Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason, practitioners and researchers have recently focused on the minimum-variance portfolio, which … Read more
Airlines must dynamically choose how to allocate their flight capacity to incoming travel demand. Because some passengers take connecting flights, the decisions for all network flights must be made simultaneously. To simplify the decision making process, most practitioners assume demand is deterministic and equal to average demand. We propose a multistage stochastic programming approach that … Read more
Network migration scheduling is the problem where inter-nodal traffic from an outdated telecommunications network is to be migrated to a new network. Nodes are migrated, one at each time period, from the old to the new network. All traffic originating or terminating at given node in the old network is moved to a specific node … Read more
The quadratic assignment problem (\QAP) is arguably one of the hardest of the NP-hard discrete optimization problems. Problems of dimension greater than 20 are considered to be large scale. Current successful solution techniques depend on branch and bound methods. However, it is difficult to get \emph{strong and inexpensive} bounds. In this paper we introduce a … Read more
Under the Open Shortest Path First (OSPF) protocol, traffic flow in an Internet Protocol (IP) network is routed on the shortest paths between each source and destination. The shortest path is calculated based on pre-assigned weights on the network links. The OSPF weight setting problem is to determine a set of weights such that, if … Read more
It is a matter of common knowledge that traditional Markowitz optimization based on sample means and covariances performs poorly in practice. For this reason, diverse attempts were made to improve performance of portfolio optimization. In this paper, we investigate three popular portfolio selection models built upon classical mean-variance theory. The first model is an extension … Read more
This paper addresses a constrained two-dimensional (2D) non-guillotine cutting problem, where a fixed set of small rectangles has to be cut from a larger stock rectangle so as to maximize the value of the rectangles cut. The algorithm we propose hybridizes a novel placement procedure with a genetic algorithm based on random keys. We propose … Read more
Multi-level production planning problems in which multiple items compete for the same resources frequently occur in practice, yet remain daunting in their difficulty to solve. In this paper we propose a heuristic framework that can generate high quality feasible solutions quickly for various kinds of lot-sizing problems. In addition, unlike many other heuristics, it generates … Read more
This paper is the first of a series of two devoted to the polyhedral study of a strongly NP-hard resource-constrained scheduling problem, referred to as the process move programming problem. This problem arises in relation to the operability of certain high-availability real time distributed systems. After a brief introduction to the problem as well as … Read more
This paper is the second of a series of two devoted to the polyhedral study of a strongly NP-hard resource-constrained scheduling problem, referred to as the process move programming problem. In the present paper, we put back into the picture the capacity constraints which were ignored in the first paper. In doing so, we introduce … Read more