We study disruptions at a major airport. Disruptions could be caused by bad weather, for example. Our study is from the perspective of the airport, the air services provider (such as air traffic control) and the travelling public, rather than from the perspective of a single airline. Disruptions cause flights to be subjected to ground … Read more
We propose a Fully Polynomial-Time Approximation Scheme (FPTAS) for stochastic dynamic programs with multidimensional action, scalar state, convex costs and linear state transition function. The action spaces are polyhedral and described by parametric linear programs. This type of problems finds applications in the area of optimal planning under uncertainty, and can be thought of as … Read more
We consider the coordination of planning decisions of a single product in a supply chain composed of one supplier and one retailer by using contracts. We assume that the retailer has the market power to impose his optimal replenishment plan to the supplier. Our concern is on the minimization of the supplier’s cost. In order … Read more
The minimum $k$-enclosing ball problem seeks the ball with smallest radius that contains at least $k$ of $m$ given points in a general $n$-dimensional Euclidean space. This problem is NP-hard. We present a branch-and-bound algorithm on the tree of the subsets of $k$ points to solve this problem. The nodes on the tree are ordered … Read more
In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA can achieve an optimal ${\cal O}(1/\epsilon^4)$ rate of convergence in terms … Read more
We propose a unifying algorithm for non-smooth non-convex optimization. The algorithm approximates the objective function by a convex model function and finds an approximate (Bregman) proximal point of the convex model. This approximate minimizer of the model function yields a descent direction, along which the next iterate is found. Complemented with an Armijo-like line search … Read more
For a type of multi-block separable convex programming raised in machine learning and statistical inference, we propose a proximal alternating direction method of multiplier (PADMM) with partially parallel splitting, which has the following nice properties: (1) To alleviate the weight of the proximal terms, the restrictions imposed on the proximal parameters are relaxed substantively; (2) … Read more
This paper presents the development of an enhanced L-Shaped method applied to an inventory management problem that considers a replenishment control system based on the periodic review (R,S) policy. We consider single-item one-echelon problems with uncertain demands and partial backorder that are modeled using two-stage stochastic programming. To enable the consideration of large-scale problems, the … Read more
Christofides and Hadjiconstantinou introduced a dynamic programming state space relaxation for obtaining upper bounds for the Constrained Two-dimensional Guillotine Cutting Problem. The quality of those bounds depend on the chosen item weights, they are adjusted using a subgradient-like algorithm. This paper proposes Algorithm X, a new weight adjusting algorithm based on integer programming that provably … Read more
Recently, a generalized version of Peaceman-Rachford splitting method (GPRSM) for solving a convex minmization model with a general separable structure has been proposed by \textbf{Sun} et al and its global convergence has been proved. In this paper, we further study theoretical aspects of the generalized Peaceman-Rachford splitting method. We first establish the worst-case $\mathcal{O}(1/t)$ convergence … Read more