|< OCTOBER >||< 2020 >|
Location: Room 517A, Taishan Research Building
Speaker: Mitsuo Gen, Japan
4.20(Mon): 18:00-21:10 Chapter1 Multiobjective Genetic Algorithms
4.21(Tue): 13:00-16:10 Chapter2 Basic Network Models
4.21(Tue): 18:00-21:10 Case Study: Exercise, Discussion and Solutions for Chapters 1 & 2
4.22(Web): 13:00-16:10 Chapter3 Logistics Network Models
4.22(Wed): 18:00-21:10 Case Study: SCM Network Model and Discussion & Solutions for Chapter 3
4.23(Thu): 8:30-11:40 Chapters 5 & 7 Advanced Planning & Scheduling and Assembly Line Balancing Models
4.23(Thu): 13:00-16:10 Case Study: HDD Manufacturing Model and Discussion & Solutions for Chapters 5 & 7
Chapter 9 Advanced Network Models and Case Study: Airline Fleet Assignment
Many real world applications in Manufacturing and Logistics impose on more complex issues, such as complex structure, complex constraints, and multiple objectives to be handled simultaneously and make the problem intractable to the traditional approaches because of NP-hard combinatorial optimization problems (COP) [1-5]. Network models and optimization for various manufacturing and logistics systems also provide a useful way as one of case studies in real world problems and are extensively used in practice. In order to develop a solution algorithm that is in a sense "good," that is, whose computational time is small, or at least reasonable for NP-hard COP met in practice, we have to consider the following issues [6-7]: Quality of solution, Computational time and Effectiveness of the nondominated solutions for multiobjective optimization problem (MOP).
Evolutionary Algorithms in AI (Artificial Intelligence) technique have recently received a considerable attention because of its potential of being a very effective design optimization technique for solving various NP hard combinatorial optimization problems and complex information processing, manufacturing and logistics systems. This intensive course introduces a thorough treatment of several Genetic Algorithms (GA) [8-10] and Particle Swarm Optimization (PSO) [11-12] to treat the following various Scheduling/Routing optimization problems based on various network-based models from the textbook: M. Gen, R. Cheng and L. Lin: “Network Models and Optimization: Multiobjective Genetic Algorithms”, 2008,Springer: http://www.springer.com/engineering/production+eng/book/978-1-84800-180-0.
By the end of the intensive course, students will understand how to apply the Evolutionary Algorithm technique to various scheduling/routing problems in Manufacturing and Logistics Systems.