An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem

In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. The resulting program was ranked first among 64 participants. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. We perform a comprehensive study of these components showing that each of them contributes to the algorithm global performances. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints. On these instances, it finds the best-known solutions very quickly

An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems

[libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php). The resulting program was ranked first among 64 participants. In this article, we generalize it and show that it is not only effective for the specific problem it was originally designed for, but is also very competitive and even returns state-of-the-art solutions on a large variety of Cutting and Packing problems from the literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple Knapsack, and Strip Packing Problems, with two- or three-staged exact or non-exact guillotine cuts, the orientation of the first cut being imposed or not, and with or without item rotation. The combination of efficiency, ability to provide good solutions fast, simplicity and versatility makes it particularly suited for industrial applications, which require quickly developing algorithms implementing several business-specific constraints. The algorithm is implemented in a new software package called PackingSolver

Conflict Optimization for Binary CSP Applied to Minimum Partition into Plane Subgraphs and Graph Coloring

CG:SHOP is an annual geometric optimization challenge and the 2022 edition proposed the problem of coloring a certain geometric graph defined by line segments. Surprisingly, the top three teams used the same technique, called conflict optimization. This technique has been introduced in the 2021 edition of the challenge, to solve a coordinated motion planning problem. In this paper, we present the technique in the more general framework of binary constraint satisfaction problems (binary CSP). Then, the top three teams describe their different implementations of the same underlying strategy. We evaluate the performance of those implementations to vertex color not only geometric graphs, but also other types of graphs.Comment: To appear at ACM Journal of Experimental Algorithmic

Preliminary Results for the Multi-Robot, Multi-Partner, Multi-Mission, Planetary Exploration Analogue Campaign on Mount Etna

This paper was initially intended to report on the outcome of the twice postponed demonstration mission of the ARCHES project. Due to the global COVID pandemic, it has been postponed from 2020, then 2021, to 2022. Nevertheless, the development of our concepts and integration has progressed rapidly, and some of the preliminary results are worthwhile to share with the community to drive the dialog on robotics planetary exploration strategies. This paper includes an overview of the planned 4-week campaign, as well as the vision and relevance of the missiontowards the planned official space missions. Furthermore, the cooperative aspect of the robotic teams, the scientific motivation, the sub task achievements are summarised

Contributions théoriques et pratiques à l'ordonnancement d'observations d'objets célestes

Large telescopes are few and observing time is a precious resource subject to a strong pressure from a competitive community. Besides, a growing number of astronomical projects require surveying a large number of targets during discontinuous runs spread over few months or years. This thesis is the result of a collaboration between operations researchers from G-SCOP laboratory and astrophysicists from IPAG research unit. Its goal was to optimize the schedule of star observations on telescopes, in particular, on the Very Large Telescope. The first chapter contains a theoretical study of the complexity of several variants of processing time dependent profit maximization scheduling problems. Those problems appear inter alia as sub-problem of the real life star observation scheduling problem studied in the second chapter, for which we developed a fast and efficient Large Neighborhood Search algorithm. The third chapter is not related to the star observation scheduling problem. It describes the Tree Search based algorithm we developed for the glass cutting problem raised by the company Saint-Gobain for the ROADEF/EURO challenge 2018.Il existe peu de grands téléscopes et le temps d'observation est une ressource précieuse sujette à une forte pression de la part d'une communauté concurrentielle. En outre, un nombre croissant de projets astronomiques nécessitent l'observation d'un grand nombre d'objets célestes pendant des runs discontinus répartis sur plusieurs mois ou années. Cette thèse est le fruit d'une collaboration entre des chercheurs en recherche opérationnelle du laboration G-SCOP et des astrophysiciens de l'unité de recherche IPAG. Son but était d'optimiser l'ordonnancement d'observations d'étoiles sur les téléscopes, en particulier, sur le Très Grand Télescope. Le premier chapitre contient une étude théorique de la complexité de plusieurs variantes de problèmes d'ordonnancement de maximization de profits dépendants du temps d'exécution. Ces problèmes apparaissent entre autres comme sous-problèmes du problème réel d'ordonnancement d'observations d'étoiles étudié dans le deuxième chapitre, pour lequel nous avons développé une recherche locale à voisinage large rapide et efficace. Le troisième chapitre ne concerne pas le problème d'ordonnancement d'observations d'étoiles. Il décrit l'algorithme de recherche arborescente que nous avons développé pour le problème de découpe de verre posé par l'entreprise Saint-Gobain pour le challenge ROADEF/EURO 2018

