We are pleased to have brilliant keynote speakers in SEMIT 2022- Sep.
The titles are of top trended subjects and the speeches would be helpful for audiences. Don't miss them!
1- Public Transport for Smart Cities; Prof. Janny M.Y. Leung
2- Optimal Control Computation for Nonlinear Time Delay Systems; Prof. Kok Lay Teo
3- 50 Years "Limits of Growth" Management Science Challenges and Perspectives on Energy Security and Complex Resource Conflicts; Prof. Stefan Pickl
4- How Can We Mind the Gap Between Theory and Practice in Prescriptive Analytics?; Prof. Bernardo Almada-Lobo
5- Public Logistics Networks for Home Delivery; Prof. Michael G. Kay
6- Energy-Efficient Scheduling in Robotic Manufacturing Cells; Prof. Hakan Gultekin
Short Bio
Janny Leung is a Professor at the University of Macau affiliated with the Faculty of Business Administration and the State Key Lab of Internet of Things for Smart City. She holds an S.B. degree in Applied Mathematics from Harvard University, an M.A. in Mathematics from Oxford University and a Ph.D. in Operations Research from the Massachusetts Institute of Technology. Her main research interests are combinatorial optimization and transportation logistics. Her research has been well supported by the National Science Foundation of USA and the Hong Kong Research Grants Council. She is a Fellow of the Chartered Institute of Logistics and Transport, a Fellow of the Hong Kong Institution of Engineers and a Fellow of The Institute for Operations Research and the Management Sciences (INFORMS). She is currently the President of the International Federation of Operational Research Societies (IFORS).
Abstract
The idea of a smart city is one that utilizes IoT technologies and data analytics to optimize the efficiency of city operations and services, so as to provide a high quality of life for its citizens. Therefore, public transport for a smart city should aim beyond the movement of people, to providing mobility for living. The growth of metropolitan areas in many countries into mega-cities have led to extreme traffic congestion in city centres and urban sprawl on their outskirts. In order to provide sufficient coverage/frequency, an integrated co-ordinated multi-modal public transportation system is needed, leading to substantial increase in operational complexity. Environmental concerns and the recent pandemic may also have changed work and commuting patterns in the future. For smart cities, public transportation must offer ubiquitous access, real-time response to demand, convenience and quality service, and energy-efficient operations. This talk will discuss the challenges in network design, operations planning, scheduling and management of smart public transportation systems.
Short Bio
Professor Kok Lay Teo received his Ph.D. degrees in Electrical Engineering from the University of Ottawa, Canada. He is an Academician of the International Academy for Systems and Cybernetic Sciences (IASCYS), a Fellow of Asian-Pacific Artificial Intelligent Association (AAIA) and a Fellow of the Australian Mathematical Society (AustMS). He was with the Department of Applied Mathematics, University of New South Wales, Australia, the Department of Industrial and Systems Engineering, National University of Singapore, Singapore, the Department of Mathematics, the University of Western Australia, Australia, and the Department of Mathematics and Statistics, Curtin University, Australia. He then took up the position of Chair Professor of Applied Mathematics and Head of the Department of Applied Mathematics at the Hong Kong Polytechnic University from December 1998 to December 2004. He returned to Curtin University as Professor of Applied Mathematica and Head of the Department of Mathematics and Statistics from January 2005 to December 2010. He was John Curtin Distinguished Professor at Curtin University from January 2011 until his retirement in November 2019. He is now John Curtin Distinguished Emeritus Professor at Curtin University, and a Professor in the School of Mathematical Sciences at Sunway University, Malaysia.
