一、报告人：
Dr. Alessandro Hill(assistant professor in industrial engineering at California Polytechnic State University)
二、摘要：
It is commonly assumed that experience leads to efficiency, yet this is largely unaccounted for in resource-constrained project scheduling. We consider the case that selected activities can be completed within reduced time when scheduled after activities that result in learning of relevant skills. Since per-period availabilities of renewable resources are limited, and given precedence requirements have to be respected, the presented optimization model with the objective of minimizing the project makespan generalizes the resource-constrained project scheduling problem. To solve the optimization problem, we introduce three constraint programming formulations that incorporate the alternative learning-based job durations via logical constraints, dynamic interval length and a multi-mode problem reformulation, respectively. In order to provide tight optimality gaps for larger problem instances, we introduce six lower bounding techniques based on model relaxations and a destructive lower bounding method. We perform an extensive computational model analysis across thousands of scenarios in order to quantify the impact project size, number of potential learning occurrences, and individual job learning potential. Moreover, we provide a comprehensive performance analysis in which we compare the scheduling capabilities with respect to the formulations and the quality of the obtained lower bounds on large PSPlib-based literature instances.
三、地点和时间：
机械工程学院 机械楼D530 2019年7月23日早上9:00-11:00
四、报告人简介：
Dr. Alessandro Hill is currently an assistant professor in industrial engineering at California Polytechnic State University. He studied mathematics and computer science at University of Augsburg (Germany) and Iowa State University (USA), and obtained his Ph.D. in Operations Research from the University of Hamburg (Germany). In several research and industry projects he worked for industries such as automotive, railroads, telecommunications, logistics and chemicals with a focus on optimization, simulation and analytics to provide decision support. His main research areas are optimization methods and applications for networks and scheduling.
