The journal Naval Research Logistics has recently published a special issue on Service Operations Management. The special issue was edited by Professor Opher Baron and brings together eight papers on service operations management today and in the future. Read the full special here. Professor Baron’s introduction to the special issue is included below: 

This special issue has eight high-quality papers that shed light on service operations management today and in the future. The issue starts with the review paper “Business analytics in service operations—Lessons from healthcare operations.” This paper reviews the literature on healthcare analytics, focusing on papers that address waiting time management. Moreover, it discusses queue mining that is a modern data-driven methodology to analyze queues. Based upon this review, the author presents a novel framework for business analytics. This framework is relevant to anyone who is interested in business analytics. The paper then lists thoughts on the ongoing and future contributions of operations research and management science (ORMS) in business analytics and the impact of the peer review process on research in ORMS.

The next paper, “Accounting for capacity: A real-time optimization approach to managing observation unit utilization,” is also within the area of business analytics in healthcare. The authors provide a data-driven real-time optimization approach to support patients’ routing decisions upon leaving the emergency department. Specifically, they consider the routing choice among the hospital observation unit, which is designed to observe patients over a relatively short period of time and the hospital’s wards. Such routing decisions have implications on the wellbeing of patients and on the financial compensation for service. One of the main difficulties in making good routing decisions in hospitals and other complex service systems is that they require live access to data from different administrative units and the need to improve the process, as seen by all these different units. The authors develop an effective and data-driven optimization routing algorithm that assigns a patient to observation units while considering the load of different hospital units. Moreover, the authors numerically demonstrate the practicality and efficacy of their approach.

The next four papers all consider strategic customers, i.e., customers that consider waiting time in their purchase decisions. In “Equilibrium strategies and optimal pricing in an online retailing queueing system“, the authors model a monopolistic retailer as an unobservable M/M/1 make-to-order system with orbit. Customers’ valuation for the product is uncertain, and they find up this value after their stay in the orbit. If this value is high, customers are matched with items and leave the system, but if it is low, then they can either return the product or exchange it. The authors characterize the optimal pricing strategy given customers’ equilibrium strategies and show that the throughput (i.e., purchase rate) may be non-monotone in the product’s quality and that the optimal price may decrease in the matching probability. Moreover, they show that returns may reduce social welfare, and it improves when customers replace rather than return the item.

In “Pricing strategy and collusion in a market with delay sensitivity“, the authors model two firms that may coordinate their strategies to increase profitability when demand depends on waiting times. In the infinitely repeated game they analyze, each firm operates as unobservable M/M/1 and aims at maximizing its discounted profit. They investigate the incentives and ability of both firms to collude. The authors demonstrate that these incentives increase with the level of competition, but the feasibility of such a collaboration depends on other market characteristics, including customers’ delay sensitivity, the service value, and the possibly asymmetric capacities. The authors shed light on the implication of their results to welfare maximization and capacity planning.

In “The disadvantage of the C𝜇-rule when customers are strategic“, the authors consider how strategic heterogenous customers should be prioritized. They consider an unobservable M/M/1 queue, focusing on two common scheduling policies, the C𝜇-rule, where customers with a higher value of waiting costs per service time gain preemptive priority over customers with a lower such value, and the standard first-come-first-served (FCFS). The authors show that while under central decision making the C𝜇-rule optimizes the social welfare, when customers are strategic, the C𝜇-rule is suboptimal and is dominated by the FCFS policy. Their results shed light on the difficulty of operating the public healthcare system effectively and the ineffectiveness of using the C𝜇-rule in such settings.

In “On the optimal disclosure of queue length information“, the authors consider customers arriving at an unobservable M/M/1. The authors show that providing queue length information for customers may optimize social welfare- provided that customers are unaware of this policy. Moreover, the authors establish that in these settings, the throughput maximizing policy provides information to customers when it is shorter than Naor’s balking threshold. Then, the authors demonstrate that the socially optimal disclosure policy provides queue length only to customers who observe a sufficiently short or sufficiently long queue. They demonstrate numerically that these findings also hold for the M/G/1 queue.

The last two papers consider situations where service time depends on the system characteristics. In “Multiclass state-dependent service systems with returns“, the authors consider heterogeneous customers arriving at a multiserver queue with a rate that depends on the system’s workload and requires a repeat visit as a function of their service time. They provide stability conditions for this system and investigate how the system is affected by the workload, service times, arrival rates, the chance of repeat visits, and the inter-visit times. They used a fluid model to show that such systems may shift among several possible equilibria. They find that the stability of equilibrium may be affected by the time customers spend in the system between repeat visits. In contrast, in a single class system with an arrival rate that is nonincreasing in the workload and a service time that never grows with the workload (i.e., there is no service time saturation effect), the inter-visit times do not impact the system’s stability.

Finally, in “Capacity choice game in a multiserver queue: Existence of a Nash equilibrium“, the authors consider an M/M/2 where each server can choose her capacity. This model captures the ability of front-line workers to choose their service rate. They establish that the existence of a symmetric Nash equilibrium depends on the servers’ capacity cost functions and on whether the servers can choose a capacity that leads to instability. They show that, under some appealing cost structure, without forcing servers to choose a high enough capacity for stability, a symmetric pure-strategy Nash equilibrium for the capacity choice exists. This result contrast with earlier results in the literature that only considered the case when servers are forced to choose a sufficiently high capacity (for stability to exist). Under this restriction, such a symmetric pure-strategy Nash equilibrium may not exist. To establish their results the authors used Tarski’s intersection theorem, which may be applicable in other situations related to operations.