Supporting Server for Constraint Processing Teaching

Cílem práce je nastudovat algoritmy řešící problémy s omezujícími podmínkami a vytvořit aplikaci, která bude demonstrovat jejich průběh a postup výpočtu. Výsledná aplikace umožňuje definovat vlastní zadání problému, který bude vyřešen vybraným algoritmem a uživateli bude zobrazen průběh výpočtu formou animace procházení stromové struktury prostoru potencionálních řešení. Práce je zaměřena na backtracking a jeho modifikace. Do aplikace byl implementován základní backtracking, backjumping, dopředná kontrola a hranová závislost. Je založena na webových technologiích HTML 5, CSS 3 a JavaScript. Aplikace bude nasazena na univerzitní server. Je určena studentům předmětu ARUO pro studium a lepší pochopení vybraných algoritmů a znázornění rozdílu mezi nimi.The goal of the thesis is to study algorithms solving problems in a field of contrained conditions and to develop an application to demonstrate their processing and progression. The application will allow user to enter his own specification of the problem, choose an algorith to solve it and to display the process of the algorithm. Result is displayed as an animation of browsing of a tree structure representing the space of potential solutions. Thesis is dedicated to the backtracking and its modifications. Application implements basic backtracking, backjumping, forward-chacking and arc-consistency algorithms.It is ased on web technologies as HTML 5, CSS 3 and JavaScript. Application will be deployed on university server. It is accessible to the students of ARUO for studying and better understanding of the selected algorithms and to demonstrate differences among them.460 - Katedra informatikydobř

Heuristics for Periodic Scheduling

V posledních několika desetiletích se masivně zvýšilo využívání elektronických komunikačních systémů, které ovlivňují všechny oblasti lidské činnosti. Díky nízkým nákladům a vysoké efektivitě mohou být tyto modely široce rozšířené. Masivní využívání takových systémů v různých doménách jako je průmysl, chytrá města (smart cities), atd., volá po vývoji rozvrhovacích metod, které jsou rychlé, přizpůsobivé a spolehlivé. V této práci formalizujeme problém vysoce kritického periodického rozvrhování. Dále navrhujeme aplikaci v Javě, která umožnuje jednoduché testování různých rozvrhovacích metod. Hlavní přínos této práce spočívá v několika heuristikách vhodných pro striktně periodické rozvrhování komunikace a porovnání jejich výkonnosti na vygenerovaných instancích.In the past decades, the usage of electronic communication systems that influence all areas of human activities massively increased. Low cost and high effectiveness allow it to be used widely. The massive usage of such systems in different domains such as industry, smart cities, etc. calls for developing scheduling methods that are fast, adjustable and reliable. In this thesis, we formalize the highly critical periodic scheduling problem and design a Java-based framework that allows easy testing of different scheduling methods. The main contribution of this thesis is several heuristics suitable for strictly periodic network communication and comparison of their performance on generated instances

Dynamic Scheduling

Jedním z problémů rozvrhování výroby v reálném životě je dynamičnost výrobních prostředí zahrnující nové výrobní požadavky a rozbíjející se zařízení během vykonávání rozvrhu. Prosté přerozvržení od nuly v reakci na neočekávané události, které nastávají v provozu, může vyžadovat nadměrný výpočetní čas. Obnovený rozvrh může být navíc neúnosně odchýlený od toho probíhajícího. Tato práce podává přehled o stávajících přístupech v oblasti dynamického rozvrhování a navrhuje postupy jak upravit rozvrh při vyrušení, jako je například selhání zdroje, příchod naléhavé objednávky nebo její zrušení. Důraz je kladen na rychlost navržených procedur i na minimální modifikaci původního rozvrhu. Rozvrhovací model vychází z projektu FlowOpt, který je založen na temporálních sítích s alternativami. Algoritmy jsou napsány v jazyce C#.One of the problems of real-life production scheduling is dynamics of manufacturing environments with new production demands and breaking machines during the schedule execution. Simple rescheduling from scratch in response to unexpected events occurring on the shop floor may require excessive computation time. Moreover, the recovered schedule may be prohibitively deviated from the ongoing schedule. This thesis reviews existing approaches in the field of dynamic scheduling and proposes techniques how to modify a schedule to accommodate disturbances such as resource failure, hot order arrival or order cancellation. The importance is put on the speed of suggested procedures as well as on a minimum modification from the original schedule. The scheduling model is motivated by the FlowOpt project, which is based on the Temporal Networks with Alternatives. The algorithms are written in the C# language.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances

Proceedings of the Workshop on Algorithmic Aspects of Advanced Programming Languages: WAAAPL'99: Paris, France, September 30, 1999

The first Workshop on Algorithmic Aspects of Advanced Programming Languages was held on September 30, 1999, in Paris, France, in conjunction with the PLI'99 conferences and workshops. The choice of programming languages has a huge effect on the algorithms and data structures that are to be implemented in that language. Traditionally, algorithms and data structures have been studied in the context of imperative languages. This workshop considers the algorithmic implications of choosing an advanced functional or logic programming language instead. A total of eight papers were selected for presentation at the workshop, together with an invited lecture by Robert Harper. We would like to thank Dider Remv, general chair of PLI'99, for his assistance in organizing this workshop

Hybrid algorithms for distributed constraint satisfaction.

A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is divided into several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problem together with the constraints relating its own variables (intra-agent constraints) and the constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting scheduling and sensor networks. Existing approaches to Distributed Constraint Satisfaction can be mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local search algorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completeness of systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate to solve DisCSPs. Three new hybrid approaches which combine both systematic and local search for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyb and; (iii) Multi-HDCS. These approaches use distributed local search to gather information about difficult variables and best values in the problem. Distributed systematic search is run with a variable and value ordering determined by the knowledge learnt through local search. Two implementations of each of the three approaches are presented: (i) using penalties as the distributed local search strategy and; (ii) using breakout as the distributed local search strategy. The three approaches are evaluated on several problem classes. The empirical evaluation shows these distributed hybrid approaches to significantly outperform both systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributed problem solving with distributed systematic search taking less time to run by using the information learnt by distributed local search. As a consequence, larger problems can now be solved in a more practical timeframe

Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators

[ES] Se desarrolla una técnica CSP para buscar soluciones robustas en el problema job-shop scheduling. La técnica esta desarrollada en tres pasos. El primer paso resuelve el problema sin tener en cuenta operadores. El segundo paso introduce las restricciones de los operadores y obtiene soluciones teniendo en cuenta el makespan y la robustez. En el tercer paso se mejora la robustez redistribuyendo los buffers. Para probar las robustez de las soluciones obtenidas se aplican incidencias virtuales en las soluciones.[EN] A CSP technique have been developed for finding robust solutions in job-shop scheduling problems with operators. The technique is developed in three steps. The first step solve the problem without operators minimizing the makespan. The second step introduce the operator constraints and give solutions take into account makespan and robustness. The third step improve the robustness redistributing the buffer. Some virtual incidences are created and to check the robustness of the solutions.Escamilla Fuster, J. (2012). Design and development of CSP techniques for finding robust solutions in job-shop scheduling problems with Operators. http://hdl.handle.net/10251/18029Archivo delegad