Modelling and prediction of worker task performance using a knowledge-based system application

It is a difficult task for an assembly line manager to select an appropriate worker from the available workers' list to assign to an assembly line workstation. Since each available worker has a unique set of skills and abilities, this research considers the worker differences in their work performance. The worker's work performance is considered based on their working speed or productivity. Task execution time (TET) is the measure used to distinguish the worker's work performance. The TET prediction is made by the application of a knowledge-based system framework. Workers' historical work-time data is used to model the knowledge objects. Workers are classified as skilled and semi-skilled respective methodologies are given for both categories of workers. Statistical-based learning algorithms are proposed for skilled workers based on the worker's age, gender, and work skill. Similarly, worker's learning patterns are used for semi-skilled workers. The predicted TET is used in solving the assembly line worker assignment problem. The second part of this work is to prioritise the aged worker during the worker selection without increment of worker count. The illustrative example helps understand the scope of the proposed methodology in an assembly line worker assignment problem.