A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry - Xie Shone Seen; Darvishi Mondragon Ortiz-Barrios; Osei Scott Kant (International Enterprise Integration Association, 2023)
In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions