Finding Improving Solutions that Control Disruption to Binary Optimization Problems

Conventional optimization solvers provide a single optimal solution to an optimization model, which in some cases is undesirable to the decision maker because of the large discrepancy between the optimal solution and the existing conditions of the real-world situation the model represents. This project focuses on developing an algorithm and computational program to generate solutions to binary integer optimization problems that can simultaneously improve the objective function value and yet control disruption from the current condition. The program uses Dinkelbach’s algorithm to determine such a solution, and is implemented in Excel utilizing Visual Basic for Applications (VBA) in conjunction with OpenSolver. Detailed instructions are included to guide users through the entire process

On Downloading and Using CPLEX within COIN-OR for Solving Linear/Integer Optimization Problems

The aim of this technical report is to present some detailed explanations in order to use the solver CPLEX within COIN-OR environment. In particular, we describe how to download, install and use the corresponding source code and libraries under Windows and Linux operating systems. We will use an example taken from the literature, with the experimental code and files written in C++, to describe the whole process of editing, compiling and running the executable, to solve this optimization problem by using this software. In the case of the Windows environment, a C++ compiler is also needed. We will use the Visual C++ 2010 Express Edition.CPLEX, COIN-OR, C++

A General Large Neighborhood Search Framework for Solving Integer Programs

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi