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

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

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

Experimental evaluation of preprocessing algorithms for constraint satisfaction problems

This paper presents an experimental evaluation of two orthogonal schemes for preprocessing constraint satisfaction problems (CSPs). The first of these schemes involves a class of local consistency techniques that includes directional arc consistency, directional path consistency, and adaptive consistency. The other scheme concerns the prearrangement of variables in a linear order to facilitate an efficient search. In the first series of experiments, we evaluated the effect of each of the local consistency techniques on backtracking and its common enhancement, backjumping. Surprizingly, although adaptive consistency has the best worst-case complexity bounds, we have found that it exhibits the worst performance, unless the constraint graph was very sparse. Directional arc consistency (followed by either backjumping or backtracking) and backjumping (without any pre-processing) outperformed all other techniques; moreover, the former dominated the latter in computationally intensive situations. The second series of experiments suggests that maximum cardinality and minimum width arc the best pre-ordering (i.e., static ordering) strategies, while dynamic search rearrangement is superior to all the preorderings studied

How to search for millions of queens

Journal ArticleThe n-queens problem is a classical combinatorial problem in artificial intelligence (AI) area. Since its simplicity and regular structure, this problem has widely been chosen as a testbed to develop and benchmark new AI search problem-solving strategies in the AI community. Due to its inherent complexity, so far even very efficient AI search algorithms can only find a solution for n-queens problem with n up to about 100. In this manuscript we present a new probabilistic local search algorithm which is based on a gradient-based heuristic. This efficient algorithm is capable of finding a solution for over 1,000,000 queens in several CPU hours on a 25Mhz Motorola 68030 computer

Empirical evaluation of Soft Arc Consistency algorithms for solving Constraint Optimization Problems

A large number of problems in Artificial Intelligence and other areas of science can be viewed as special cases of constraint satisfaction or optimization problems. Various approaches have been widely studied, including search, propagation, and heuristics. There are still challenging real-world COPs that cannot be solved using current methods. We implemented and compared several consistency propagation algorithms, which include W-AC*2001, EDAC, VAC, and xAC. Consistency propagation is a classical method to reduce the search space in CSPs, and has been adapted to COPs. We compared several consistency propagation algorithms, based on the resemblance between the optimal value ordering and the approximate value ordering generated by them. The results showed that xAC generated value orderings of higher quality than W-AC*2001 and EDAC. We evaluated some novel hybrid methods for solving COPs. Hybrid methods combine consistency propagation and search in order to reach a good solution as soon as possible and prune the search space as much as possible. We showed that the hybrid method which combines the variant TP+OnOff and branch-and-bound search performed fewer constraint checks and searched fewer nodes than others in solving random and real-world COPs

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

Finding regions of local repair in hierarchical constraint satisfaction

Algorithms for solving constraint satisfaction problems (CSP) have been successfully applied to several fields including scheduling, design, and planning. Latest extensions of the standard CSP to constraint optimization problems (COP) additionally provided new opportunities for solving several problems of combinatorial optimization more efficiently. Basically, two classes of algorithms have been used for searching constraint satisfaction problems (CSP): local search methods and systematic tree search extended by the classical constraint-processing techniques like e.g. forward checking and backmarking. Both classes exhibit characteristic advantages and drawbacks. This report presents a novel approach for solving constraint optimization problems that combines the advantages of local search and tree search algorithms which have been extended by constraint-processing techniques. This method proved applicability in a commercial nurse scheduling system as well as on randomly generated problems