University timetabling is an issue that has received more attention in the field of operations research. Course scheduling is the process of arranging time slots and room for a class by paying attention to existing limitations. This problem is an NP-Hard problem, which means the computation time to find a solution increases exponentially with the size of the problem. Solutions to problems of this kind generally use a heuristic approach, which tries to find a sufficiently good (not necessarily optimal) solution in a reasonable time. We go through two stages in solving the timetabling problem. The first stage is to schedule all classes without breaking any predefined rules. The second stage optimizes the timetable generated in the first stage. This study attempts to solve the class timetabling problem issued in a competition called the 2019 International Timetabling Competition (ITC 2019). In the first stage, we use the Iterative Forward Search (IFS) algorithm to eliminate timetable candidates and to generate a schedule. In the second stage, we employ the Great Deluge algorithm with a hyper-heuristic approach to optimize the solution produced in the first stage. We have tested the method using 30 datasets by taking 1,000,000 iterations on each dataset. The result is an application that does schedule elimination and uses the IFS algorithm to produce a schedule that does not violate any of the hard constraints on 30 ITC 2019 datasets. The implementation of the Great Deluge algorithm optimizes existing schedules with an average penalty reduction of 42%.
Penjadwalan mata kuliah merupakan salah satu bidang dari riset operasi. Permasalahan ini menjadwalkan sebuah kelas dengan tidak melanggar satu batasan yang ada. Pada saat ini permasalahan penjadwalan mata kuliah menjadi semakin kompleks dengan banyaknya batasan yang ada. Salah satunya terdapat pada kompetisi International Timetabling Competition 2019 (ITC 2019) yang menggeluarkan dataset real world terbaru.
Penelitian ini berfokus terhadap optimasi kualitas penjadwalan mata kuliah untuk menurunkan nilai penalti pada solusi akhir dari penjadwalan mata kuliah. Algoritma yang digunakan adalah Late Acceptence Hill Climbing(LAHC) dengan pendekatan hiper-heuristik dengan menggunakan Low Level Heuristik(LLH) mutasi dan local search. Algoritma diterapkan terhadap 30 dataset ITC 2019 dengan 100.000 iterasi dalam 5 kali percobaan.
Hasilnya penerapan algoritma ini mampu mengoptimasi dengan rata-rata 52% dari solusi awal. Selain itu algoritma ini menghasilkann solusi yang konsisten selama 10 kali percobaan di setiap datasetnya.
The Traveling Salesman Problem (TSP) is very popular in combinatoric optimization. The TSP problem is finding the optimal route from several cities where the distance between cities is known, and a salesman must visit each city exactly once and return to the origin city. The goal is to find a route with a minimum total distance. This problem is known as a non-deterministic polynomial hard (NP-hard) problem, which means the computation time to find a solution increases exponentially with the size of the problem. NP-Hard problems can be solved by using heuristic methods where the solution obtained is good enough (does not guarantee the most optimal solution) in a reasonable time. One of the most recent variants of TSP problem is finding the cheapest flight routes to several cities, which is part of the Traveling Salesman Challenge 2.0 (TSC 2.0) 2018 competition . This paper reports our study of implementing an artificial bee colony (ABC) algorithm for the TSC 2.0 problem. ABC algorithm is chosen based on its superiority over other algorithms in several optimization problems. The algorithm is implemented in a hyper-heuristic form. Several combinations of swap operators are used to find the best combination result. The experimental result shows that the ABC algorithm can solve the TSC 2.0 problem with a fairly good performance by producing a savings cost of 54.6% from the initial solution and 26% compared to the Genetic Algorithm.
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