Dynamic Rule Selection Research Articles

A novel complex network based dynamic rule selection approach for open shop scheduling problem with release dates.

In the open shop scheduling problem, resources and tasks are required to be allocated in an optimized manner, but when the arrival of tasks is dynamic, the problem becomes much more difficult. To solve large scale open shop scheduling problem with release dates, heuristic algorithms are more promising compared with metaheuristic algorithms. In this paper, a framework of general scheduling object is developed, under which open shop scheduling problem is described. Then, a complex scheduling network model for open shop scheduling problem is established, and the problem is transformed into reasonably arranging the node traversal order with the goal of traversing all nodes in the network as quickly as possible, on condition that each node has a traversal time and only disconnected nodes can be traversed simultaneously. Considering that network structural features and local time attributes of nodes can provide heuristic information, six single heuristic rules are raised and a novel complex network based dynamic rule selection approach is proposed to solve dynamic open shop problem by switching dynamically the scheduling rules based on real-time production status. Finally, two experiments are carried out and the experimental results show that the proposed heuristic rules have acceptable performance, and the proposed complex network based dynamic rule selection approach is feasible.

NOVEL HYBRID RULE NETWORK BASED ON TS FUZZY RULES

A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classication and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically com- bine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.

Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation

Computational efficiency is one of the major challenges of applying simulation to short-term operation scheduling of semiconductor wafer fabrication factories (fabs), which are characterized by re-entrant process flows, stringent production control requirements and fast changing technology and business environments. The paper explores the application of the ordinal optimization (OO)-based simulation technique to efficiently selecting good rules for scheduling wafer fabrications. An efficient simulation tool, which makes use of OO and optimal computing budget allocation techniques, is developed. Experiments with the OO-based simulation tool are conducted for static selection of good rules under different factors such as initial state, performance index and time horizon. Results indicate that one to two orders of computation time reduction over traditional simulations can be achieved and that what rules are good varies with factors of initial state, performance index and time horizon. These results motivate our further investigation about applications to dynamic selection of dispatching rules upon the occurrence of two significant uncertain events: holding of significant amount of wafers-in-process due to engineering causes and major machine failures. Results demonstrate the value of dynamic rule selection for uncertainty handling, the insightful selection of good rules and the needs for future research.

Parsing with dynamic rule selection

Although many linguistic theories and formalisms have been developed and discussed on the problem of parsing algorithm in the past decades, the efficiency and accuracy of parsing are still serious problems in practical machine translation systems. This paper presents a parsing algorithm with dynamic rule selection and the experimental results. By describing the design and practice of the improved algorithm, the paper discusses in detail the designing method for a high speed and efficient parser.

Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach

This paper develops an adaptive scheduling policy for dynamic manufacturing systems. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. The inductive learning methodology used for constructing this state-dependent scheduling policy also provides an understanding of the relative importance of the various system parameters in determining the appropriate dispatching rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tight due date jobs and machine breakdowns.

Dynamic, computer-assisted interpretation of infrared spectra of condensed-phase mixtures

A knowledge-based system, MIXIR, designed to assist researchers in interpreting the infrared spectra of condensed-phase mixtures is described. This system overcomes many of the inherent limitations of static-rule-based systems. An iterative problem-solving approach is permitted, making use of information gained during the interpretation process. The dynamic rule selection and significance evaluation alos allow the user to provide information on the sample matrix, or to select a subset of the compounds for rule derivation. The MIXIR system was evaluated with a knowledge base compiled using IRBASE, a complementary program, and a test set of 20 mixtures.