Authors: Miguel Rojas-Santiago; Shanthi Muthuswamy; Maria Hulett
Addresses: Department of Industrial Engineering, Universidad del Norte, Barranquilla, Colombia ' Department of Technology, College of Engineering and Engineering Technology, Northern Illinois University, De Kalb, IL, 60115, USA ' Department of Management Science, School of Business, University of Miami, Coral Gables, FL, 33146, USA
Abstract: A flow shop scheduling problem with setup times has been studied in a food processing setting. This company packs cooking sauces and spices. The objective is to minimise the makespan (Cmax) taking the setup times into consideration. Given that this problem in NP-hard, an ant colony optimisation (ACO) algorithm has been developed to find the initial solution which is further improved using 2-opt and 3-opt local heuristic. Using Taillard's benchmark problems' processing times and randomly generated setup times, 60 problem instances were computed for 50 to 200 jobs using 10 and 20 machine scenarios. These Cmax values were compared against the results obtained through a particle swarm optimisation (PSO) metaheuristic. The results clearly show that the ACO algorithm schedules the machines consistently well to minimise the Cmax value in comparison to the PSO algorithm.
Keywords: ant colony optimisation; ACO; scheduling; makespan; flow shop; setup time; particle swarm optimisation; PSO.
International Journal of Industrial and Systems Engineering, 2020 Vol.36 No.1, pp.98 - 109
Received: 25 Jul 2018
Accepted: 24 Jan 2019
Published online: 21 Aug 2020 *