Applying Robust Fuzzy-stochastic Programming Model in Multi-objective Optimization for Flow-shop Scheduling with Parallel Machines: a Real-life Case Study in an Engine Component Industrial

Resources scheduling, jobs sequencing, and resources assignment are considered the brain activities that play an extremely important role to the business, such as flow-shop scheduling. Additionally, the internal manufacturing environment and the current market are drastically changing, in which attracted significant attention in scheduling under uncertainties. This research aims to investigate the multi-objective flow-shop scheduling with parallel machines under machine breakdown and operational uncertainty. A novel robust fuzzy stochastic programming (RFSP) model is presented to address the unexpected operational disruptions and the inherent uncertainty of processing time. The objective is to optimize the cost of late delivery and the efficiency of manufacturing evaluated by overall equipment effectiveness (OEE) whilst considering the CO2 emissions produced. Eventually, a proposed algorithm is applied with a real-life scheduling data from a German factory that faces a practical situations in machine breakdowns and operational risks with unrelated parallel machines. Thus, this study intends to provide the computational results and useful information for multiple decision processes.