Nicolas Barnier

Solving the Kirkman's schoolgirl problem in a few seconds

In this paper, we propose an effective search procedure that interleaves two steps: subproblem generation and subproblem solution. We mainly focus on the first part. It consists of a variable domain value ranking based on reduced costs. Exploiting the ranking, we generate, in a Limited Discrepancy Search tree, the most promising subproblems first. An interesting result is that reduced costs provide a very precise ranking that allows to almost always find the optimal solution in the first generated subproblem, even if its dimension is significantly smaller than that of the original problem. Concerning the proof of optimality, we exploit a way to increase the lower bound for subproblems at higher discrepancies. We show experimental results on the TSP and its time constrained variant to show the effectiveness of the proposed approach, but the technique could be generalized for other problems.The Social Golfer Problem has been extensively used by the constraint community in recent years as an example of a highly symmetric problem. It is an excellent problem for benchmarking symmetry breaking mechanisms such as SBDS or SBDD and for demonstrating the importance of the choice of the right model for one problem. We address in this paper a specific instance of the Golfer Problem well known as Kirkmans Schoolgirl Problem and list a collection of techniques and tricks to find efficiently all its unique solutions. In particular, we propose SBDD+, a generic improvement over SBDD which allows a deep pruning when a symmetry is detected during the search. Our implementation of the presented techniques improves previously published results by an order of magnitude for CPU time as well as for number of backtracks. It computes the seven unique solutions of Kirkmans problem in a few seconds.

Graph Coloring for Air Traffic Flow Management

The aim of Air Traffic Flow Management (ATFM) is to enhance the capacity of the airspace while satisfying Air Traffic Control constraints and airlines requests to optimize their operating costs. This paper presents a design of a new route network that tries to optimize these criteria. The basic idea is to consider direct routes only and vertically separate intersecting ones by allocating distinct flight levels, thus leading to a graph coloring problem. This problem is solved using constraint programming after having found large cliques with a greedy algorithm. These cliques are used to post global constraints and guide the search strategy. With an implementation using FaCiLe, our Functional Constraint Library, optimality is achieved for all instances except the largest one, while the corresponding number of flight levels could fit in the current airspace structure. This graph coloring technique has also been tested on various benchmarks, featuring good results on real-life instances, which systematically appear to contain large cliques.

Solving the Kirkman's Schoolgirl Problem in a Few Seconds

The Social Golfer Problem has been extensively used by the constraint community in recent years as an example of a highly symmetric problem. It is an excellent problem for benchmarking symmetry breaking mechanisms such as SBDS or SBDD and for demonstrating the importance of the choice of the right model for one problem. We address in this paper a specific instance of the Golfer Problem well known as Kirkman’s Schoolgirl Problem and list a collection of techniques and tricks to find efficiently all its unique solutions. In particular, we propose SBDD+, a generic improvement over SBDD which allows a deep pruning when a symmetry is detected during the search. Our implementation of the presented techniques improves previously published results by an order of magnitude for CPU time as well as for number of backtracks. It computes the seven unique solutions of Kirkman’s problem in a few seconds.

Combine and Conquer: Genetic Algorithm and CP for Optimization

We introduce a new optimization method based on a Genetic Algorithm (GA) combined with Constraint Satisfaction Problem (CSP) techniques. The approach is designed for combinatorial problems whose search spaces are too large and/or objective functions too complex for usual CSP techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the handling of sub-domains of the CSP variables by the genetic algorithm. The population of the genetic algorithm is made up of strings of sub-domains whose adaptation are computed through the resolution of the corresponding ¤b-CSPs’ which are somehow much easier than the original problem. We provide basic and dedicated recombination and mutation operators with various degrees of robustness. The first set of experimentations adresses a naIve formulation of a Vehicle Routing Problem (VRP). The results are quite encouraging as we outperform CSP techniques and genetic algorithm alone on these formulations

Constraint programming for air traffic management : a survey

Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.

A ground holding model for aircraft deconfliction

In the SESAR traffic growth predictions, traffic complexity will become an issue that the current Air Traffic Management organization is not able to handle. The 4D trajectory concept offers new perspectives for deconflicting the traffic by ground-holding aircraft before they take-off. This paper studies the possible complexity reduction achievable by optimizing the aircraft take-off times. Therefore a simple model is introduced to detect pairwise 3D possible conflicts and define conflicting take-off time differences. Two resolution algorithms are tested on a real traffic data sample collected in the French airspace. The first one is based on a Constraint Programming model of the problem and ensures the optimality of the maximum delay required to solve every conflict. The second one uses an evolutionary computation algorithm to minimize the mean delay among the aircraft population. A sliding window model is introduced to reduce the size of the problem and to regularly update the current situation. Experimental results performed in the French airspace with fast time simulation show that with perfect 4D trajectory, every conflict over flight level 290 can be solved by delaying less than a quarter of the traffic within a range of delays varying from 1 to 90 minutes and a mean delay of 4 minutes. The Constraint Programming approach gives better results than the evolutionary computation approach. Adding uncertainty around 4D trajectories dramatically degrades the results.

Optimized vertical separation in Europe

As acknowledged by the SESAR program, current ATC systems must be drastically improved to accommodate the predicted traffic growth in Europe. This study aims at assessing the performance of 4D-trajectory planning and strategic deconfliction, two of the key concepts identified to meet SESAR capacity objectives. Among the possible degrees of freedom, we focus here on the flight level (FL) optimization to avoid en-route conflicts between intersecting flights. The resulting problem can be reduced to Graph Coloring with a specific cost function minimizing the discrepancies to requested FLs. This FL allocation leads to very large combinatorial optimization problems when applied at the continental scale, especially when considering temporal uncertainties. The instances were solved with a Tabu Search algorithm in a few seconds to a few minutes, depending on size and conflict density. Our results shows that the global conflict resolution workload is alleviated by at least 20%, while bounding the individual FL discrepancies to three levels for a small proportion of the traffic.

Integration of UAS in Terminal Control Area

In this article, we test a horizontal detect and avoid algorithm for UASs flying in Terminal Control Areas. We have used recorded commercial traffic trajectories and randomly built thousands of conflict scenarios with UASs to check the ability of such an algorithm to ensure the separation with commercial aviation. We consider two different types of UASs, flying at 80kn or 160kn, with six different missions: flying straight or turning and leveled, climbing or descending. We only focus on horizontal maneuvers at constant speed in order to not interfere with the TCASs of aircraft, nor rely on most UASs poor ability to change speed. The article investigates the influence of the various parameters on the separation achieved and the amount of maneuvers required, especially the strategy used to select the best maneuver among the allowed headings. The analysis of our results shows that, amid two basic and “extreme” strategies that favor either minimal heading changes or the robustness of the maneuvers, the combination of both, switching from the first one to the second whenever the distance between the UAS and aircraft falls under a given threshold, gives the best results with very few remaining airproxes, while keeping low the amount and amplitude of maneuvers.

Review: constraint programming for air traffic management: A survey1

Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.

Constraint programming for air traffic management: a survey - In memory of Pascal Brisset.

Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.