Multi-objective optimization of cycle time and energy consumption in parallel robotic assembly lines using a discrete firefly algorithm

• Published in: Engineering computations Vol. 39; no. 6; pp. 2424 - 2448
• Main Authors: Soysal-Kurt, Halenur, İşleyen, Selçuk Kürşat
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 07.06.2022; Emerald Group Publishing Limited
• Subjects: Algorithms; Alliances; Assembly lines; Comparative studies; Costs; Cycle time; Energy consumption; Energy efficiency; Heuristic methods; Integer programming; Linear programming; Literature reviews; Manufacturers; Manufacturing; Mathematical models; Mathematical programming; Multiple objective analysis; Mutation; Optimization; Pareto optimization; Productivity; Robotics; Robots; Simulated annealing; Taguchi methods; Energy consumption; Multi-objective optimization; Parallel; Discrete firefly algorithm; Robotic assembly line balancing
• ISSN: 0264-4401; 1758-7077
• DOI: 10.1108/EC-12-2020-0747

PurposeAssembly lines are one of the places where energy consumption is intensive in manufacturing enterprises. The use of robots in assembly lines not only increases productivity but also increases energy consumption and carbon emissions. The purpose of this paper is to minimize the cycle time and total energy consumption simultaneously in parallel robotic assembly lines (PRAL).Design/methodology/approachDue to the NP-hardness of the problem, A Pareto hybrid discrete firefly algorithm based on probability attraction (PHDFA-PA) is developed. The algorithm parameters are optimized using the Taguchi method. To evaluate the results of the algorithm, a multi-objective programming model and a restarted simulated annealing (RSA) algorithm are used.FindingsAccording to the comparative study, the PHDFA-PA has a competitive performance with the RSA. Thus, it is possible to achieve a sustainable PRAL through the proposed method by addressing the cycle time and total energy consumption simultaneously.Originality/valueTo the best knowledge of the authors, this is the first study addressing energy consumption in PRAL. The proposed method for PRAL is efficient in solving the multi-objective balancing problem.