A possibilistic programming approach for biomass supply chain network design under hesitant fuzzy membership function estimation - Gitinavard, Hossein; Shirazi, Mohsen Akbarpour; Zarandi, Mohammad Hossein Fazel (Scientia Iranica. Transaction E, Industrial engineering, 2024)
The recognition of membership function by knowledge acquisition from experts is an important factor for many fuzzy mathematical programming models. Meanwhile, hesitant fuzzy set theory as a known and popular modern fuzzy set by assigning some discrete membership degrees under a set could appropriately deal with imprecise information in decision-making problems. Thus, the Hesitant Fuzzy Membership Function (HFMF) estimation could help users of the mathematical programming approaches to provide a powerful solution in continuous space problems. Therefore, this study proposes a possibilistic programming approach based on Bezier curve mechanism for estimating the HFMF. In the process of possibilistic programming approach, an optimization model is presented to tune the primary parameters of Bezier curve by the goal of minimizing the SSE) between the empirical data and fitted HFMF. After that, the efficiency and applicability of the proposed approach is checked by proposing a novel mathematical model for biomass supply chain network design problem. Finally, a computational experiment and validation procedure about the biomass supply chain network design is provided to peruse the verification and validation of the proposed approaches.