optimizing multi-level aspirations through multi-segment goal programming

Goal programming (gp) has emerged as one of the most powerful analytical approaches for solving complex real-world decision-making problems. however, traditional goal programming methods often fail to effectively handle problems involving multi-segment (ms) or multi-level (ml) aspiration structures, which are commonly observed in marketing, supply chain (sc) management, and multi-criteria decisionmaking (mcdm) environments. this study introduces a multi-segment goal programming (msgp) model designed to address the limitations of existing gp techniques. the proposed model considers multiple goal levels for each decision variable across various objectives, offering a more flexible and realistic representation of decision-makers’ expectations.to demonstrate the validity and effciency of the proposed method, three illustrative examples are developed. the results show that the model provides more accurate and adaptable solutions than conventional single-level or two-level gp models. the study concludes by emphasizing that the multi-segment gp model can effectively be applied in various domains such as supplier selection, production planning, and image segmentation, where multiple aspiration levels and competing objectives coexist.