Abstract
This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration. After describing the simulation-based innovization using data mining procedure and its difference from conventional simulation analysis methods, results from an industrial case study carried out for the improvement of an assembly line in an automotive manufacturer will be presented.
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Notes
- 1.
Cycle time, which is also called variously as manufacturing lead time, throughput time or sojourn time, is used in this paper to refer to the time from a job is released at the beginning of the line/system until it reaches its end (i.e., the time a part spends as WIP). This terminology follows the definition found in standard textbooks for manufacturing systems analysis, e.g., [36].
- 2.
Defined in the shifting bottleneck detection method [41], active time is the time when the machine is working, changing tools or in repair and inactive time includes starving, blocked or waiting.
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Acknowledgments
Results presented in this case study are based on parts of the research outcomes of the Factory Analyses in ConcepTual phase using Simulation (FACTS) project (2006–2008) and the FFI-HSO (Holistic Simulation Optimization) project (2009–2012). The authors gratefully acknowledge VINNOVA, Sweden, for the provision of research funding for these two projects.
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Ng, A.H.C., Dudas, C., Nießen, J., Deb, K. (2011). Simulation-Based Innovization Using Data Mining for Production Systems Analysis. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_15
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