Automatic Timetable Generation Using Genetic Algorithm

The generation of timetables has always been so tedious right from time and apart from being tedious, the timetable created has always been filled with series of errors and mistakes. So many techniques have been put forward to solving this problem. In this paper, genetic Algorithm was used in other to find a solution to the timetable problem. The system administrator logs into the system and then the administrator input the courses with their codes and the unit. At that point, the admin will keep adding until the number of courses needed has been inputted. The admin can remove a course that has been inputted in the case of error. After inputting the courses, it moves to the next page where all the lecture halls or rooms that will be used will be inputted. After inputting these, the system then generates the timetable system. This technique (genetic algorithm) used helps in reducing to minimum errors and mistakes in encountered in developing an automatic timetable. Key Words: Automatic; timetable; generation; genetic; algorithm; DOI: 10.7176/JIEA/8-1-0

Automatic Timetable Generation Using Genetic Algorithm

The generation of timetables has always been so tedious right from time and apart from being tedious, the timetable created has always been filled with series of errors and mistakes. So many techniques have been put forward to solving this problem. In this paper, genetic Algorithm was used in other to find a solution to the timetable problem. The system administrator logs into the system and then the administrator input the courses with their codes and the unit. At that point, the admin will keep adding until the number of courses needed has been inputted. The admin can remove a course that has been inputted in the case of error. After inputting the courses, it moves to the next page where all the lecture halls or rooms that will be used will be inputted. After inputting these, the system then generates the timetable system. This technique (genetic algorithm) used helps in reducing to minimum errors and mistakes in encountered in developing an automatic timetable. Keywords: Automatic; timetable; generation; genetic; algorithm; DOI: 10.7176/CEIS/10-4-04 Publication date:May 31st 201

Wording optimization tasks on risk management by simulation in the CoC QUASAR

The article reveals the possibility of solving optimization tasks for security risk management in a specific social system. The application of modern technological solutions to support the assessments contributes to the assessment objectification and the scenarios based methods application. The modeling of security processes and their reproduction, including repeatedly in a simulation environment can be used to solve optimization tasks to ensure security. The optimization task for security risk management has been formulated as methodology for solving the problems in the laboratories of work package "Risk Management" of the Center for Competence QUASAR

Assigning proctors to exams with scatter search

In this paper we present an algorithm to assign proctors to exams. This NP-hard problem is related to the generalized assignment problem with multiple objectives. The problem consists of assigning teaching assistants to proctor final exams at a university. We formulate this problem as a multiobjective integer program (IP) with a preference function and a workload-fairness function. We then consider also a weighted objective that combines both functions. We develop a scatter search procedure and compare its outcome with solutions found by solving the IP model with CPLEX 6.5. Our test problems are real instances from a University in Spain.Multiobjective combinatorial optimization, metaheuristics, scatter search

Sonet Network Design Problems

This paper presents a new method and a constraint-based objective function to solve two problems related to the design of optical telecommunication networks, namely the Synchronous Optical Network Ring Assignment Problem (SRAP) and the Intra-ring Synchronous Optical Network Design Problem (IDP). These network topology problems can be represented as a graph partitioning with capacity constraints as shown in previous works. We present here a new objective function and a new local search algorithm to solve these problems. Experiments conducted in Comet allow us to compare our method to previous ones and show that we obtain better results

The Single Row Facility Layout Problem: State of the Art

The single row facility layout problem (SRFLP) is a NP-hard problem concerned with the arrangement of facilities of given lenghs on a line so as to minimize the weighted sum of the distances between all the pairs of facilities. The SRFLP and its special cases often arise while modeling a large variety of applications. It was actively researched until the mid-nineties. It has again been actively studied since 2005. Interestingly, research on many aspects of this problem is still in the initial stages, and hence the SRFLP is an interesting problem to work on. In this paper, we review the literature on the SRFLP and comment on its relationship with other location problems. We then provide an overview of different formulations of the problem that appear in the literature. We provide exact and heuristic approaches that have been used to solve SRFLPs, and finally point out research gaps and promising directions for future research on this problem.

A hybrid dual-population genetic algorithm for the single machine maximum lateness problem

We consider the problem of scheduling a number of jobs, each job having a release time, a processing time and a due date, on a single machine with the objective of minimizing the maximum lateness. We developed a hybrid dual-population genetic algorithm and compared its performance with alternative methods on a new diverse data set. Extensions from a single to a dual population by taking problem specific characteristics into account can be seen as a stimulator to add diversity in the search process, which has a positive influence on the important balance between intensification and diversification. Based on a comprehensive literature study on genetic algorithms in single machine scheduling, a fair comparison of genetic operators was made