July 17, 2024

Fields Industrial Optimization Seminar

Supported by

The inaugural meeting of the Fields Industrial Optimization Seminar took place on November 2, 2004. This series will meet once a month, on the first Tuesday, in the early evening. Each meeting will comprise two related lectures on a topic in optimization; typically, one speaker will be a university-based research and the other from the private or government sector.We welcome the participation of everyone in the academic or industrial community with an interest in optimization -- theory or practice, expert or student.

Please subscribe to the Fields mail list to be informed of upcoming seminars.

Seminars start at 5:00 pm at the Fields Institute, 222 College Street, Room 230

June 2, 2009

Brian Denton, North Carolina State University
Optimization of Surgery Delivery Systems
Audio and Slides of the Talk

Scheduling systems are used in many industries to increase the utilization of resources, match workload to available capacity, and smooth the flow of customers through a service system. They are also important for healthcare delivery where applications include scheduling of patients to outpatient clinics and the design of operating room schedules. In this talk I will discuss stochastic optimization models for scheduling surgeries at outpatient clinics and hospitals. I will discuss three related problems. The first involves setting individual procedure start times for a single operating room (OR) given uncertainty in the duration of procedures. The objective of this problem is to minimize three competing criteria: patient and OR team waiting time, OR idle time, and overtime. The second problem involves the allocation of surgeries across multiple ORs with the goal of balancing the fixed cost of opening ORs with the expected cost of total overtime. The third problem involves setting optimal arrival times for patients to an outpatient surgical suite comprising multiple activities including: intake processes, surgery, and recovery. For each problem I will describe the mathematical model, structural properties, methodologies employed, and sample numerical results based on real data to illustrate the impact of the model.


Franklin Dexter, Professor and Director of Operations Research, Operations Research, Department of Anesthesia, University of Iowa
Big Open (IE) Problems in Operating Room Management
Audio & Slides of the Talk

My talk will address my perspectives on the top three open (IE/OR/MS) problems in operating room management from the perspective of a person whose focus is implementation of methods to improve anesthesia group and hospital operating room managerial decision-making. First, there needs to be research in education of personnel with an IE/OR/MS background to work in perioperative medicine. People’s hypothesis (opinions) based on analogies from other domains have not applied when studied. Second, medical monitoring, physician order entry, and textual entry systems need to be used for real-time, automatic determination of conditions for managerial decision-making. These data fusion problems rely heavily on understanding the clinical context which has hampered development. Third, methods are needed to provide individual physicians with recommendations on how to increase their productivity, starting with simpler problems based on perfect knowledge and then advancing to incorporate measurement error and missing values. Complementary research is needed on how to communicate those results to the individuals.

Franklin Dexter is Professor at the University of Iowa. As part of his job in the Department of Anesthesia, he has performed more than 220 consultations for more than 105 corporations. He has published more than 250 papers in the fields of operating room management and anesthesia. He has given more than more than 110 invited presentations. He is Editor of the Section on Economics, Education, and Policy of the journal Anesthesia & Analgesia. Twice a year, he teaches a four-day intensive course in operating room management at the University. He completed a Sc.B. in Applied Mathematics & Biology with Honors from Brown University; Masters Degree & Ph.D. in Biomedical Engineering, with specialization in biomathematics, from Case Western Reserve University; M.D. degree from Case Western Reserve University School of Medicine; and Anesthesiology residency at the University of Iowa. More details, as well as the comprehensive bibliography of scientific papers in OR management, are at

April 30, 2009

David Bremner, University of New Brunswick
Optimal Separating Hyperplanes

Separating hyperplanes are important in the theory of convex geometry and the practise of optimization. The notion of an optimal separating hyperplane is in some sense as old as that of linear separation itself, with early proofs of the separating hyperplane theorem computing what in modern terms is a ``maximum margin hyperplane''.

Finding a best separating hyperplane is a kind of generalized linear programming problem, where each input point yields a linear constraint, and the objective function is defined by whatever notion of ``best'' is relevant for the application. Naturally the theoretical and practical tractability of the resulting optimization problem depends on what exactly this objective function is. Even for objectives arising from one application, radically different results are possible.

