![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
Recently, Hierarchical Optimization Models (HOM) have been widely used to model Combinatorial Optimization Problems (COP) in numerous fields including transport, supply chain economics, healthcare, etc. Their efficiency as a modeling approach is justified by their ability to ease the difficulty of the studied COP. The main problem will be decomposed hierarchically into a set of interconnected subproblems using multiple decomposition strategies. However, solving the hierarchical optimization model presents a great challenge and difficult task to get an optimal solution for the main problem. In this context, we propose a generic algorithm to efficiently solve the hierarchical optimization model. Moreover, the developed algorithm will be able to solve all kinds of hierarchical optimization models using any decomposition strategy. To validate the proposed modeling and solving approach, we solved the Home Health Care Scheduling Problem (HCSP) by conducting some experiments on a set of real-world data instances.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Marouene Chaieb
Marouene Chaieb is an assistant professor in the Information Systems Department at Prince Sattam bin Abdulaziz University, KSA. He holds a bachelor’s in computer science, a master’s degree in information systems, and a PhD in information systems from the Higher Institute of Management of Tunis, Tunisia. His areas of interest and research are in healthcare service delivery, systems modeling and optimization, logistics and supply chain management, medical computing, and decision making. He is a member of the LARODEC laboratory from 2006. Corresponding author. Email: [email protected], [email protected]
Jaber Jemai
Jaber Jemai holds a bachelor’s, master’s, and a PhD degree in computing information systems from the University of Tunis and a postgraduate diploma in data science and business analytics from Mc Combs School of Business, University of Texas at Austin. He justifies more than 19 years of experience in academia. He is currently an associate professor in the Computer and Information Systems division of the Higher Colleges of Technology in the UAE. The research interests of Dr Jaber include combinatorial optimization and machine learning techniques and their applications to solve logistics, transportation, healthcare, and financial problems among others. He authored and co-authored more than 20 papers published in international refereed journals and conferences. Email: [email protected]
Dhekra Ben Sassi
Dhekra Ben Sassi born in Tunis, Tunisia. She holds a BSc in computer science in 2010 from Institut Sperieur de Gestion de Tunis, Tunisia, an MSc in computer science in 2012 from Institut Sperieur de Gestion de Tunis, and a PhD in 2018 in computer information systems from the University of Tunis. Her main research interest is in the area of business intelligence. She is an assistant professor at the Department of Computer Science at Sattam bin Abdelaziz University, Al Kharej, KSA. Email: [email protected]
Khaled Mellouli
Khaled Mellouli was born in Tunisia. He holds a BSc in computer science, an MSc in computer science, and a PhD from the USA. His main research interest is in the area of artificial intelligence (AI) and optimization problems (OP). He is a full professor at Institut Haute des etudes commerciale (IHEC Tunis) and the director of LARODEC Laboratory, Institut Superieur de Gestion de Tunis. Email: [email protected]