Theoretical and practical contributions to star observation scheduling problems

Il existe peu de grands téléscopes et le temps d'observation est une ressource précieuse sujette à une forte pression de la part d'une communauté concurrentielle. En outre, un nombre croissant de projets astronomiques nécessitent l'observation d'un grand nombre d'objets célestes pendant des runs discontinus répartis sur plusieurs mois ou années. Cette thèse est le fruit d'une collaboration entre des chercheurs en recherche opérationnelle du laboration G-SCOP et des astrophysiciens de l'unité de recherche IPAG. Son but était d'optimiser l'ordonnancement d'observations d'étoiles sur les téléscopes, en particulier, sur le Très Grand Télescope. Le premier chapitre contient une étude théorique de la complexité de plusieurs variantes de problèmes d'ordonnancement de maximization de profits dépendants du temps d'exécution. Ces problèmes apparaissent entre autres comme sous-problèmes du problème réel d'ordonnancement d'observations d'étoiles étudié dans le deuxième chapitre, pour lequel nous avons développé une recherche locale à voisinage large rapide et efficace. Le troisième chapitre ne concerne pas le problème d'ordonnancement d'observations d'étoiles. Il décrit l'algorithme de recherche arborescente que nous avons développé pour le problème de découpe de verre posé par l'entreprise Saint-Gobain pour le challenge ROADEF/EURO 2018.Large telescopes are few and observing time is a precious resource subject to a strong pressure from a competitive community. Besides, a growing number of astronomical projects require surveying a large number of targets during discontinuous runs spread over few months or years. This thesis is the result of a collaboration between operations researchers from G-SCOP laboratory and astrophysicists from IPAG research unit. Its goal was to optimize the schedule of star observations on telescopes, in particular, on the Very Large Telescope. The first chapter contains a theoretical study of the complexity of several variants of processing time dependent profit maximization scheduling problems. Those problems appear inter alia as sub-problem of the real life star observation scheduling problem studied in the second chapter, for which we developed a fast and efficient Large Neighborhood Search algorithm. The third chapter is not related to the star observation scheduling problem. It describes the Tree Search based algorithm we developed for the glass cutting problem raised by the company Saint-Gobain for the ROADEF/EURO challenge 2018

An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems

[libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php). The resulting program was ranked first among 64 participants. In this article, we generalize it and show that it is not only effective for the specific problem it was originally designed for, but is also very competitive and even returns state-of-the-art solutions on a large variety of Cutting and Packing problems from the literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple Knapsack, and Strip Packing Problems, with two- or three-staged exact or non-exact guillotine cuts, the orientation of the first cut being imposed or not, and with or without item rotation. The combination of efficiency, ability to provide good solutions fast, simplicity and versatility makes it particularly suited for industrial applications, which require quickly developing algorithms implementing several business-specific constraints. The algorithm is implemented in a new software package called PackingSolver

An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems

[libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php). The resulting program was ranked first among 64 participants. In this article, we generalize it and show that it is not only effective for the specific problem it was originally designed for, but is also very competitive and even returns state-of-the-art solutions on a large variety of Cutting and Packing problems from the literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple Knapsack, and Strip Packing Problems, with two- or three-staged exact or non-exact guillotine cuts, the orientation of the first cut being imposed or not, and with or without item rotation. The combination of efficiency, ability to provide good solutions fast, simplicity and versatility makes it particularly suited for industrial applications, which require quickly developing algorithms implementing several business-specific constraints. The algorithm is implemented in a new software package called PackingSolver

An anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem

In this article, we present the anytime tree search algorithm we designed for the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French company Saint-Gobain. The resulting program was ranked first among 64 participants. Its key components are: a new search algorithm called Memory Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a pseudo-dominance rule. We perform a comprehensive study of these components showing that each of them contributes to the algorithm global performances. In addition, we designed a second tree search algorithm fully based on the pseudo-dominance rule and dedicated to some of the challenge instances with strong precedence constraints. On these instances, it finds the best-known solutions very quickly

An anytime tree search algorithm for two-dimensional two- and three-staged guillotine packing problems

[libralesso_anytime_2020] proposed an anytime tree search algorithm for the 2018 ROADEF/EURO challenge glass cutting problem (https://www.roadef.org/challenge/2018/en/index.php). The resulting program was ranked first among 64 participants. In this article, we generalize it and show that it is not only effective for the specific problem it was originally designed for, but is also very competitive and even returns state-of-the-art solutions on a large variety of Cutting and Packing problems from the literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple Knapsack, and Strip Packing Problems, with two- or three-staged exact or non-exact guillotine cuts, the orientation of the first cut being imposed or not, and with or without item rotation. The combination of efficiency, ability to provide good solutions fast, simplicity and versatility makes it particularly suited for industrial applications, which require quickly developing algorithms implementing several business-specific constraints. The algorithm is implemented in a new software package called PackingSolver