Scheduling learning dependent jobs in customised assembly lines

The large variety of product models required by customised markets implies lot size reduction. This strongly affects manual-based production activities, since workers need to promptly adapt to the specifications of the next model to be produced. Completion times of manual-based activities tend to be highly variable among workers, and are difficult to estimate. This affects the scheduling of those activities since scheduling precision depends on reliable estimates of job completion times. This paper presents a method that combines learning curves and job scheduling heuristics aimed at minimising the total weighted earliness and tardiness. Workers performance data is collected and modelled using learning curves, enabling a better estimation of the completion time of jobs with different size and complexity. Estimated completion times are then inputted in new scheduling heuristics for unrelated parallel workers, equivalent to machines in this study, created by modifying heuristics available in the literature. Performance of the proposed heuristics is assessed analysing the difference between the optimal schedule objective function value and that obtained using the heuristics, as well as the workload imbalance among workers. Some contributions in this paper are: (i) use of learning curves to estimate completion times of jobs with different sizes and complexities from different teams of workers; and (ii) use of a more complex scheduling objective function, namely the total weighted earliness and tardiness, as opposed to most of the developments in the current scheduling literature. A shoe manufacturing application illustrates the developments in the paper.