A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance

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+FMOO 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. (C) 2019 Elsevier B.V. All rights reserved.