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About

Alireza Alinezhad’s research focuses on the intersection of multiple criteria decision making (MCDM) and data envelopment analysis (DEA), with a particular emphasis on technology selection. His major contributions lie in developing practical common-weight approaches that improve the discriminating power of efficiency evaluations when choosing among competing technologies. Alinezhad introduced innovative methodologies—including goal programming, multi-objective linear programming (MOLP), and Maximin models—that allow decision-makers to assess technologies based on both quantitative and qualitative outputs using a single exact input. His most cited work, “An MCDM-DEA approach for technology selection” (2011, 7 citations), builds on earlier frameworks to offer a robust tool for selecting advanced manufacturing technologies. Other notable papers, such as “Practical common weights goal programming approach for technology selection” (2009, 5 citations) and “Technology selection with both quantitative and qualitative outputs” (2008, 4 citations), further demonstrate his commitment to solving real-world industrial dilemmas. Alinezhad’s research has provided managers with systematic, transparent methods for navigating the complexity and variety of modern technologies, making his work a valuable resource for practitioners and scholars in operations research and technology management.