Despite the long history, there remain many interesting questions of both a theoretical and a practical/computational nature. In this talk I will explore in particular the notion of a maximum margin hyperplane (i.e. pair of parallel separators with maximum distance between them), which is intimately connected to machine learning and pattern
recognition, and the more discrete notion of the most unbalanced separator through a particular point, which leads to ``halfspace-depth'' or ``Tukey depth'', and applications in non-parametric statistics.


Ken Clarkson, IBM
Coresets, Sparse Greedy Approximation, and the Frank-Wolfe Algorithm

The problem of maximizing a concave function f(x) in a simplex S can be solved approximately by a simple greedy algorithm. That algorithm can find a point x_k, in k steps, so that f(x_k) _> f(x*) - O(1/k) - O(1/k), where f(x*) is the maximum value of f in S. This algorithm and analysis were known before, and related to problems of statistics and machine learning, such as boosting, training support vector machines, regression, and density mixture estimation. In other work, coming from computational geometry, the existence of ?-coresets was shown for the minimum enclosing ball problem, by means of a simple greedy algorithm. These two algorithms, and some other similar greedy algorithms, are all special cases of the classical Frank-Wolfe algorithm. I'll tie these results together, review some stronger convergence results, and generalize or strengthen some coreset bounds.

April 7, 2009

Dominique Orban, Ecole Polytechnique de Montréal
The various facets of degeneracy in smooth optimization

At the term of two decades of boiling activity in the optimization community, interior-point methods for smooth problems emerge as the de-facto standard for the efficient solution of large-scale linear, convex, and nonconvex programming problems. Not only are those methods robust and scalable, they turn out to be equally well suited, with little modification, to so-called degenerate problems. In this talk, we review various aspects of degeneracy in nonlinear programming, their origins, their effect on the problem, the algorithm, and the linear algebra at the core of interior-point methods, and the remedies that can be put in place.


Pat Piperni, Bombardier Aerospace
Preliminary Aero-Structural Optimization of a Business Jet

An overview of an industrial approach to the aero-structural optimization of a large business jet is presented herein. The optimization methodology is based on the integration of aerodynamic and structural analysis codes that combine computational, analytical, and semi-empirical methods, validated in an aircraft design environment. The aerodynamics sub-space is analyzed with a three-dimensional Transonic Small Disturbance code capable of predicting the drag of a complete, trimmed aircraft within engineering accuracy. The design of the wing structure is
accomplished using a quasi-analytical method that defines the layout of the ribs and geometry of the spar webs, spar-caps and skin-stringer panels, and predicts the wing flexural properties and weight distribution. In addition, the prediction of operating economics as well as the integrated en route performance is coupled into the scheme by way of fractional change functional transformations. In order to illustrate the automated design system capabilities, the methodology is applied to the optimization of a large business jet comprising winglets, rear-mounted engines and a
T-tail configuration. The aircraft-level design optimization goal in this instance is to minimize a cost function for a fixed range mission assuming a constant Maximum Take-Off Weight.

March 3, 2009

Audio and Slides of Talk
Matheus Grasselli
, McMaster University
Managerial flexibility in incomplete markets and systems of RBSDEs
We model managerial flexibility between different modes of project as a compound real options problem. In incomplete markets this typically leads to a utility optimization problem, related to the indifference value of the relevant cash-flows, combined with an optimal stopping problem, related to the early exercise nature of the managerial decisions. I will explain how to formulate both problems in framework of systems of reflected backward stochastic differential equations, use comparison results to characterize how the exercise thresholds vary with the model parameters, and confirm these dependencies through numerical experiments.

Helmut Mausser, Algorithmics Toronto
Credit Risk Optimization
Measures of a portfolio's credit risk are typically based on extreme quantiles of the credit loss distribution. While a simulation-based framework allows credit risk to be assessed under realistic assumptions, the large number of samples, or scenarios, required to obtain robust risk estimates presents a computational challenge. In this context, we describe and compare several optimization models that derive from a structural, or Merton-type, model for credit risk. In particular, we consider formulations that combine scenarios with analytic approximations in order to reduce the volume of data required in the optimization.
This is based on joint work with Ian Iscoe, Alex Kreinin and Oleksandr Romanko.

Feb. 3, 2009

Audio and Slides of Talk
Ross Mitchell
, University of Calgary
Automatic Fusion of 3D Data: An Important Optimization Task in Modern Science

Alignment of three-dimensional (3D) datasets is an important optimization task in many scientific disciplines. For example, it is used in geophysics for prospecting and seismic monitoring. It is also used to visualize and compare changes in the spatial distribution of activated genes within multi-cellular organisms. It is an important tool for monitoring climate change, since it aids the assessment of remotely sensed, multi-spectral, satellite data of the earth. In medicine, alignment is needed to correct for motion during medical imaging, and to combine medical images from multiple individuals to create “atlases” or maps of normal and abnormal anatomy, physiology or function. And in clinical practice, automatic alignment is used to detect changes in 3D medical images to aid the diagnosis, treatment, and monitoring of disease.

However, widespread use of automatic 3D alignment algorithms has been hindered by their computational costs, lengthy execution times and dif?culties in tuning and guidance. We describe a new method that executes on commodity graphical processing units (GPUs) and is designed to take advantage of their massive parallelism and high memory bandwidth. Our method allows interactive, high quality visualization of both rigid and deformable 3D alignment of multi-contrast datasets. It has accuracy equal to, or better than, several common techniques, yet reduces computation time by one to two orders of magnitude.


Andrew Hope, Princess Margaret Hospital Toronto
Medical Imaging and Radiation Oncology: Targeting to Cure

Medical imaging plays a crucial role in the diagnosis, treatment, and monitoring of cancer patients and their disease. Radiation oncologists rely on accurate imaging to plan and target cancer therapy that is, largely, non-invasive. Advances in imaging now allow physicians to visualize motion, integrate data from multiple studies, correct images for distortion during treatment. Imaging is now an integrated part of modern radiation therapy and is applied daily. In this overview, we will explore recent advances in medical imaging including four-dimensional computed tomography, cone- beam computed tomography, and the use of multi-modality imaging for target delineation.

Dec. 2, 2008

Audio and Slides of Talk

Michel X. Goemans
Approximating Submodular Functions Everywhere
Submodular functions are a key concept in combinatorial optimization. Algorithms that involve submodular functions usually assume that they are given by a (value) oracle. We consider the problem of approximating a non-negative, monotone, submodular function f everywhere, after only poly(n) oracle queries (where n is the size of the ground set). Our main result is a deterministic algorithm that makes poly(n) oracle queries and derives a function such that, for *every* set S, g(S) approximates f(S) within a factor a(n), where a(n)=sqrt(n+1) for rank functions of matroids and a(n)=O(sqrt(n) log(n)) for general monotone submodular functions. Our result is based on approximately finding a maximum volume inscribed ellipsoid in a symmetrized polymatroid, and the analysis involves various properties of submodular functions and polymatroids. Our algorithm is tight up to logarithmic factors, even for rank functions of a matroid. Our approximation result allows to approximate optimization problems involving submodular functions, such as the submodular max-min fair allocation problem.
This is based on joint work with Nick Harvey, Satoru Iwata and Vahab Mirrokni.

Jon Lee, IBM TJ Watson Research Center
Nonlinear Combinatorial Optimization
I will discuss algorithms for solving problems of the form max { f(Wx) : x in F }, where f is a nonlinear function, the rows of the matrix W can be thought of as describing several linear objectives, and F is a discrete set of feasible solutions. The nonlinear function f can be though of as balancing the various linear objectives.We look at various combinatorial settings for the choices of F. So this can be seen to fit somewhere on the landscape between multicriteria optimization and nonlinear combinatorial optimization.
In the most general setting, the model is intractable, so we look at cases that yield polynomial-time algorithms and approximation schemes. Our specific results involve broad special cases. E.g., regarding F, we consider multi-knapsacks, matchings, matroids and polymatroids, Regarding f, we consider minimization of concave and convex functions, generalization of norms, etc. Regarding W, to get positive results, we usually need to assume that it has a fixed number of rows and the entries are encoded in unary. I will describe an interesting application to fitting polynomial models to multivariate data.
Most of our algorithms were mainly designed for theoretical efficiency. Nonetheless, there is a good argument for trying to implememt some of these methods on modern high-performance platforms. We describe such a successful effort for the model fitting application, using ultra-high precision solution of linear systems on the BlueGene supercomputer.
All of this is joint work, difererent parts with Berstein, Gunnels, Margulies, Maruri-Aguilar, Onn, Riccomagno, Weismantel, Wynn.

Oct. 7, 2008

Audio and Slides of Talk

Endre Boros, Rutgers University
Quadratic binary optimization and its applications
Quadratic binary optimization (QUBO) is a natural mathematical model for numerous practical problems. In this overview lecture we recall the origins of this model, and some of the numerous results providing efficient solutions in some case, or efficient bounds and persistencies in some other cases. We shall also discuss connections to the very popular "graph-cut" approach of Computer vision, and provide extensive computational experiments with various applications.

Gabriel Tavares, Fair Isaac Corporation
Linear Programming Plus Branch-and-cut for Solving Certain Difficult QUBO Problems
A linear optimization framework is proposed to solve the Quadratic Unconstrained Binary Optimization (QUBO) problem. It consists of 3 stages: stage (i) calculates an incumbent solution by using QUBO heuristics (a Multi-Start Tabu-Search approach is considered); stage (ii) calculates an optimal Basic Feasible Solution (BFS) to the relaxation of the Linearization Model (LM) for QUBO plus some valid cuts; and stage (iii) solves the mixed integer program LM by using the incumbent solution and by enhancing it with the subset of cuts binding at the BFS. The effectiveness of the approach, implemented using Xpress-Mosel, is shown when solving: minimum 3-partition, MAX-CUT and nMAX-SAT.

Nov. 4, 2008

Audio and Slides of Talk

Philip E. Gill, University of California, San Diego
What's new in SQP methods?
In his 1963 PhD thesis, Wilson proposed the first sequential quadratic programming (SQP) method for the solution of constrained nonlinear optimization problems. In the intervening 45 years, SQP methods have evolved into a powerful and effective class of methods for a wide range of optimization problems. We review some of the most prominent developments in SQP methods since 1963 and discuss the relationship of SQP methods to other popular methods, including augmented Lagrangian methods and interior methods.
Given the scope and utility of nonlinear optimization, it is not surprising that SQP methods are still a subject of active research. Recent developments in methods for mixed integer nonlinear programming and the minimization of functions subject to differential equation constraints has led to a heightened interest in methods that may be ``hot started'' from a good approximate solution. We discuss the role of SQP methods in this context, with particular reference to some recent enhancements to our large-scale SQP package SNOPT. We end with some discussion of the challenges associated with formulating algorithms that can exploit multicore and GPU-based computer architectures.

Bohdan Kaluzny, Centre for Operational Research & Analysis, Defence Research & Development Canada.
Combinatorial Optimization in Canadian Forces Airlift Modeling
The Canadian Forces make extensive use of strategic and tactical airlift assets to deploy and sustain overseas forces. Various-sized cargo aircraft are employed for missions to deliver equipment, supplies, and passengers. Maximizing the payload delivered per flight while maintaining a safe load balance is of high importance. This talk presents Genetic Annealing for Loading of Aircraft: a Heuristic Aiding Deployment (GALAHAD). This loading module uses a genetic algorithm based on a bin packing heuristic and a convex hull fitness function to generate realistic estimates of the number of airlift assets required to transport a given manifest of items. The generated load plans are subject to load balancing constraints which are modeled by Mixed Integer Linear Programming.