Two-Step Hybridized algorithm of Robust Scheduling for Dealing with Uncertain Processing Time

Manufacturing industries commonly face the challenge of production environment variance, including equipment performance issues, differences in personnel's skill levels, and difficulty in managing the outsourced processing time. These problems lead to bias between the actual processing time and standard processing time set by the manufacturer. So, planning the production schedules in terms of the current standard processing time, can't effectively adapt to the environmental uncertainties, which result in unstable order completion delivery date, production waste, and processing delays, severely affect the accuracy and stability of scheduling. To solve this issue, this research proposes a type of robust scheduling method that take the processing time variability into consideration, to deal with normal variations in manufacturing industries' production environment. This method incorporates the consideration of unstable processing time factors based on optimized scheduling. By simulating large number of possibilities of processing scenarios, and combining with Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO), in a phased approach, optimizes the dual key performance indicators such as Relative Penalty Cost (RC) and Relative Average Makespan Variance (RMV). Through experimentation, it has proven that this technology in simulated and actual data, comparing with only considering deterministic schedule of standard processing time, the on-time delivery rate can increase by 30%. Additionally, it reduces scheduling variance by approximately 11 % and 19% respectively, which reduces the cost increase and management difficulties due to processing time variability, improves the stability and efficiency of production scheduling, and ensures a much smoother and more reliable production process.