Assoc. Prof. Hongbo Li
School of Management, Shanghai University
Research Area：Management Information Systems; Project Scheduling; IT Project Management
Brief introduction of your research experience:
Hongbo Li is Associate Professor of Information Systems and Management Science in the School of Management at Shanghai University, Shanghai, China. He obtained his PhD degree in Management Science in July 2014 from School of Economics and Management, Beihang University, Beijing, China. He was a visiting PhD student at Research Center for Operations Management, Faculty of Economics and Business, KU Leuven, Belgium from 2012 to 2013. His research interests include artificial intelligence, metaheuristics, project scheduling, robust scheduling, data science, business analytics, and information systems. He has published in a variety of refereed journals, such as Journal of Scheduling, International Journal of Production Research, Decision Support Systems, Expert Systems with Applications, and Electronic Commerce Research and Applications.
Speech Title: An Effective Genetic Algorithm for the Resource Leveling Problem with Generalized Precedence Relations
Abstract: Resource leveling aims to obtain a feasible schedule to minimize the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource leveling problem with generalized precedence relations (RLP-GPR). Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.