An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Mixed-model assembly lines often create model imbalance due to differences in task times for the different product models. Smoothing algorithms guided by meta-heuristics that can escape local optimums can be used to reduce model imbalance. In this research, we utilise the meta-heuristics tabu search (TS), the great deluge algorithm (GDA) and record-to-record travel (RTR) to reduce three objective functions: the absolute deviation from cycle time, the maximum deviation from cycle time, and the sum of the cycle time violations. We found that the GDA was significantly superior to the RTR and TS algorithms across all problem sizes and objective functions. For the 19 task problems, RTR performed significantly better than TS for all three objective functions. On the other hand, for the 61 and 111 task problems TS performed significantly better than RTR for all three objective functions.