On the Power of Robust Solutions in Two-Stage Stochastic and Adaptive Optimization Problems

We consider a two-stage mixed integer stochastic optimization problem and show that a static robust solution is a good approximation to the fully adaptable two-stage solution for the stochastic problem under fairly general assumptions on the uncertainty set and the probability distribution. In particular, we show that if the right-hand side of the constraints is uncertain and belongs to a symmetric uncertainty set (such as hypercube, ellipsoid or norm ball) and the probability measure is also symmetric, then the cost of the optimal fixed solution to the corresponding robust problem is at most twice the optimal expected cost of the two-stage stochastic problem. Furthermore, we show that the bound is tight for symmetric uncertainty sets and can be arbitrarily large if the uncertainty set is not symmetric. We refer to the ratio of the optimal cost of the robust problem and the optimal cost of the two-stage stochastic problem as the stochasticity gap. We also extend the bound on the stochasticity gap for another class of uncertainty sets referred to as positive. If both the objective coefficients and right-hand side are uncertain, we show that the stochasticity gap can be arbitrarily large even if the uncertainty set and the probability measure are both symmetric. However, we prove that the adaptability gap (ratio of optimal cost of the robust problem and the optimal cost of a two-stage fully adaptable problem) is at most four even if both the objective coefficients and the right-hand side of the constraints are uncertain and belong to a symmetric uncertainty set. The bound holds for the class of positive uncertainty sets as well. Moreover, if the uncertainty set is a hypercube (special case of a symmetric set), the adaptability gap is one under an even more general model of uncertainty where the constraint coefficients are also uncertain.National Science Foundation (U.S.) (NSF Grant DMI-0556106)National Science Foundation (U.S.) (NSF Grant EFRI-0735905

A First Derivative Potts Model for Segmentation and Denoising Using ILP

Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel ILP formulation of the first derivative Potts model with the $\ell_1$ data term, where binary variables are introduced to deal with the $\ell_0$ norm of the regularization term. The ILP is then solved by a standard off-the-shelf MIP solver. Numerical experiments are compared with the multicut problem.Comment: 6 pages, 2 figures. To appear at Proceedings of International Conference on Operations Research 2017, Berli

On the Power and Limitations of Affine Policies in Two-Stage Adaptive Optimization

We consider a two-stage adaptive linear optimization problem under right hand side uncertainty with a min–max objective and give a sharp characterization of the power and limitations of affine policies (where the second stage solution is an affine function of the right hand side uncertainty). In particular, we show that the worst-case cost of an optimal affine policy can be Omega(m12−) times the worst-case cost of an optimal fully-adaptable solution for any delta > 0, where m is the number of linear constraints. We also show that the worst-case cost of the best affine policy is O(m) times the optimal cost when the first-stage constraint matrix has non-negative coefficients. Moreover, if there are only k ≤ m uncertain parameters, we generalize the performance bound for affine policies to O(k) , which is particularly useful if only a few parameters are uncertain. We also provide an O(k) -approximation algorithm for the general case without any restriction on the constraint matrix but the solution is not an affine function of the uncertain parameters. We also give a tight characterization of the conditions under which an affine policy is optimal for the above model. In particular, we show that if the uncertainty set, R+m is a simplex, then an affine policy is optimal. However, an affine policy is suboptimal even if is a convex combination of only (m + 3) extreme points (only two more extreme points than a simplex) and the worst-case cost of an optimal affine policy can be a factor (2 − delta) worse than the worst-case cost of an optimal fully-adaptable solution for any delta > 0.National Science Foundation (U.S.) (NSF Grants DMI-0556106)National Science Foundation (U.S.) (EFRI-0735905

