Focal Spot, Spring 1995

https://digitalcommons.wustl.edu/focal_spot_archives/1069/thumbnail.jp

Final Report of the ModSysC2020 Working Group - Data, Models and Theories for Complex Systems: new challenges and opportunities

Final Report of the ModSysC2020 Working Group at University Montpellier 2At University Montpellier 2, the modeling and simulation of complex systems has been identified as a major scientific challenge and one of the priority axes in interdisciplinary research, with major potential impact on training, economy and society. Many research groups and laboratories in Montpellier are already working in that direction, but typically in isolation within their own scientific discipline. Several local actions have been initiated in order to structure the scientific community with interdisciplinary projects, but with little coordination among the actions. The goal of the ModSysC2020 (modeling and simulation of complex systems in 2020) working group was to analyze the local situation (forces and weaknesses, current projects), identify the critical research directions and propose concrete actions in terms of research projects, equipment facilities, human resources and training to be encouraged. To guide this perspective, we decomposed the scientific challenge into four main themes, for which there is strong background in Montpellier: (1) modeling and simulation of complex systems; (2) algorithms and computing; (3) scientific data management; (4) production, storage and archiving of data from the observation of the natural and biological media. In this report, for each theme, we introduce the context and motivations, analyze the situation in Montpellier, identify research directions and propose specific actions in terms of interdisciplinary research projects and training. We also provide an analysis of the socio-economical aspects of modeling and simulation through use cases in various domains such as life science and healthcare, environmental science and energy. Finally, we discuss the importance of revisiting students training in fundamental domains such as modeling, computer programming and database which are typically taught too late, in specialized masters

Application of general semi-infinite Programming to Lapidary Cutting Problems

We consider a volume maximization problem arising in gemstone cutting industry. The problem is formulated as a general semi-infinite program (GSIP) and solved using an interiorpoint method developed by Stein. It is shown, that the convexity assumption needed for the convergence of the algorithm can be satisfied by appropriate modelling. Clustering techniques are used to reduce the number of container constraints, which is necessary to make the subproblems practically tractable. An iterative process consisting of GSIP optimization and adaptive refinement steps is then employed to obtain an optimal solution which is also feasible for the original problem. Some numerical results based on realworld data are also presented

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated

Robust Direct Aperture Optimization Methods for Cardiac Sparing in Left-Sided Breast Cancer Radiation Therapy

Designing conformal and equipment-compatible radiation therapy plans is essential for ensuring high-quality treatment outcomes for cancer patients. Intensity modulated radiation therapy (IMRT) is a commonly-used method of radiation delivery for cancer patients, wherein beams of radiation are individually contoured to cover a patient’s tumour cells while avoiding healthy cells and organs. In IMRT for left-sided breast cancer, the goal is to irradiate all cells in the breast tissue while avoiding the neighbouring, and extremely radiation-sensitive, heart cells. To add to the complexity of this treatment, the entire dose must be delivered while the patient is breathing, causing the location of the heart and target organs to move and deform unpredictably. The search for a plan that is of the highest quality for a specified set of parameters is called treatment plan optimization. One method of treatment plan optimization that provides an optimal radiation distribution, even under the worst-case realization of a patient’s motion uncertainty, uses a framework called robust optimization. A drawback of using this robust optimization framework, however, is that it does not immediately output physically deliverable IMRT plans. Rather, a subsequent, non-trivial post-processing phase must be applied to the output intensity distributions in order to generate an equipment-compatible plan; a process which can substantially degrade the treatment quality. In this thesis, a holistic approach that combines enforcement of delivery constraints with robust optimization is introduced. The process for creating deliverable plans is called direct aperture optimization (DAO), and the combined model is called robust DAO (RDAO). Novel modelling strategies for integrating the DAO requirements into a robust framework are presented, leading to a large-scale difficult-to-solve mixed integer programming problem. To contend with the complexity of the problem, additional modelling approaches are suggested for improving solution efficiency. These approaches include a hybrid heuristic-optimization technique, which provides good quality, but non-optimal treatment plans. Clinicians may use the output of this hybrid technique as is, or apply it as a warm start for the RDAO model. The models are implemented in C++ and CPLEX and results are presented, first using a one-dimensional phantom, and then a three-dimensional clinical patient dataset. While the full RDAO model is quite time-consuming to run, high-quality plans are ultimately produced. These plans are both clinically deliverable and mitigate the risk of underdosing a patient’s cancerous cells under motion uncertainty, demonstrating their value over plans that did not account for motion uncertainty

International conference "Information technologies in education in the 21st century": Conference proceedings.

Proceedings of a conference which concluded TEMPUS project JEP 25008_200

Biologically-based radiation therapy planning and adjustable robust optimization

Radiation therapy is one of the main treatment modalities for various different cancer types. One of the core components of personalized treatment planning is the inclusion of patient-specific biological information in the treatment planning process. Using biological response models, treatment parameters such as the treatment length and dose distribution can be tailored, and mid treatment biomarker information can be used to adapt the treatment during its course. These additional degrees of freedom in treatment planning lead to new mathematical optimization problems. This thesis studies various optimization aspects of biologically-based treatment planning, and focuses on the influence of uncertainty. Adjustable robust optimization is the main technique used to study these problems, and is also studied independently of radiation therapy applications

Application of constrained optimization methods in health services research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force

Background Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity. Objectives In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available. Conclusions Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force’s first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods