An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

Purpose - Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.Design/methodology/approach - This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.Findings - The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.Originality/value - This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

Automated summary

This paper proposes an artificial bee colony based-hyper heuristic algorithm to solve multi-objective simple assembly line balancing problems. By optimizing the efficiency and idle percentage of the assembly line while minimizing the number of workstations, the algorithm demonstrates superior performance in benchmark tests.