Fuzzy logic, MCDM and optimization-based computing with application in semiconductor supply chain localization

Purpose Wafer foundries face numerous challenges in localizing the semiconductor supply chain, necessitating various measures to overcome these challenges. Given their limited funding, time and resources, these measures require prioritization. Design/methodology/approach To prioritize these measures, this study proposes a dynamic fuzzy compromise planning (DFCP) approach, which dynamically allocates resources across a limited budget based on the priority of measures within each time period. The DFCP approach embeds a fuzzy technique for order preference by similarity to ideal solution mechanism into a fuzzy mixed binary-nonlinear programming model. Findings The DFCP approach has been applied to a real-world case. Experimental results demonstrated that the DFCP approach satisfied the three objectives of wafer foundries, while existing methods either over-concentrated resources on a few possible measures or produced discontinuous planning fragments. Originality/value Semiconductor supply chain localization is undoubtedly one of the most advanced manufacturing models in the semiconductor industry, yet it has received little discussion to date. The DFCP approach combines multi-criteria decision-making practices with fuzzy optimization models, which is quite different from most existing fuzzy compromise programming methods.