Admi Syarif, AS and Pamungkas, Ade and Mahendra, Renaldi Kumar and Gen, Mitsuo (2021) Performance Evaluation of Various Heuristic Algorithms To Solve Job Shop Scheduling Problem (JSSP). IJIES, 14 (2). pp. 334-343. ISSN 2185-3118 (eISSN), 2185-310X (pISSN)


Download (6MB) | Preview
Official URL:


Abstract: Scheduling is a famous optimization problem that seeks the best strategy of allocating resources over time to perform jobs/tasks satisfying specific criteria. It exists everywhere in everyday life, particularly in manufacturing or industrial applications. An essential class of scheduling problems is a job shop scheduling problem (JSSP), an NP-hard optimization problem. Several researchers have reported the use of heuristic methods to solve JSSP. This paper aims to investigate the performance of some heuristic algorithms to solve JSSP. Firstly, we developed a Genetic Algorithm (GA) approach and evaluated the performance of some heuristic algorithms, including Particle Swarm Optimization (PSO), Upper-level algorithm (UPLA), Differential-based Harmony Search (DHS), Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Parallel Bat Optimization (PBA), and Tabu Search (TS). Experimental results of the 28 benchmark test problems validate that algorithms can present the problem's optimal solution. Almost all algorithms, except ACO, can provide an error of less than 1 percent. PBA delivers the most impressive performance that solves 26 cases optimally, with the average error equal to 0.05%. Among

Item Type: Article
Subjects: Q Science > Q Science (General)
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User: DR Admi Syarif
Date Deposited: 28 Dec 2021 04:12
Last Modified: 28 Dec 2021 04:12

Actions (login required)

View Item View Item