A review of the healthcare-management (modeling) literature published at Manufacturing and Service Operations Management

Healthcare systems throughout the world are under pressure to widen access, improve efficiency and quality of care, and reduce inequity. Achieving these conflicting goals requires innovative approaches, utilizing new technologies, data analytics, and process improvements. The operations management community has taken on this challenge: more than 10% of articles published in M&SOM in the period from 2009 to 2018 has developed analytical models that aim to inform healthcare operational decisions and improve medical decision-making. This article presents a review of the research published in M&SOM on healthcare management since its inception 20 years ago and reflects on opportunities for further research

Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

Proactive Customer Service: Operational Benefits and Economic Frictions

Problem Definition: We study a service setting where the provider has information about some customers' future service needs and may initiate service for such customers proactively, if they agree to be flexible with respect to the timing of service delivery. Academic / Practical Relevance: Information about future customer service needs is becoming increasingly available through remote monitoring systems and data analytics. However, the literature has not systematically examined proactive service as a tool that can be used to better match demand to service supply when customers are strategic. Methodology: We combine i) queueing theory, and in particular a diffusion approximation developed specifically for this problem that allows us to derive analytic approximations for customer waiting times, with ii) game theory, which captures customer incentives to adopt proactive service. Results: We show that proactive service can reduce customer waiting times, even if only a relatively small proportion of customers agree to be flexible, the information lead time is limited, and the system makes occasional errors in providing proactive service - in fact we show that the system's ability to tolerate errors increases with (nominal) utilization. Nevertheless, we show that these benefits may fail to materialize in equilibrium because of economic frictions: customers will under-adopt proactive service (due to free-riding) and over-join the system (due to negative congestion-based externalities). We also show that the service provider can incentivize optimal customer behavior through appropriate pricing. Managerial Implications: Our results suggest that proactive service may offer substantial operational benefits, but caution that it may fail to fulfill its potential due to customer self-interested behavior

Surge pricing on a service platform under spatial spillovers: evidence from Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price spillover. There is a debate in the management community on the ecacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.First author draf

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

Throbbing Between Two Lives: Resource Pooling in Service Supply Chains

Resource pooling is known to benefit performance through reduced congestion, but primarily in settings with homogenous demand. In settings where demand is heterogeneous, pooling can be counter effective. The effects of pooling of staff when demand is heterogeneous and dependent are not known. We present a simulation model based on a service supply chain that delivers Interactive TV to customers. Customers expect high performance in terms of innovativeness and reliability. Based on the results of simulation analysis, we find that when target innovativeness of the service is increased, pooling outperforms not pooling, but the delays that are involved with pooling will make the system and hence its performance unstable. Stable and high performance can be realized through unbalanced hiring. This means that a target performance increase in the upstream stage of the chain (innovation), is accompanied by hiring staff in the downstream stages of the chain (QA and operation)