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Abstract
۱٫ Introduction
۲٫ Background
۳٫ Combining SD and DES for maintenance development
۴٫ Description of the HSBOF
۵٫ Discussion and conclusions
Acknowledgements
References
Abstract
Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research. Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement. Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.
Introduction
Maintenance considerably increases the budget in manufacturing industries. Even though a cost focus belongs to the past and maintenance has shifted towards being an organizational strategic capacity (Simões, Gomes & Yasin, 2011), the tradeoff between invested costs and their benefits is still of great concern for decision makers. A cost focus leads to reactive maintenance, which according to Geary, Disney and Towill (2006), potentially leads to increased disruption in real-world supply chains, causing excess variance in performance. Recent developments in terms of increased automation, more expensive equipment, and more complex production systems have required larger capital tied up in assets (Garg & Deshmukh, 2006), and proactive maintenance policies are therefore considered a necessity (Pinjala, Pintelon & Vereecke, 2006). Nonetheless, identifying appropriate practices and implementing sound strategies for developing maintenance performance are still non-trivial. A clear measure of this is the frequently-emphasized gap between theory and practice in the maintenance optimization literature (e.g. Fraser, Hvolby and Tseng (2015), Linnéusson, Ng and Aslam (2018a). One aspect of this gap is that little attention has been paid to making model results understandable to practitioners (Dekker, 1996, p.235). Moreover, Woodhouse (2001) identifies the organizational capabilities to manage the implementation of sustainable maintenance practices a crucial limiting factor. According to Baldwin and Clark (1992), capabilities such as identifiable combinations of skills, procedures, physical assets, and information systems are sources of superior performance.