Delay, memory, and messaging tradeoffs in distributed service systems

We consider the following distributed service model: jobs with unit mean, exponentially distributed, and independent processing times arrive as a Poisson process of rate $\lambda n$, with $0<\lambda<1$, and are immediately dispatched by a centralized dispatcher to one of $n$ First-In-First-Out queues associated with $n$ identical servers. The dispatcher is endowed with a finite memory, and with the ability to exchange messages with the servers. We propose and study a resource-constrained "pull-based" dispatching policy that involves two parameters: (i) the number of memory bits available at the dispatcher, and (ii) the average rate at which servers communicate with the dispatcher. We establish (using a fluid limit approach) that the asymptotic, as $n\to\infty$, expected queueing delay is zero when either (i) the number of memory bits grows logarithmically with $n$ and the message rate grows superlinearly with $n$, or (ii) the number of memory bits grows superlogarithmically with $n$ and the message rate is at least $\lambda n$. Furthermore, when the number of memory bits grows only logarithmically with $n$ and the message rate is proportional to $n$, we obtain a closed-form expression for the (now positive) asymptotic delay. Finally, we demonstrate an interesting phase transition in the resource-constrained regime where the asymptotic delay is non-zero. In particular, we show that for any given $\alpha>0$ (no matter how small), if our policy only uses a linear message rate $\alpha n$, the resulting asymptotic delay is upper bounded, uniformly over all $\lambda<1$; this is in sharp contrast to the delay obtained when no messages are used ($\alpha = 0$), which grows as $1/(1-\lambda)$ when $\lambda\uparrow 1$, or when the popular power-of-$d$-choices is used, in which the delay grows as $\log(1/(1-\lambda))$

Probabilistic combinatorial optimization: Moments, semidefinite programming, and asymptotic bounds

10.1137/S1052623403430610SIAM Journal on Optimization151185-20

Product forms as a solution base for queueing systems

A class of queueing networks has a product-form solution. It is interesting to investigate which queueing systems have solutions in the form of linear combinations of product forms. In this paper it is investigated when the equilibrium distribution of one or two-dimensional Markovian queueing systems can be written as linear combination of products of powers. Also some cases with extra supplementary variables are investigated

Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data

Stochastic network design is fundamental to transportation and logistic problems in practice, yet faces new modeling and computational challenges resulted from heterogeneous sources of uncertainties and their unknown distributions given limited data. In this article, we design arcs in a network to optimize the cost of single‐commodity flows under random demand and arc disruptions. We minimize the network design cost plus cost associated with network performance under uncertainty evaluated by two schemes. The first scheme restricts demand and arc capacities in budgeted uncertainty sets and minimizes the worst‐case cost of supply generation and network flows for any possible realizations. The second scheme generates a finite set of samples from statistical information (e.g., moments) of data and minimizes the expected cost of supplies and flows, for which we bound the worst‐case cost using budgeted uncertainty sets. We develop cutting‐plane algorithms for solving the mixed‐integer nonlinear programming reformulations of the problem under the two schemes. We compare the computational efficacy of different approaches and analyze the results by testing diverse instances of random and real‐world networks. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 154–173, 2017Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137236/1/nav21739_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137236/2/nav21739.pd

Decomposition Algorithms for Analyzing Transient Phenomena in Multi-class Queueing Networks in Air Transportation

In a previous paper (Peterson, Bertsimas, and Odoni 1992), we studied the phenomenon of transient congestion in landings at a hub airport and developed a recursive approach for computing moments of queue lengths and waiting times. In this paper we extend our approach to a network, developing two approximations based on the method used for the single hub. We present computational results for a simple 2-hub network and indicate the usefulness of the approach in analyzing the interaction between hubs. Although our motivation is drawn from air transportation, our method is applicable to all multi-class queuing networks where service capacity at a station may be modeled as a Markov or semi-Markov process. Our method represents a new approach for analyzing transient congestion phenomena in such networks. Airport congestion and delay have grown significantly over the last decade. By 1986 ground delays at domestic airports averaged 2000 hours per day, the equivalent of grounding the entire fleet of Delta Airlines at that tillie (250 aircraft) for one day (Donoghue 1986). In 1990, 21 airports in the U.S. exceeded 20, 000 hours of delay, with 12 more projected to exceed this total by 1997 (National Transportation Research Board 1991). This amounts to *School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana tSloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts ;Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusett