Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems

Abstract

The primary objective of this paper is to compare five rescheduling strategies according to their effectiveness in reducing entropic-related complexity arising from machine breakdowns in manufacturing systems. Entropic-related complexity is the expected amount of information required to describe the state of the system. Previous case studies carried out by the authors have guided computer simulations, which were carried out in Arena 5.0 in combination with MS Excel. Simulation performance is measured by: (1) entropic-related complexity measures, which quantify: (a) the complexity associated with the information content of schedules, and (b) the complexity associated with the variations between schedules; and (2) mean flow time. The results highlight two main points: (a) the importance of reducing unbalanced machine workloads by using the least utilised machine to process the jobs affected by machine breakdowns, and (b) low disruption strategies are effective at reducing entropic-related complexity; this means that applying rescheduling strategies in order to manage complexity can be beneficial up to a point, which, in low disruption strategies, is included in their threshold conditions. The contribution of this paper is two-fold. First, it extends the application of entropic-related complexity to every schedule generated through rescheduling, whereas previous work only applied it to the original schedule. Second, recommendations are proposed to schedulers for improving their rescheduling practice in the face of machine breakdowns. Those recommendations vary according to the manufacturing organisations’ product type and scheduling objectives. Further work includes: (a) preparing a detailed workbook to measure entropic-related complexity at shop-floor level; and (b) extending the analysis to other types of disturbances, such as customer changes.

Keywords:

• FMS
• simulation
• dynamic scheduling
• rescheduling
• lean manufacturing
• manufacturing systems

Acknowledgements

The authors gratefully acknowledge Engineering and Physical Sciences Research Council (EPSRC) support including grants GR/M52458 and 98316556, and Overseas Research Scholarship (ORS) for award 1999032139. The authors would also like to acknowledge Gerry Frizelle and his research team at the Institute for Manufacturing at the University of Cambridge for their support with the development of the computer simulations. The authors thank the referees for their feedback comments that contributed to an improvement of the paper.