P1-1 Modeling and Optimization for Sustainability in Transport Systems using Artificial Intelligence
Abstract: Sustainability in transport systems is becoming a key feature that must be integrated or at least considered in the planning, design, construction, and maintenance stages. The tools of modeling, simulation, and optimization (MSO) are often necessary to use either singly or in a group to achieve that end. Owing to the large, complex and integrated nature of transport systems, using traditional MSO in transportation systems can be cumbersome. Different techniques in artificial intelligence (AI) are particularly suited to use and thus overcome some of the limitations of traditional MSO techniques. This presentation first looks at unintended/undesirable consequences of transport systems (also called issues of viability) that make transport systems unsustainable. By definition, these are the areas of necessary attention (to achieve sustainability) in the different stages of transport projects. Sample applications of selected AI techniques as means to incorporate and/or attain necessary sustainability levels in transport systems are then presented.
Bio: Ghassan Abu-Lebdeh is an Associate Professor of Civil Engineering at the American University of Sharjah (AUS) in the UAE. His research and teaching interest are in transport systems operations and sustainability, with special interest in control of congested and interrupted flow facilities. Dr. Abu-Lebdeh obtained his PhD form the University of Illinois at Urbana-Champaign, USA. Dr. His tenure spans 14 years in academia (AUS, Michigan State University, and University of Kentucky) and 10 years in the industry in the states of Massachusetts and Illinois.
P1-2 Task Scheduling for Multifunction Radar
Hasan S. Mir
Abstract: A framework and method is presented for developing a cyclic task schedule in a multifunction radar. Rather than assuming the task dwell time to be a fixed value when building the schedule, the task dwell time is modeled as a fuzzy set to allow for increased radar schedule flexibility. An optimization model is developed for the scheduling problem and a heuristic method for its solution is proposed. The heuristic method exploits the fuzzy set model in order to intelligently adjust the task dwell times. This adjustment allows for accommodation of more tasks on the radar timeline, thereby resulting in fewer dropped tasks. Computational results are presented to assess the behavior of the proposed scheduling method.
Bio: Hasan S. Mir received the B.S. (cum laude), M.S., and Ph.D. degrees in electrical engineering from the University of Washington, Seattle, in 2000, 2001, and 2005, respectively. From 2005 to 2009, he was with the Air Defense Technology Group at Massachusetts Institute of Technology Lincoln Laboratory. Since 2009, he has been an Assistant Professor with the Department of Electrical Engineering, American University of Sharjah, Sharjah, U.A.E.
P2-1 Optimzation in Supply Chain Management: Packing and Routing
Abstract: Decision making in the supply chain is a strategic action that requires optimization techniques to be accomplished while considering resource constraints. Applications performing optimization techniques are countless. Among the most challenging problems in the supply chain is the delivery problem between the supply chain layers namely, the suppliers -retailers and retailers-customers levels. As it is the case, an efficient delivery process should optimize both the packing and the routing of the ordered items. This talk presents a design of the supply chain flows viewed as an optimization modeling as a full space and multi-stage formulations.
The addressed problem includes the class of packing problems in both single objective and multiobjective frameworks and the efficient routing for the delivery process to the set of end points.
In this talk, a decision support system that employs metaheuristic and exact solution approaches is proposed to promptly solve this NP-hard problem.
The developed techniques involve both the generation of optimal and near-optimal solutions as well as the Pareto front.
An open mind issue to the dynamic framework is evoked to allow the management of real time orderings and the adjustment of the delivery process during a time period. Such an online problem can be solved by a dynamic approach.
Bio: Saoussen Krichen is an Associate Professor in the Faculty of Law, Economics and Management of Jendouba, Tunisia and Member of the Larodec laboratory (Institut Superieur de Gestion de Tunis). She works on single and multiobjective, optimisation, dynamic optimization, coalition formation and supply chain management.
P2-2 Uncertainty and Optimization: A Unified approach
Abstract: This presentation gives an overview of recent Optimization models under uncertainty. Probabilistic optimization models have been widely used to address many real life problems, such as portfolio selection, transportation, reliability and supply chain management. However the major challenge of these probabilistic optimization models is the partial knowledge about the probability distribution. In recent years there were new optimization models where the probability distribution can be partially known. The purpose of this presentation is to review these existing models, highlight their research challenges, and help to guide future efforts in optimization and modeling.
Bio: Hatem Masri is an Associate Professor at the College of Business Administration, in the University of Bahrain, Kingdom of Bahrain. He received a PhD in Management in 2004 and a master in Operations research in 1999 from the University of Tunis, Tunisia. His research interests include stochastic programming, multiple objective stochastic programming, supply chain management, financial engineering, vehicle routing problems. His research has been published in several international journals (EJOR, FSS, IJAR …) and funded by the University of Tunis, the University of Nizwa and the University of Bahrain. Dr. Hatem is member of the International Society on Multiple Criteria Decision Making, IEEE, INFORMS, Tunisian Decision Aid Society and Tunisian Management Sciences Society.