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Many regression-based methods to date have been proposed for solving the due date assignment (DDA) problem. The advantages of regression-based DDA methods are that they are easy to both put into practice and comprehend. However, relatively little scheduling research has focused on improving the performances of regression-based DDA methods. The performance of a regression-based DDA method could be improved if its values of regression coefficients could provide a more accurate and precise flowtime estimation for each individual job. The difficulty in doing this stems from the dynamic and stochastic nature of production environment that precludes accurate estimation. Therefore, the aim of this study is to suggest a particular methodology for setting the regression coefficients to improve the performance of regression-based DDA method. In particular, the regression-based DDA method achieved by our suggested methodology is able to adjust the values of coefficients dynamically to best predict the job due date based on the condition of the shop at the instant of job entry. To evaluate the robustness of the methodology, an experimental design was used with four regression coefficient determining procedures, two shop models, and three dispatching rules. The results of this investigation clearly indicate that significant improvements in the performance of regression-based DDA method can occur when the suggested methodology is used. [ABSTRACT FROM AUTHOR]

In this study a novel case indexing approach is proposed for case-based reasoning (CBR). This new approach, called the tree-indexing approach, is a modified form of the inductive learning-indexing (IL-indexing) approach and is especially applied to assist CBR in numeric prediction. The tree-indexing approach organizes the cases in the memory by inducting a tree-shaped structure, in order to improve the efficiency and effectiveness of case retrieval. The experiments, using three real world problems from the UCI repository, show that the CBR with the tree-indexing approach (T-CBR) is superior to the conventional CBR. This study also applies T-CBR for solving the due date assignment problem in a dynamic job shop environment in order to investigate whether T-CBR's expected benefits can be observed in practice. The results of the experiments show that our proposed T-CBR can indeed more accurately predict the job due date than the other methods presently in use. [ABSTRACT FROM AUTHOR]

Due date assignment (DDA) is the first important task in shop floor control. Due date-related performance is impacted by the quality of the DDA rules. Assigning order due dates and delivering the goods to the customer on time will enhance customer service and provide a competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN), is adopted to model new due date assignment rules. An ANN-based DDA rule, combined with simulation technology and statistical analysis, is presented. Whether or not the ANN-based DDA rule can outperform the conventional and Reg-based DDA rules taken from the literature is examined. The interactions between the DDA, order review/release (ORR), and dispatching rules significantly impact upon one another, and it is therefore very important to determine a suitable DDA rule for the various combinations of ORR and dispatching rules. From the simulation and statistical results, the ANN-based DDA rules perform better in due date prediction. The ANN-based DDA rules have a smaller tardiness rate than the other rules. ANN-based DDA rules have a better sensitivity and variance. Therefore, if system information is not difficult to obtain, the ANN-based DDA rule can perform a better due date prediction. This paper provides suggestions for DDA rules under various combinations of ORR and dispatching rules. ANN-Sep is suitable for most of these combinations, especially when ORR, workload regulation (WR) and two boundaries (TB), rules are adopted. [ABSTRACT FROM AUTHOR]

A new multi-objective heuristic algorithm for resolving the facility layout problem is presented in this paper. It incorporates qualitative and quantitative objectives and resolves the problem of inconsistent scales and different measurement units. We consider suboptimal solutions since optimal methods are computationally unfeasible to large layout problems. In this paper, we develop the dominant index (DI) and incorporate it in our heuristic algorithm to guarantee the quality of proposed solutions. Moreover, a new measure of solution quality, dominant probability ( D p), is offered to determine the probability that one layout is better than the others. Computational results show that our proposed heuristic algorithm is an efficient method for obtaining good-quality solutions. [ABSTRACT FROM AUTHOR]

Due date assignment is an important task in shop floor control, affecting both timely delivery and customer satisfaction. Due date related performances are impacted by the quality of the due date assignment methods. Among the simple and easy to implement due date assignment methods, the total work content (TWK) method achieves the best performance for tardiness related performance criteria and is most widely used in practice and in study. The performance of the TWK method can be improved if the due date allowance factor k could render a more precise and accurate flowtime estimation of each individual job. In this study, in order to improve the performance of the TWK method, we have presented a model that incorporated a data mining tool - Decision Tree - for mining the knowledge of job scheduling about due date assignment in a dynamic job shop environment, which is represented by IF-THEN rules and is able to adjust an appropriate factor k according to the condition of the shop at the instant of job arrival, thereby reducing the due date prediction errors of the TWK method. Simulation results show that our proposed rule-based TWK due date assignment (RTWK) model is significantly better than its static and dynamic counterparts (i.e., TWK and Dynamic TWK methods). In addition, the RTWK model also extracted comprehensive scheduling knowledge about due date assignment, expressed in the form of IF-THEN rules, allowing production managers to easily understand the principles of due date assignment . [ABSTRACT FROM AUTHOR]