Impact of Queue Configuration on Service Time: Evidence from a Supermarket

We study how queue configuration affects human servers’ service time by comparing dedicated queues with shared queues using field data from a natural experiment in a supermarket. We hypothesize that queue configuration may affect servers’ service rate through several mechanisms: pooling may affect service rate directly as a result of social loafing effect and competition effect and indirectly via its impact on queue length. To investigate these impacts, we take advantage of the supermarket’s checkout layout and use a data set containing both checkout transaction details and queue information collected from video recordings in the supermarket. After we control for the queue length, we find that servers in dedicated queues are about 10.7% faster than those in shared queues, mainly because of the social loafing effect. We also demonstrate that pooling has an indirect negative effect on service time through its impact on queue length. In addition, the queue configuration’s direct effect and its indirect queue length effect function independently of each other. In aggregation, the social loafing effect dominates, and servers slow down (a 6.86% increase in service time) in shared queues.postprin

The Human Factor: The Behavioral Drivers and Operational Impact of Discretion

Aim: Recent operations research acknowledges that agents in our operational systems have discretion to make decisions. Modeling this behavior requires assumptions, but these assumptions may induce gaps between models and real-world observations. In the end, these decisions coalesce firm-level outputs, both for good and ill. Despite this, deliberate system design can transform problematic deviance into productive discretion. In this dissertation, I detail three explorations of system design and the operational impact of human discretion. Background: The operations literature has a rich history of applying formal mathematical models to explain and study both product and service settings. Operational systems matter, but wherever these systems contain human discretion, people matter too. Context and Methodology: I primarily focus on the operational effects of discretion in the healthcare setting, where the literature frequently examines how providers shape a service system. My research empirically responds to each of my research questions with modern econometric and machine learning methods. In the first essay, I designed and implemented a field experiment among 145 healthcare clinics. In the second and third essays, I leverage archival data analysis methods. Conclusion: Operations research considers many facets of work: “What work should we do? When should we do it? How should we do it? And who should be doing it?” Given my focus on the role of people within the system, my work provides valuable clarity into how human discretion affects operational outcomes, and my insights empower future operations research to better understand the full spectrum of worker behavior.Doctor of Philosoph

Emergency Medical Service Ambulance System Planning: History and Models

Integer linear programming models that incorporate probabilistic and stochastic components represent one approach for capturing the stochastic nature of emergency medical service ambulance systems. This includes modeling non-deterministic call arrival and servicing rates and congestion in the ambulance network (i.e., ambulance unavailability). These models focus on maximizing the total population that can find an available ambulance within a set service time standard (s) with a probability of at least α%. In MALP the concept of local vehicle busyness estimates is introduced to estimate the availability of service in a neighborhood given the neighborhood’s level of demand and the number of ambulance vehicles located in the neighborhood. QMALP is an extension of MALP where queue-theory derived parameters are implemented in the MALP model framework in order to relax the assumption that the probability of different ambulances being busy are independent. Despite this considerable development, several concerns remained about MALP and QMALP, namely the districting assumption where its assumed that a neighborhood’s calls for service are served only by an ambulance in the area, that ambulances in a neighborhood only serve calls for service originating within the neighborhood, or that at least the flow of ambulance service to and from external neighborhoods was roughly equal. Questions have been raised about the validity of MALP and QMALP’s reliability estimates, that is, whether a neighborhood actually received α-reliable service.To address these issues, we developed the Resource-Constrained Queue-based Maximum Availability Location Problem (RC-QMALP). This model is based on a location-allocation framework that (1) assigns workload from neighborhoods to ambulances located within s and ambulance idle capacity to neighborhoods and (2) includes additional constraints designed to help ensure the validity of the original MALP and QMALP constraints used to establish whether a neighborhood can find an available ambulance with α-reliability. We also implemented a secondary minsum objective that minimizes the average travel distance between ambulances and the neighborhoods they service while maintaining the priority of the MALP and QMALP coverage objective.In this thesis, we validated RC-QMALP by comparing the reliable coverage levels predicted by the RC-QMALP to the ambulance system simulations that used the locational configurations suggested by the RC-QMALP. We found that MALP 2 and QMALP provided higher levels of reliable coverage and that RC-QMALP’s secondary objective has a negligible impact on system performance. However, RC-QMALP-based models provide more accurate estimates of reliable coverage and location solutions whose simulated reliable coverage performance was always within 5% of the optimal solution with the same system parameters (we tested 1,080 different model configurations). Our work suggests that (1) simulation models must be developed to handle the modeling assumptions that underlie location optimization models and that (2) service reliability location models should consider additional factors such as ambulance workloads (and their distribution)