Workforce grouping and assignment with learning-by-doing and knowledge transfer
研究工人通过实践和观察同事学习时的分组与任务分配问题，将非线性规划转化为混合整数规划以快速求解，并分析最优方案的管理启示。
We consider a workforce allocation problem in which workers learn both by performing a job and by observing the performance of and interacting with co-located colleagues. As a result, an organisation can benefit from both effectively assigning individuals to jobs and grouping workers into teams. A challenge often faced when solving workforce allocation models that recognise learning is that learning curves are non-linear. To overcome this challenge, we identify properties of an optimal solution to a non-linear programme for grouping workers into teams and assigning the resulting teams to sets of jobs. With these properties identified, we reformulate the non-linear programme to a mixed integer programme that can be solved in much less time. We analyse (near-)optimal solutions to this model to derive managerial insights.