15 اردیبهشت 1403
علي احمديان

علی احمدیان

مرتبه علمی: دانشیار
نشانی: بناب- دانشگاه بناب
تحصیلات: دکترای تخصصی / مهندسی برق
تلفن: 04137745000
دانشکده: دانشکده فنی و مهندسی
گروه: گروه مهندسی برق

مشخصات پژوهش

عنوان
Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
multiscale decision making; big data analytics; planning and scheduling; clustering; supply chain; multiple attributes; computational complexity; energy hub
پژوهشگران Falah Alhameli (نفر اول)، علی احمدیان (نفر دوم)، Ali Elkamel (نفر سوم)

چکیده

In modern systems, there is a tendency to model issues more accurately with low computational cost and considering multiscale decision-making which increases the complexity of the optimization. Therefore, it is necessary to develop tools to cope with these new challenges. Supply chain management of enterprise-wide operations usually involves three decision levels: strategic, tactical, and operational. These decision levels depend on each other involving different time scales. Accordingly, their integration usually leads to multiscale models that are computationally intractable. In this work, the aim is to develop novel clustering methods with multiple attributes to tackle the integrated problem. As a result, a clustering structure is proposed in the form of a mixed integer non-linear program (MINLP) later converted into a mixed integer linear program (MILP) for clustering shape-based time series data with multiple attributes through a multi-objective optimization approach (since different attributes have different scales or units) and minimize the computational complexity of multiscale decision problems. The results show that normal clustering is closer to the optimal case (full-scale model) compared with sequence clustering. Additionally, it provides improved solution quality due to flexibility in terms of sequence restrictions. The developed clustering algorithms can work with any two-dimensional datasets and simultaneous demand patterns. The most suitable applications of the clustering algorithms are long-term planning and integrated scheduling and planning problems. To show the performance of the proposed method, it is investigated on an energy hub as a case study, the results show a significant reduction in computational cost with accuracies ranging from 95.8% to 98.3%.