Robust scaling parameters for composite dispatching rules

• Published in: IIE transactions 2010-11, Vol.42 (11), p.842-853
• Main Authors: Chen, Jenny Yan, Pfund, Michele E., Fowler, John W., Montgomery, Douglas C., Callarman, Thomas E.
• Format: Article
• Language: English
• Subjects: apparent tardiness cost with setups rule; Case studies; Composite dispatching rules; Computation; Cubes; Design factors; Dispatching rules; experimental design; Mathematical analysis; mixture experiments; Operations research; Placement; Production scheduling; robust scaling parameters; Scheduling; Standard deviation; Studies
• ISSN: 0740-817X; 2472-5854; 1545-8830; 2472-5862
• DOI: 10.1080/07408171003685825

The successful implementation of composite dispatching rules depends on the values of their scaling parameters. A unified four-phase method to determine robust scaling parameters for composite dispatching rules is proposed, with the goal of achieving reasonably good scheduling performance with the least computational effort in implementation. In phase 1, factor ranges that characterize the problem instances in each tool group (one or more machines operating in parallel) are calculated. In phase 2, a face-centered cube design is used to decide the placement of design points in the factor region. The third phase involves using mixture experiments to find good scaling parameter values at each design point. In the last phase, the central point of the area in which all of the good scaling parameters lie is identified with the robust scaling parameter. The proposed method is applied to determine the robust scaling parameter for the Apparent Tardiness Cost with Setups (ATCS) rule to solve the Pm|s jk |Σ w j T j scheduling problem in a case study. The results of this case study show that the proposed method is more efficient and effective than existing methods in the literature. It requires many fewer experiments and achieves more than a 30% improvement in the average scheduling performance (i.e., total weighted tardiness) and more than a 60% improvement in the standard deviation of the scheduling performance.