AN INTELLIGENT SYSTEM FOR FORMULATING LINEAR PROGRAMS

The research and system development work described in this paper is aimed at overcoming some of the problems associated with the development of large, complex linear programming problems. The most overwhelming problem is that of size. It is not uncommon for large planning and policy analysis problems to have tens of thousands of constraints and activities. Matrix generator systems have been designed to help in this process. However, the amount of manual labor involved is still very great and the formulation process is subject to errors which are difficult to detect. We provide an overview of a system which uses artificial intelligence and database techniques to help a knowledgeable user formulate large linear programs. The system automates many of the tedious processes associated with large-scale modeling and provides a top-down development environment with a number of different forms of problem representation.Information Systems Working Papers Serie

THE SCIENCE AND ART OF FORMULATING LINEAR PROGRAMS

This paper describes the philosophy underlying the development of an intelligent system to assist in the formulation of large linear programs. The LPFORM system allows users to state their problem using a graphical rather than an algebraic representation. A major objective of the system is to automate the bookkeeping involved in the development of large systems. It has expertise related to the structure of many of the common forms of linear programs (e.g. transportation, product-mix and blending problems) and of how these prototypes may be combined into more complex systems. Our approach involves characterizing the common forms of LP problems according to whether they are transformations in place, time or form. We show how LPFORM uses knowledge about the structure and meaning of linear programs to construct a correct tableau. Using the symbolic capabilities of artificial intelligence languages, we can manipulate and analyze some properties of the LP prior to actually generating a matrix.Information Systems Working Papers Serie

TAIP: an anytime algorithm for allocating student teams to internship programs

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process. Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.Comment: 10 pages, 7 figure

A simple dual ascent algorithm for the multilevel facility location problem

We present a simple dual ascent method for the multilevel facility location problem which finds a solution within $6$ times the optimum for the uncapacitated case and within $12$ times the optimum for the capacitated one. The algorithm is deterministic and based on the primal-dual technique. \u

Approximation by Basis Pursuit: Background and Application to the Construction of Efficient Spline Approximations

Basis Pursuit was developed primarily as a tool in the field of signal processing, beginning in the mid 1990’s. The idea is to model the behavior of discrete signals using a wide range of functional behaviors and scales and to obtain an accurate and efficient representation of the signal using a minimal number of functions from a large “dictionary” of possible behaviors. The key observation is by formulating the representation as an ℓ1 optimization, the problem can be posed as a linear program so that the optimal solution uses no more than the number of constraints - it must be a basic feasible solution. While the problem has been explored in signal processing, we are here interested in the possible application to approximation of functions as classically considered in analysis. We present a number of applications in approximation with a dictionary consisting of multiresolution cubic spline spaces, with varying objective functions, but optimizations that ultimately must minimize a linear objective function. While signal processing applications are concerned with efficient solution of very large linear programs, here we can limit the sizes of the problems and study the nature of the solutions themselves. We use interpolation, uniform approximation, and formulations involving blended approximation, with objective functions involving ℓ1 terms blended with uniform or quadratic penalty functions

RCD: Rapid Close to Deadline Scheduling for Datacenter Networks

Datacenter-based Cloud Computing services provide a flexible, scalable and yet economical infrastructure to host online services such as multimedia streaming, email and bulk storage. Many such services perform geo-replication to provide necessary quality of service and reliability to users resulting in frequent large inter- datacenter transfers. In order to meet tenant service level agreements (SLAs), these transfers have to be completed prior to a deadline. In addition, WAN resources are quite scarce and costly, meaning they should be fully utilized. Several recently proposed schemes, such as B4, TEMPUS, and SWAN have focused on improving the utilization of inter-datacenter transfers through centralized scheduling, however, they fail to provide a mechanism to guarantee that admitted requests meet their deadlines. Also, in a recent study, authors propose Amoeba, a system that allows tenants to define deadlines and guarantees that the specified deadlines are met, however, to admit new traffic, the proposed system has to modify the allocation of already admitted transfers. In this paper, we propose Rapid Close to Deadline Scheduling (RCD), a close to deadline traffic allocation technique that is fast and efficient. Through simulations, we show that RCD is up to 15 times faster than Amoeba, provides high link utilization along with deadline guarantees, and is able to make quick decisions on whether a new request can be fully satisfied before its deadline.Comment: World Automation Congress (WAC), IEEE, 201