Integrating concepts from constraint programming and operations research algorithms

von Torsten FahlePaderborn, Univ., Diss., 200

How Much of the “Unconscious” is Just Pre – Threshold?

Visual awareness is a specific form of consciousness. Binocular rivalry, the alternation of visual consciousness resulting when the two eyes view differing stimuli, allows one to experimentally investigate visual awareness. Observers usually indicate the gradual changes of conscious perception in binocular rivalry by a binary measure: pressing a button. However, in our experiments we used gradual measures such as pupil and joystick movements and found reactions to start around 590 ms before observers press a button, apparently accessing even pre-conscious processes. Our gradual measures permit monitoring the somewhat gradual built-up of decision processes. Therefore these decision processes should not be considered as abrupt events. This is best illustrated by the fact that the process to take a decision may start but then stop before an action has been taken – which we will call an abandoned decision process here. Changes in analog measures occurring before button presses by which observers have to communicate that a decision process has taken place do not prove that these decisions are taken by a force other than the observer – hence eliminating “free will” – but just that they are prepared “pre-thresholdly,” before the observer considers the decision as taken

Cost Based Filtering vs. Upper Bounds for Maximum Clique

In this paper we consider a branch-and-bound algorithm for the maximum clique problem. We introduce cost based filtering techniques for the so-called candidate set (i.e. a set of nodes that can possibly extend the clique in the current choice point). Doing this, we can reduce the number of choice points visited by a typical factor of 10 -- 50. Additionally, we present a taxonomy of upper bounds used in the OR community for maximum clique. Analytical results show that our cost based filtering is in a sense as tight as most of these well-known bounds for the maximum clique problem

Constraint Programming based Lagrangian Relaxation for the Automatic Recording Problem

Whereas CP methods are strong with respect to the detection of local infeasibilities, OR approaches have powerful optimization abilities that ground on tight global bounds on the objective. An integration of propagation ideas from CP and Lagrangian relaxation techniques from OR combines the merits of both approaches. We introduce a general way of how linear optimization constraints can strengthen their propagation abilities via Lagrangian relaxation. The method is evaluated on a set of benchmark problems stemming from a multimedia application. The experiment

CP-based Lagrangian Relaxation for a Multimedia Application

Whereas CP methods are strong with respect to the detection of local infeasibilities, OR approaches have powerful optimization abilities that ground on tight global bounds on the objective. An integration of propagation ideas from CP and Lagrangian relaxation techniques from OR combines the merits of both approaches. We introduce a general way of how linear optimization constraints can strengthen their propagation abilities via Lagrangian relaxation. The algorithm is evaluated on a set of benchmark problems stemming from a multimedia application

Proceedings CPAIOR’03 A Hybrid Setup for a Hybrid Scenario: Combining Heuristics for the Home Health Care Problem

Home health care, i.e. visiting and nursing patients in their homes, is a growing sector in the medical service business. From a staff rostering point of view, the problem is to find a feasible working plan for all nurses that has to respect a variety of hard and soft constraints, and preferences. Additionally, home health care problems contain a routing component: A nurse must be able to visit her patients in a given roster using a car or public transport. It is desired to design rosters that consider both, the staff rostering and vehicle routing components while minimizing transportation costs and maximizing satisfaction of patients and nurses. In this paper we present the core optimization components of the PARPAP software. In the optimization kernel, a combination of linear programming, constraint programming, and (meta-)heuristics for the home health care problem is used, and we show how to apply these different heuristics efficiently to solve home health care problems. The overall concept is able to adapt to various changes in the constraint structure, thus providing the flexibility needed in a generic tool for real-world settings.