Parallel Space Decomposition of the Mesh Adaptive Direct Search algorithm

This paper describes a parallel space decomposition PSD technique for the mesh adaptive direct search MADS algorithm. MADS extends a generalized pattern search for constrained nonsmooth optimization problems. The objective of the present work is to obtain good solutions to larger problems than the ones typically solved by MADS. The new method PSD-MADS is an … Read more

OrthoMADS: A deterministic MADS instance with orthogonal directions

he purpose of this paper is to introduce a new way of choosing directions for the mesh adaptive direct search (Mads) class of algorithms. The advantages of this new OrthoMads instantiation of Mads are that the polling directions are chosen deterministically, ensuring that the results of a given run are repeatable, and that they are … Read more

Exact and heuristic solutions of the global supply chain problem with transfer pricing

We examine the example of a multinational corporation that attempts to maximize its global after tax profits by determining the flow of goods, the transfer prices, and the transportation cost allocation between each of its subsidiaries. Vidal and Goetschalckx (2001) proposed a bilinear model of this problem and solved it by an Alternate heuristic. We … Read more

Passenger Name Record Data Mining Based Cancellation Forecasting for Revenue Management

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation … Read more

Geometric Rounding: A Dependent Rounding Scheme for Allocation Problems

This paper presents a general technique to develop approximation algorithms for allocation problems with integral assignment constraints. The core of the method is a randomized dependent rounding scheme, called geometric rounding, which yields termwise rounding ratios (in expectation), while emphasizing the strong correlation between events. We further explore the intrinsic geometric structure and general theoretical … Read more

A Hybrid Relax-and-Cut/Branch-and-Cut Algorithm for the Degree-Constrained Minimum Spanning Tree Problem

A new exact solution algorithm is proposed for the Degree-Constrained Minimum Spanning Tree Problem. The algorithm involves two combined phases. The first one contains a Lagrangian Relax-and-Cut procedure while the second implements a Branch-and-Cut algorithm. Both phases rely on a standard formulation for the problem, reinforced with Blossom Inequalities. An important feature of the proposed … Read more

An exact algorithm for solving the ring star problem

This paper deals with the ring star problem that consists in designing a ring that pass through a central depot, and then assigning each non visited customer to a node of the ring. The objective is to minimize the total routing and assignment costs. A new chain based formulation is proposed. Valid inequalities are proposed … Read more

Linear Programming for Mechanism Design: An Application to Bidder Collusion at First-Price Auctions

We demonstrate the use of linear programming techniques in the analysis of mechanism design problems. We use these techniques to analyze the extent to which a first-price auction is robust to collusion when, contrary to some prior literature on collusion at first-price auctions, the cartel cannot prevent its members from bidding at the auction. In … Read more

Integrated Forecasting and Inventory Control for Seasonal Demand: a Comparison with the Holt-Winters Approach

We present a data-driven forecasting technique with integrated inventory control for seasonal data and compare it to the traditional Holt-Winters algorithm. Results indicate that the data-driven approach achieves a 2-5% improvement in the average regret. Citation Technical Report, Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, PA. Article Download View Integrated Forecasting and Inventory … Read more

Tractable Robust Expected Utility and Risk Models for Portfolio Optimization

Expected utility models in portfolio optimization is based on the assumption of complete knowledge of the distribution of random returns. In this paper, we relax this assumption to the knowledge of only the mean, covariance and support information. No additional assumption on the type of distribution such as normality is made. The investor’s utility is … Read more