Genetic Algorithm for lecturing schedule optimization

Abstract

Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.

Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

“…The performance of the HEWOA was compared to other competitive methods that solved course timetabling: GA using heuristic mutation; GA using invalid genes focused on random resetting mutation; guided GA [34], parallel GA and local search [35]; and greedy and genetic fusion algorithm [36]. The result in Table IV of their performances in terms of total execution time is based on ten (10) runs per method.…”

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

“…In addition, the peak consumption of the end user may not always coincide with the peak load of the power system, which determines the hours of use of the demand response system. Most often, the planning of the schedule of classes, which largely determines the power consumption of educational institutions, is carried out using a genetic algorithm [23][24][25]. However, in the context of the development of renewable energy sources, as well as the requirements for improving the energy efficiency of consumers, which are developing more actively than ever before, the disadvantage of the methods used is the need to consider energy consumption for planning the curriculum of educational institutions.…”

Course scheduling optimization using genetic algorithm and tabu search

“…Algoritme genetika melakukan pencarian yang meniru mekanisme dari genetika alam [7]. Dengan meniru teori evolusi, AG dapat digunakan untuk mencari solusi permasalahanpermasalahan dalam dunia nyata yang optimal dan efektif, salah satunya adalah untuk menghasilkan penjadwalan yang optimal [8], [9]. AG cukup baik jika digunakan dalam pembuatan jadwal mata kuliah untuk memecahkan masalah yang cukup besar, meskipun membutuhkan waktu yang lama jika di kerjakan secara manual [2].…”