Heuristic for task-worker assignment with varying learning slopes

Abstract

When a worker performs a task repetitively, s/he requires less time to produce the succeeding units of a task due to his/her learning ability. In mass production, a constant production rate assumption is always assumed in developing a task-worker assignment since the learning period is only a small part compared to the overall production period. However, in the fashion industry, new product styles are launched more frequently and order sizes are smaller. Due to this small lot size, task-worker assignments based on a constant production rate assumption may not be applicable. As a result, learning should be considered in the assignments in the fashion industry. This paper proposes a method of task-worker assignment considering worker skill levels and learning abilities. The processing time of each worker varies in the production period depending on worker learning ability. We focused on task-worker assignments where tasks are ordered in a series and the number of tasks is greater than the number of workers. Workers could perform multiple tasks with the precedence restriction. An integer linear programming model was formulated with the objective to minimize makespan. A heuristic was proposed to find the best assignment. The performance of the heuristic method was tested by comparing the quality of solution and computational time to optimal solutions. The experimental results show that the heuristic provides good solutions with a better CPU time compared to the optimal solutions. For problems 7, 8 and 9 workers, the heuristic found a solution within 3% of the optimal solutions with 0.67% on average.