Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
A model-based framework to overlap product development activities
Management Science - Special issue on frontier research in manufacturing and logistics
Modeling and worker motivation in JIT production systems
Management Science
Dynamic Control of a Queue with Adjustable Service Rate
Operations Research
Work-Team Implementation and Trajectories of Manufacturing Quality: A Longitudinal Field Study
Manufacturing & Service Operations Management
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Commissioned Paper: On the Interface Between Operations and Human Resources Management
Manufacturing & Service Operations Management
Quality--Speed Conundrum: Trade-offs in Customer-Intensive Services
Management Science
Constraint-based methods for scheduling discretionary services
AI Communications
Information, Technology, and Information Worker Productivity
Information Systems Research
Diagnostic Accuracy Under Congestion
Management Science
Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay
Manufacturing & Service Operations Management
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Most performance evaluation models in the operations management literature implicitly assume that tasks possess standardized completion criteria. However, in many systems, particularly service and professional work, judgment is frequently required to determine how much time to allocate to a task. In this paper, we show that introducing discretion in task completion adds a fourth variability buffer, quality, to the well-known buffers of capacity, inventory and time. To gain insight into the managerial implications of this difference, we model the work of one- and two-worker systems with discretionary task completion as controlled queues. After characterizing the optimal control policy and identifying some practical heuristics, we use this model to examine the differences between discretionary and nondiscretionary work. We show that in systems with discretionary task completion, (i) adding capacity may actually increase congestion, and (ii) task variability in service time can improve system performance. This implies that it may be suboptimal to expect shorter delays as a result of a capacity increase, and that task variability reduction may not be an appropriate goal in systems with discretionary task completion. We also find that the benefit of queue pooling is smaller in systems with discretionary task completion than in systems with nondiscretionary task completion.