Timetabling in constraint logic programming

In this paper we describe the timetabling problem and its solvability in a Constraint Logic Programming Language. A solution to the problem has been developed and implemented in ECLiPSe, since it deals with finite domains, it has well-defined interfaces between basic building blocks and supports good debugging facilities. The implemented timetable was based on the existing, currently used, timetables at the School of Informatics at out university. It integrates constraints concerning room and period availability

Timetable Management Using Genetic Algorithms

Scheduling course timetables for a large array of courses is a very complex problem which often has to be solved manually by the center staff even though results are not always fully optimal. Timetabling being a highly constrained combinatorial problem, this work attempts to put into play the effectiveness of evolutionary techniques based on Darwin's theories to solve the timetabling problem if not fully optimal but near optimal. Genetic Algorithm is a popular meta-heuristic that has been successfully applied to many hard combinatorial optimization problems which includes timetabling and scheduling problems. In this work, the course sets, halls and time allocations are represented by a multidimensional array on which a local search is performed and a combination of the direct representation of the timetable with heuristic crossover is made to ensure that fundamental constraints are not violated. Finally, the genetic algorithm was applied in the development of a viable timetabling system which was tested to demonstrate the variety of possible timetables that can be generated based on user specified constraint and requirements. Keywords: Time table management, genetic algorithm

Timetable Management Using Genetic Algorithms

Scheduling course timetables for a large array of courses is a very complex problem which often has to be solved manually by the center staff even though results are not always fully optimal. Timetabling being a highly constrained combinatorial problem, this work attempts to put into play the effectiveness of evolutionary techniques based on Darwin’s theories to solve the timetabling problem if not fully optimal but near optimal. Genetic Algorithm is a popular meta-heuristic that has been successfully applied to many hard combinatorial optimization problems which includes timetabling and scheduling problems. In this work, the course sets, halls and time allocations are represented by a multidimensional array on which a local search is performed and a combination of the direct representation of the timetable with heuristic crossover is made to ensure that fundamental constraints are not violated. Finally, the genetic algorithm was applied in the development of a viable timetabling system which was tested to demonstrate the variety of possible timetables that can be generated based on user specified constraint and requirements. Keywords: Time table management, genetic algorithm

Genetic Algorithm For University Course Timetabling Problem

Creating timetables for institutes which deal with transport, sport, workforce, courses, examination schedules, and healthcare scheduling is a complex problem. It is difficult and time consuming to solve due to many constraints. Depending on whether the constraints are essential or desirable they are categorized as ‘hard’ and ‘soft’, respectively. Two types of timetables, namely, course and examination are designed for academic institutes. A feasible course timetable could be described as a plan for the movement of students and staff from one classroom to another, without conflicts. Being an NP-complete problem, many attempts have been made using varying computational methods to obtain optimal solutions to the timetabling problem. Genetic algorithms, based on Darwin\u27s theory of evolution is one such method. The aim of this study is to optimize a general university course scheduling process based on genetic algorithms using some defined constraints

Solving the course scheduling problem by constraint programming and simulated annealing

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 87-62)Text in English; Abstract: Turkish and Englishix, 80 leavesIn this study it has been tackled the NP-complete problem of academic class scheduling (or timetabling). The aim of this thesis is finding a feasible solution for Computer Engineering Department of İzmir Institute of Technology. Hence, a solution method for course timetabling is presented in this thesis, consisting of two phases: a constraint programming phase to provide an initial solution and a simulated annealing phase with different neighbourhood searching algorithms. When the experimental data are obtained it is noticed that according to problem structure, whether the problem is tightened or loosen constrained, the performance of a hybrid approach can change. These different behaviours of the approach are demonstrated by two different timetabling problem instances. In addition to all these, the neighbourhood searching algorithms used in the simulated annealing technique are tested in different combinations and their performances are presented

Service-oriented logic programming

We develop formal foundations for notions and mechanisms needed to support service-oriented computing. Our work builds on recent theoretical advancements in the algebraic structures that capture the way services are orchestrated and in the processes that formalize the discovery and binding of services to given client applications by means of logical representations of required and provided services. We show how the denotational and the operational semantics specific to conventional logic programming can be generalized using the theory of institutions to address both static and dynamic aspects of service-oriented computing. Our results rely upon a strong analogy between the discovery of a service that can be bound to an application and the search for a clause that can be used for computing an answer to a query; they explore the manner in which requests for external services can be described as service queries, and explain how the computation of their answers can be performed through service-oriented derivatives of unification and resolution, which characterize the binding of services and the reconfiguration of applications

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyper-heuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help to facilitate more intelligent hyper-heuristic search methods

Adapting Artificial Immune Algorithms For University Timetabling

Penjadualan kelas dan peperiksaan di universiti adalah masalah pengoptimuman berkekangan tinggi. University class and examination timetabling are highly constrained optimization problems