Temporal networks

Integrace plánování rozvrhování vyžaduje hledání nových přístupů problému rozvrhování. Rozvrhovací systém musí být schopen poskytnout užitečné informace plánovači, aby se zabránilo vytvářní neuskutečnitelných plánů. Pro rozvrhování založené na splňování omezujících podmínek je možné de novat vlastní fi ltrační pravidla a tak zefektivnit řešící algoritmus. Pokud filtrační pravidla využívají informace sdělené plánovačem a rozvrhovacím systémem (např. precedenční a nebo temporální podmínky), výstup těchto pravidel je mozné poskytnout plánovači, který je může s výhodou využít. V této práci je navržena filtrační metoda, která využívá temporální vztahy mezi aktivitami alokovanými na jeden nebo více disjunktivních zdrojů. Práce také popisuje sadu propagačnch pravidel založených na kombinaci ruzných fi ltračních technik.Integration of planning and scheduling requires new approaches to the scheduling problem. The scheduler must be able to provide useful information for the planner in order to avoid generation of unfeasible plans. In constraint-based scheduling it is possible to de ne custom ltering rules that improve the solving procedure. If the ltering rules exploit the information shared by the planner and the scheduler (e.g. precedence or temporal constraints), the outcome of these rules can be used to provide useful hints for the planner. This work presents a ltering technique that exploits temporal relations between a set of activities allocated to one or more disjunctive resources. The work also presents a set of propagation rules for constraint-based scheduling based on various ltering techniqes.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches

Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.California Institute of Technology. Keck Institute for Space Studie

Linear-time filtering algorithms for the disjunctive constraint and a quadratic filtering algorithm for the cumulative not-first not-last

We present new filtering algorithms for Disjunctive and Cumulative constraints, each of which improves the complexity of the state-of-theart algorithms by a factor of log n. We show how to perform TimeTabling and Detectable Precedences in linear time on the Disjunctive constraint. Furthermore, we present a linear-time Overload Checking for the Disjunctive and Cumulative constraints. Finally, we show how the rule of Not-first/Not-last can be enforced in quadratic time for the Cumulative constraint. These algorithms rely on the union find data structure, from which we take advantage to introduce a new data structure that we call it time line. This data structure provides constant time operations that were previously implemented in logarithmic time by the Θ-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases. We also show that the time line can be used to solve specific scheduling problems

A general framework integrating techniques for scheduling under uncertainty

Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof

Search Strategies for Scheduling Problems

V předložené práci porovnávám prohledávací strategie pro řešení rozvrhovacích problémů z pohledu programování s omezujícími podmínkami. Těžiště práce je věnováno rozvrhovacím problémům obsahujícím alternativní úlohy. V práci jsou jednak rozebrány různé již publikované způsoby modelování těchto problémů, dále pak jsou popsány a experimentálně porovnány prohledávací strategie pracující s těmito modely. Porovnáván je zejména vliv strategií na rychlost práce řešiče v závislosti na typu a velikosti dat. Jako vedlejší efekt práce studuje možnosti řešení rozvrhovacích problémů obsahujících alternativní úlohy pomocí řešiče Choco, který byl pro implementaci experimentů použit.In the present work I compare the search strategies for solving scheduling problems from the view of constraint programming. The thesis is focused on scheduling problems containing alternative activities. An analysis of previously published various ways of modelling the problems is provided, next description and experimental comparison of search strategies targetting these models is provided. The influence of strategies on the speed of the solver is studied primarily. As a sideeffect the work studies the ways how Choco solver, which was utililized for implementation of the experiments, can be used to solve the scheduling problems with alternative activities.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult