Showing papers by "Jan A. Van Mieghem published in 2020"

Capacity and Inventory Management: Review, Trends, and Projections

Abstract: We present a reproducible, objective review of research trends using text mining and citations of papers published in Manufacturing & Service Operations Management during its first 20 years whose a...

Collaboration, Interruptions, and Changeover Times: Workflow Model and Empirical Study of Hospitalist Charting

Abstract: Problem definition: Collaboration is important in services but may lead to interruptions. Professionals exercise discretion on when to preempt individual tasks to switch to collaborative tasks. Aca...

Digital Operations: Autonomous Automation and the Smart Execution of Work

Abstract: The integration of digital technologies is changing the way organizations operate and deliver value. Digitizing operations may replace manual work through increased automation, but it may also enable smarter execution of workflows thereby augmenting human work. Evaluating the level and reach of these three pillars--digitization, automation, and smart--across the organization’s value chain provides a diagnostic tool that can inspire future desired directions of digitization.

Dual Sourcing and Smoothing Under Non-Stationary Demand Time Series: Re-Shoring with Speedfactories

Abstract: We investigate near-shoring a small part of the global production to local \emph{SpeedFactories} that serve only the variable demand. The short lead time of the responsive SpeedFactory reduces the risk of making large volumes in advance, yet it does not involve a complete re-shoring of demand. Using a break-even analysis we investigate the lead time, demand, and cost characteristics that make dual sourcing with a SpeedFactory desirable compared to complete off-shoring. Our analysis employs a linear generalization of the celebrated order-up-to inventory policy to settings where capacity costs exist. The policy allows for order smoothing to reduce capacity costs and performs well relative to the (unknown) optimal policy. We highlight the significant impact of auto-correlated and non-stationary demand series, which are prevalent in practice yet challenging to analyze, on the economic benefit of re-shoring. Methodologically, we adopt a linear policy and normally distributed demand and use $Z-$transforms to present exact analyses.

Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations

Abstract: Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers' aversion, inability or discretion to precisely implement algorithmic prescriptions. We propose a new "human-centric bin packing algorithm" that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then pro-actively adjusts the algorithmic prescriptions of those ``targeted packages.'' We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba's original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations.

Feature-Based Design of Priority Queues: Digital Triage in Healthcare

Abstract: We study data-driven classification where a classifier assigns jobs (e.g., patients or medical images) based on observed features to priority queues for human review. Traditional classifiers are designed to minimize misclassification loss functions but may underperform when integrated with workflows that can be modeled by queueing systems for two key reasons: First, conventional loss functions do not capture the externalities inherent in queueing systems that amplify the impact of classification errors: misclassifying an urgent patient as non-urgent impacts the wait of other patients classified as urgent and non-urgent. The second problem is that the queueing system design (which includes the number of priority queues and the assignment of job types to priority queues) is typically optimized ex-ante assuming perfect classification. We propose an integral approach where the classifier and/or the queueing system are optimized to minimize the workflow's average waiting cost. We demonstrate the value of our approach using a real data-set covering 560,486 patient visits to three emergency rooms over three years. We theoretically characterize the optimal number of priority queues and the optimal prioritization policy as a function of the classifier's accuracy for tractable problem instances. Compared to the use of off-the-shelf classifiers, the integral approach significantly reduces average delay costs in highly utilized systems with significant heterogeneity in delay costs. It compensates for classifier inaccuracies by assigning job types differently and to fewer priorities than the traditional approach. Competing Interest Declaration: The authors declare that they have no competing financial, professional, or personal interests that might have influenced the design and the results of this study.