He was a member of the Australian Research Council’s (ARC) Mathematical, Information, and Computing Sciences Research Evaluation Committee for 2010 and 2015 rounds of Excellence in Research for Australia (ERA). Professor Teo has published 6 books and over 550 SCI-listed journal papers. Details of his latest book are: Kok Lay Teo, Bin Li, Changjun Yu and Volker Rehbock, Applied and Computational Optimal Control: A Control Parameterization Approach, Springer Optimization and Its Application 171, 2021. He has a software package, MISER3.3, for solving constrained optimal control problems. His current editorial positions include serving as Editor-in-Chief of the Journal of Industrial and Management Optimization; and as a member of editorial board of a number of journals such as Automatica, Journal of Global Optimization, Journal of Optimization Theory and Applications, Optimization and Engineering, Discrete and Continuous Dynamic Systems, Optimization Letters, and Applied Mathematical Modelling. His research interests include theoretical and computational aspects of optimal control and optimization, and their practical applications such as in signal processing in telecommunications, process control, and industrial and management optimization.
Abstract
Time-delay system is a type of dynamic system that depends not only on the current state and/or control but also on previous state and/or control. This phenomenon is encountered in various real-world situations. The control parameterization technique used in conjunction with the time scaling transform is an effective computational method for solving various optimal control problems. More specifically, the control parameterization method approximates the control function as a piecewise constant function with its heights and switching times as decision variables. The time scaling transform maps various time points into fixed time points in a new time horizon. However, time delays bring difficulties to time-scaling transformation when solving the time-delay optimal control problems. Although time- scaling transform technique maps the variable switching times into fixed time points in a new time horizon, it also transforms the fixed time delays defined in the original time horizon into variable delays in the new time horizon. Therefore, the time-scaling transformation technique fails to be applicable to solve the time-delay optimal control problem. In this talk, two generalized time scaling transforms will be discussed. The first transform is a hybrid time- scaling transform, which works by mapping the current state/control into a new time scale while the time-delay state/control still remain in the original time horizon. On this basis, variational method or co-state method can be used to derive the gradient formulas. Thus, gradient-based algorithms can be developed to solve the time-delay optimal control problems. However, the values of the delay state/control in the new time horizon can only be obtained by numerical interpolation. For the second transform, the explicit closed form expression for the variable delay in the new time horizon is derived, and hence is regarded as a complete version of the time scaling transform for time-delay optimal control problems. The gradient formulas of the objective and constraint functions can be derived based on variational method or costate method. A real-world practical example is solved so as to illustrate the effectiveness of the methods proposed.
Short Bio
Stefan Pickl is a professor at the Institute for Theoretical Computer Science, Mathematics and Operations Research at Universität der Bundeswehr München, Germany. He studied mathematics, theoretical electrical engineering and philosophy at the TU Darmstadt (diploma 1993, ERASMUS scholarship holder at the EPFL Lausanne) where he also earned his doctorate in 1998. In 2004 he got a C4 professorship for Operations Research at the Uni Bw in Munich and earned Habilitation in 2004/05 from the University of Cologne. From 2000-2005, Mr. Pickl was a research assistant and project manager at the Center for Applied Computer Science in Cologne (ZAIK) with main responsibility in the area of "Modeling, Simulation and Optimization of Resource Conflicts - Analysis of Complex Systems". He is vice-president of the German Committee for Disaster Reduction DKKV.
Abstract
2022 is a special year from different perspectives: Resource conflicts, security and climate-policy issues play an important role. This talk summarizes the history of more than 50 years Club of Rome and their important contribution “Limits of Growth” and presents special views from Management Science to complex resource conflicts and scenario-based decision making processes in context of energy security.
Different mathematical decision models and solution concepts are introduced. The TEM model is summarized and a game-theoretic extension is discussed. An algorithmic solution concept based on intelligent optimization techniques is derived. Some generalizations are characterized and discussed: Managerial Decision Making will be influenced in the future by certain developments of AI-based expert systems, machine learning techniques as well as different reinforcement learning approaches. Prescriptive analytics could be considered as an example how managerial decision making could be seen as a further application for control science and classical optimization in context of energy security and complex resource conflicts. May intelligent game theoretic solutions lead to sustainable solutions … ?
