Work process improvement through simulation optimization of task assignment and mental workload

Cansu Kandemir () and Holly A. H. Handley ()
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Cansu Kandemir: Izmir University of Economics
Holly A. H. Handley: Old Dominion University

Computational and Mathematical Organization Theory, 2019, vol. 25, issue 4, No 2, 389-427

Abstract: Abstract The outcome of a work process depends on which tasks are assigned to which employees. However, sometimes optimized assignments based on employees’ qualifications may result in an uneven and ineffective workload distribution. Likewise, an even workload distribution without considering the employee’s qualifications may cause unproductive employee-task matching that results in low performance. This trade-off is even more noticeable for work processes during critical time junctions, such as in military command centers and emergency rooms that require fast, effective and error free performance. This study evaluates optimizing task-employee assignments according to their capabilities while also maintaining a workload threshold. The goal is to select the employee-task assignments in order to minimize the average duration of a work process while keeping the employees under a workload threshold to prevent errors caused by overload. Due to uncertainties related with the inter-arrival time of work orders, task durations and employees’ instantaneous workload, a simulation–optimization approach is required. A discrete event human performance simulation model was used to evaluate the objective function of the problem coupled with a genetic algorithm based meta-heuristic optimization approach to search the solution space. A sample work process is used to show the effectiveness of the developed simulation–optimization approach. Numerical tests indicate that the proposed approach finds better solutions than common practices and other simulation–optimization methods. Accordingly, by using this method, organizations can increase performance, manage excess-level workloads, and generate higher satisfactory environments for employees, without modifying the structure of the process itself.

Keywords: Simulation optimization; Genetic algorithm; Mental workload analysis (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10588-018-9275-7

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