‐solving tools, focusing specifically on the task of production scheduling. In spite of the commonality in production scheduling system requirements and design, different scheduling environments invariably present different challenges (e.g. different constraints, different objectives, different domain structure, etc.). The proposed methodology for building ontologies used for production scheduling systems represents a synthesis of extensive work in developing constraint‐based scheduling models for a wide range of applications in manufacturing and production planning. Since the effective modeling is one of the most important and difficult steps in the development of reliable information systems, and taking into consideration the fact that the general problem of the production scheduling in the industries is very difficult and still unsolved, one can easily estimate the merit of this methodology.

Constructs a knowledge‐based simulation model for job shop scheduling simulation techniques to build the initial status of the knowledge base. Uses the simulation experiments to study the effect of technological factors, such as machine loading conditions and criteria of scheduling performance requirements, on the selection of scheduling priority rules. Implements the model with real workshop data to test its validity to cater for real‐world situations such as machine group centres, more than one machine and the availability of operators. The proposed model yields satisfactory results.

Service operations managers often attempt to improve labour‐scheduling efficiency through the use of one or more types of scheduling flexibility. Part‐time labour, for example, is one type of scheduling flexibility which is used in many service operations. The use of part‐time workers increases productivity by providing a closer match between the forecast labour requirements and the amount of labour actually scheduled. Demonstrates the feasibility of implementing a microcomputer‐based approach to workforce‐scheduling problems which contain a substantial degree of scheduling flexibility. This approach has particular application to small service firms which may have, or can obtain, microcomputers to schedule their operating personnel. The actual scheduling procedure is designed to produce daily and weekly work schedules simultaneously (i.e. tour schedules) for workers assumed to be of homogeneous skill and ability, such as those employed in a fast‐food restaurant. The application of such an approach in the microcomputer environment would have a significant impact on the ability of small service establishments further to enhance labour utilization through the increased use of workforce‐scheduling flexibility. Clearly demonstrates that a microcomputer‐based workforce‐scheduling model which incorporates a substantial amount of scheduling flexibility, can and should be developed for use in today′s small service organizations.