Performance analysis of production scheduling in Toyota simulation

Abstract

This research analyzes production scheduling performance in the context of sustainable manufacturing using Toyota Production System (TPS) simulation. The primary focus of this study is to study scheduling performance based on the makespan value and job order for each method. To reduce makespan, two metaheuristic techniques are employed: the tabu search (TS) method and the simulated annealing (SA) method. This research fills the literature gap by exploring makespan optimization methods, combining computer simulation with metaheuristics, and considering TPS scheduling constraints. Data obtained from a miniature car simulation based on the Toyota Production System concept. The research method includes SA and TS implementation using Python and Visual Basic 6.0. The results show that SA and TS produce makespan 2.2-3.2% lower than the Initial Method. SA shows flexibility with different job sequences for each level of demand, while TS produces consistent sequences. The increase in makespan as demand increases is consistent across all methods (14.1-16.4%). In conclusion, SA and TS are effective optimization methods for production scheduling, with the selection depending on the preference for flexibility or consistency.

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