Two Phase Heuristic Algorithm for the University Course Timetabling Problem: The Case of University of Dar Es Salaam

University course timetabling is the problem of scheduling resources such as lecturers, courses, and rooms to a number of timeslots over a planning horizon, normally a week, while satisfying a number of problem-specific constraints. Since timetabling problems differ from one institution to another, this paper investigated the case of the University of Dar Es salaam, based on the combination of Simulated Annealing (SA), and steepest descent in a two-phase approach. Solutions have been generated which greatly outperform the manually generated ones. Furthermore, the method compares well with previous work on Tabu Search but with faster execution time and higher quality on rooms allocation. It is concluded that the approach gives good results given a careful selection of parameters.Keywords: Timetabling Problem, Simulated Annealing, Combinatorial Optimization, Steepest DescentTanz. J. Sci. Vol. 37 201

Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios

SIMULATED ANNEALING APPROACH FOR UNIVERSITY TIMETABLE PROBLEM

Abstract :  University Scheduling is a way of allocating students, lecturers, and rooms, which is used for lectures in the available time slots. The common problems are that a lecturer is scheduled in the same time slot, or several courses are scheduled in the same room and the same time slot. For this reason, scheduling needs to be made in such a way that it can optimize the use of resources. We use simulated annealing as an approach to solve that problem. The results showed that the higher the initial temperature value used and the greater the iteration value would reduce the violation constraints in scheduling problems

An Empirical Performance Comparison of Meta-heuristic Algorithms for School Bus Routing Problem

School Bus Routing Problem is an NP-hard Combinatorial Optimization problem. Thus, mega-heuristic algorithms are widely used to solve instances of the School Bus Routing Problem with large data. In this work we present a model of the School Bus Routing Problem and empirical performances comparison between three meta-heuristic algorithms named Simulated Annealing (SA), Tabu Search (TS) and Ant-Colony Optimization (ACO) on the problem. We have analyzed their performances in terms of solution quality. The results show that all three algorithms have the ability to solve the School Bus Routing Problem. In addition, computational results show that TS performed best when execution time is not restricted while ACO had relative good performance when time is restricted but poor when the time is unrestricted.Keywords:  School Bus Routing Problem; Combinatorial Optimization; Meta-heuristic Algorithm

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

The Application of Late Acceptance Heuristic Method for the Tanzanian High School Timetabling Problem

High School timetabling is the problem of scheduling lessons of different subjects and teachers to timeslots within a week, while satisfying a set of constraints which are classified into hard and soft constraints. This problem is different from university course timetabling problem because of the differences in structures including classroom allocations and grouping of subject combinations. Given the scarce education resources in developing countries, high school timetabling problem plays a very important role in optimizing the use of meager resources and therefore contribute to improvement of quality of education. The problem has attracted attention of many researchers around the world; however, very little has been done in Tanzania. This paper presents a solution algorithm known as Late Acceptance heuristic for the problem and compares results with previous work on Simulated Annealing and Great Deluge Algorithm for three schools in Dar es Salaam Tanzania. It is concluded that Late Acceptance heuristic gives results which are similar to the previous two algorithms but performs better in terms of time saving. Keywords: Late Acceptance; High School Timetabling; Combinatorial Optimization; Heuristics; NP-Har

Aspects of computerised timetabling

This research considers the problem of constructing high school timetables using a computer. In the majority of high schools, termly or yearly timetables are still being produced manually. Constructing a timetable is a hard and time consuming task which is carried out repeatedly thus a computer program for assisting with this problem would be of great value. This study is in three parts. First. an overall analysis of the problem is undertaken to provide background knowledge and to identify basic principles in the construction of a school timetable. The characteristics of timetabling problems are identified and the necessary data for the construction of a timetable is identified. The first part ends with the production of a heuristic model for generating an initial solution that satisfies all the hard constraints embodied in the curriculum requirements. The second stage of the research is devoted to designing a heuristic model for solving a timetable problem with hard and medium constraints. These include constraints like the various numbers of common periods, double periods and reducing the repeated allocation of a subject within any day. The approaches taken are based on two recently developed techniques, namely tabu search and simulated annealing. Both of these are used and comparisons of their efficiency are provided. The comparison is based on the percentage fulfilment of the hard and medium requirements. The third part is devoted to one of the most difficult areas in timetable construction, that is the softer requirements which are specific to particular schools and whose satisfaction is not seen as essential. This section describes the development of an expert system based on heuristic production rules to satisfy a range of soft requirements. The soft requirements are studied and recorded as rules and a heuristic solution is produced for each of the general requirements. Different levels of rule are developed, from which the best possible solution to a particular timetable problem is expertly produced. Finally, possible extensions of the proposed method and its application to other types of the timetabling problem are discussed