A Hybrid Meta heuristic Algorithm for the Balanced Line Production under Uncertainty

¹ Faculty of Industrial Technology, Phranakhon Rajabhat University, Bangkok, 10220, Thailand
² Industrial Statistics and Operational Research Unit (ISO-RU), Faculty of Engineering, Thammasat University, Pathumthani, 12120, Thailand
³ Dalle Molle Institute for Artificial Intelligence (IDSIA), University of Applied Sciences and Arts of Southern Switzerland (SUPSI), Galleria 2, Manno 6928, Switzerland

Abstract

This study proposes a hybrid Golden Ball Algorithm for solving a balanced line production for a garment firm in Thailand. At present, production lines are those in which the timing of the job movement between stations is coordinated in such a way that all of the jobs are indexed simultaneously via some heuristic sequencing or dispatching rules. This research studies the balanced line production problem with some stochastic patterns, and develops a Golden Ball Algorithm or GBA and its variants to solve the problem. To assess the effectiveness of the proposed hybrid algorithm, a computational study is conducted for both deterministic and stochastic patterns of the problem. The comparisons are made for two different levels of processing times and due date. It can be concluded that the variant HGBA2 of the algorithm by adjusting answers of the successor function on both custom training and successor phases, is slightly more effective than the other hybrid approaches in terms of quality of solutions under uncertainty.