Short Bio
Full Professor at Industrial Engineering and Management, Faculty of Engineering, Porto University, and of Porto Business School. Co-founder of advanced analytics LTPlabs. Member of the Board of Trustees of Fundação Belmiro de Azevedo.
Former member of the Board at INESC TEC Technology and Science. Former researcher at Operations Research Center of Massachusetts Institute of Technology – MIT/ORC and Visiting Professor at University of São Paulo.
Advanced Management Programme from INSEAD. PhD and Master in Industrial Engineering and Management, University of Porto. Certified Analytics Professional from The Institute for Operations Research and the Management Sciences.
His main area of activity is Management Science/Operations Research. He develops and applies advanced analytical models and methods to help make better decisions, solving managerial problems in various domains (manufacturing, health, retail and mobility), with a special focus on Operations Management.
Abstract
Most organizations already use to a certain extent effectively descriptive analytics to understand past events. Fewer attempts through predictive analytics the anticipation of scenarios and estimation of trends, and only a minority triggers great or clever recommendations based on prescriptive analytics. The necessary change of companies’ mindset regarding the use of optimization models and business decision support systems, requires more than just appropriate technology, people and processes. It requires a proper change management.
In parallel, academic institutions must also lift the practical relevance of the research conducted in operations research and management science.
In this talk, we make use of a few successful and unsuccessful business analytics R&D projects related to operations management, as well as recent developments in prescriptive analytics, to draw some guidelines and best practices of this field.
Short Bio
Professor Michael G. Kay is director of the Integrated Manufacturing Systems Engineering graduate program at North Carolina State University. He did his doctoral studies at NC State’s Center for Robotics and Intelligent Machines and teaches courses in logistics engineering, production system design, and operations research. His research is currently focused on the development of public logistics networks.
Abstract
This talk will describe the design methodologies and protocol/mechanism specifications involved in developing a public logistics network that is able to facilitate low-cost deliveries to the home via the use of autonomous vehicles. The challenges and opportunities of this approach to home delivery are discussed, along with a discussion and relative cost comparison of different home delivery alternatives. A network design procedure is discussed that determines the number and location of distribution centers (DCs) in a metro area that best supports home delivery, along with a storage system control architecture for use inside of a DC and a mechanism to coordinate the operation of each vehicle in DC in the home delivery network. Finally, an estimate is provided of the likely cost for each delivery to the home.
Short Bio
Hakan Gultekin has been an Associate Professor at the Department of Mechanical and Industrial Engineering at Sultan Qaboos University, Oman, since 2018. He received his Ph.D. degree in Industrial Engineering from Bilkent University, Turkey, in 2007. After his postdoc studies at the University of Liege, Belgium, he was employed at TOBB University of Economics and Technology, Turkey, from 2007 to 2018.
Dr. Gultekin's research interests include scheduling, optimization modelling, and exact and heuristic algorithm development for problems arising in communication, modern manufacturing, energy, and logistics. His research has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK).
Abstract
Industrial robots are widely used in factories, and their usage rates are still increasing rapidly every year. They are preferred for many reasons, such as increasing throughput rates, providing flexibility in production, and performing work that may be toxic or dangerous for workers or non-ergonomic. One of the most common uses of industrial robots is material handling. Production systems consisting of several machines and a robot responsible for loading and unloading these machines and transporting materials between them are called robotic cells.
In order to obtain maximum benefit from robotic cells, critical operational problems need to be solved. Among them, the sequencing of the jobs and the robot's movements are among the most important. On the other hand, robotic cells are systems with high energy consumption due to their structure. This talk will first define the classical operational problems in robotic cells. Later we will discuss the modelling of energy consumption of the robot with respect to its movement speed and the trade-off between the throughput rate and energy consumption. We will develop optimization models and solution procedures that consider this trade-off for robotic cells of different structures. The results compiled from various studies will show how much energy can be saved under different scenarios.