Domination between traffic matrices

A traffic matrix $D^1$ dominates a traffic matrix $D^2$ if $D^2$ can be routed on every (capacitated) network where $D^1$ can be routed. We prove that $D^1$ dominates $D^2$ if and only if $D^1$, considered as a capacity vector, supports $D^2$. We show several generalizations of this result. Citation Centro Vito Volterra, Universita’ di Roma … Read more

Reduction Tests for the Prize-Collecting Steiner Problem

The Prize-Collecting Steiner Problem (PCSP) is a generalization of the classical Steiner Problem in Graphs (SPG) where instead of terminal vertices that must be necessarily connected, one have profits associated to the vertices that must be balanced against the connection costs. This problem is gaining much attention in the last years due to its practical … Read more

Note: A Graph-Theoretical Approach to Level of Repair Analysis

Level of Repair Analysis (LORA) is a prescribed procedure for defence logistics support planning. For a complex engineering system containing perhaps thousands of assemblies, sub-assemblies, components, etc. organized into several levels of indenture and with a number of possible repair decisions, LORA seeks to determine an optimal provision of repair and maintenance facilities to minimize … Read more

Robust Profit Opportunities in Risky Financial Portfolios

For risky financial securities with given expected return vector and covariance matrix, we propose the concept of a robust profit opportunity in single and multiple period settings. We show that the problem of finding the “most robust” profit opportunity can be solved as a convex quadratic programming problem, and investigate its relation to the Sharpe … Read more

Interior point methods for large-scale linear programming

We discuss interior point methods for large-scale linear programming, with an emphasis on methods that are useful for problems arising in telecommunications. We give the basic framework of a primal-dual interior point method, and consider the numerical issues involved in calculating the search direction in each iteration, including the use of factorization methods and/or preconditioned … Read more

Leader-Follower Equilibria for Electric Power and NO_x Allowances Markets

This paper investigates the ability of the largest producer in an electricity market to manipulate both the electricity and emission allowances markets to its advantage. A Stackelberg game to analyze this situation is constructed in which the largest firm plays the role of the leader, while the medium-sized firms are treated as Cournot followers with … Read more

Efficiency and Fairness of System-Optimal Routing with User Constraints

We study the route-guidance system proposed by Jahn, Möhring, Schulz and Stier-Moses (2004) from a theoretical perspective. This approach computes a traffic pattern that minimizes the total travel time subject to user constraints, which ensure that routes suggested to users are not much longer than shortest paths. We show that when distances are measured with … Read more

Optimizing Call Center Staffing using Simulation and Analytic Center Cutting Plane Methods

We present a simulation-based analytic center cutting plane method to solve a sample average approximation of a call center problem of minimizing staffing costs, while maintaining an acceptable level of service in multiple time periods. We establish convergence of the method when the service level functions are discrete pseudoconcave. An extensive numerical study of a … Read more

Security-constrained transmission planning: A mixed-integer disjunctive approach

We extend a static mixed intger diajunctive (MID) transmission expansion planning model so as to deal with circuit contingency criterion. The model simultaneously represents the network constraints for base case and each selected circuit contingency. The MID approach aloows a commercial optimization solver to achieve and prove solution aptimiality. The proposed approach is applied to … Read more

Stochastic p-Robust Location Problems

Many objectives have been proposed for optimization under uncertainty. The typical stochastic programming objective of minimizing expected cost may yield solutions that are inexpensive in the long run but perform poorly under certain realizations of the random data. On the other hand, the typical robust optimization objective of minimizing maximum cost or regret tends to … Read more