Cutting Plane Algorithms for 0-1 Programming Based on Cardinality Cuts

Abstract: We present new valid inequalities for 0-1 programming problems that work in similar ways to well known cover inequalities. Discussion and analysis of these cuts is followed by their revision and use in integer programming as a new generation of cuts that excludes not only portions of polyhedra containing noninteger points, also parts with … Read more

The Knapsack Problem with Conflict Graphs

We extend the classical 0-1 knapsack problem by introducing disjunctive constraints for pairs of items which are not allowed to be packed together into the knapsack. These constraints are represented by edges of a conflict graph whose vertices correspond to the items of the knapsack problem. Similar conditions were treated in the literature for bin … Read more

A Multistage Stochastic Programming Approach to Open Pit Mine Production Scheduling with Uncertain Geology

The Open Pit Mine Production Scheduling Problem (OPMPSP) studied in recent years is usually based on a single geological estimate of material to be excavated and processed over a number of decades. However techniques have now been developed to generate multiple stochastic geological estimates that more accurately describe the uncertain geology. While some attempts have … Read more

On complexity of Selecting Branching Disjunctions in Integer Programming

Branching is an important component of branch-and-bound algorithms for solving mixed integer linear programs. We consider the problem of selecting, at each iteration of the branch-and-bound algorithm, a general branching disjunction of the form $“\pi x \leq \pi_0 \vee \pi x \geq \pi_0 + 1”$, where $\pi, \pi_0$ are integral. We show that the problem … Read more

Lattice-based Algorithms for Number Partitioning in the Hard Phase

The number partitioning problem (NPP) is to divide n numbers a_1,…,a_n into two disjoint subsets such that the difference between the two subset sums – the discrepancy, D, is minimized. In the balanced version of NPP (BalNPP), the subsets must have the same cardinality. With $a_j$s chosen uniformly from $[1,R]$, R > 2^n gives the … Read more

Solving the Rectangular assignment problem and applications

The rectangular assignment problem is a generalization of the linear assignment problem (LAP): one wants to assign a number of persons to a smaller number of jobs, minimizing the total corresponding costs. Applications are, e.g., in the fields of object recognition and scheduling. Further, we show how it can be used to solve variants of … Read more

Modeling and Solving Location Routing and Scheduling Problems

This paper studies location routing and scheduling problems, a class of problems in which the decisions of facility location, vehicle routing, and route assignment are optimized simultaneously. For a version with capacity and time restrictions, two formulations are presented, one graph-based and one set-partitioning-based. For the set-partitioning-based formulation, valid inequalities are identified and their effectiveness … Read more

On LP Relaxations for the Pattern Minimization Problem

We discuss two formulations of the Pattern Minimization Problem: (1) introduced by Vanderbeck, and (2) obtained adding setup variables to the cutting stock formulation by Gilmore-Gomory. Let $z_i^{LP}(u)$ be the bound given by the linear relaxation of ($i$) under a given vector $u = (u_k)$ of parameters. We show that $z_2^{LP}(u}) \ge z_1^{LP}(u)$ and provide … Read more

Mixed-Integer Models for Nonseparable Piecewise Linear Optimization: Unifying Framework and Extensions

We study the modeling of non-convex piecewise linear functions as Mixed Integer Programming (MIP) problems. We review several new and existing MIP formulations for continuous piecewise linear functions with special attention paid to multivariate non-separable functions. We compare these formulations with respect to their theoretical properties and their relative computational performance. In addition, we study … Read more

Implicitely and Densely Discrete Black-Box Optimization Problems

This paper addresses derivative-free optimization problems where the variables lie implicitly in an unknown discrete closed set. The evaluation of the objective function follows a projection onto the discrete set, which is assumed dense rather than sparse. Such a mathematical setting is a rough representation of what is common in many real-life applications where, despite … Read more