Service Capacity Allocation Research Articles

Managing Outpatient Service with Strategic Walk-ins

Outpatient care providers usually allow patients to access service via scheduling appointments or direct walk-in. Patients choose strategically between these two access channels (and otherwise balking) based on the trade-off of appointment delay and in-clinic waiting. How to manage outpatient care with such dual access channels, taking into account patient strategic choice behavior, is a challenge faced by providers. We study three operational levers to address this management challenge: service capacity allocation between these two channels, appointment delay information revelation via the choice and design of online scheduling systems, and a walk-in triage system that restricts the use of walk-in hours only for acute care. By studying a stylized queueing model, we find that neither a real-time online scheduling system (which offers instant access to appointment delay information at time of booking) nor an asynchronous online system (which does not directly provide delay information) can be universally more efficient. Although real-time systems appear more popular in practice, asynchronous systems sometimes can result in higher operational efficiency. Under the provider’s optimal capacity allocation, which scheduling system is more efficient hinges on two key factors: the patient demand–provider capacity relationship and patient willingness to wait. For the walk-in triage system, we find that it may or may not be beneficial to adopt; the provider’s own cost trade-off between lost demand and overtime work is the key determinant. Our research highlights that there is no one-size-fits-all model for outpatient care management, and the best use of operational levers critically depends on the practice environment. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: S. Wang’s work was supported in part by the National Natural Science Foundation of China [Grants 72001220 and 71931008]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4676 .

Revenue Management of Group Service Considering Different Quality Levels and Stochastic Customer Patience

We consider a service that provides different quality levels to customers who are served in groups (referred to as “group service” (GS)). We consider stochastic customer patience and build a multitype service model to maximize service profit. First, we consider a fixed number of service personnel and decompose the model into several subproblems. We then transform the model into a service personnel allocation problem that can be efficiently solved. Furthermore, we use the water-filling theory, together with a dichotomy approach, to design the numerical algorithm to conduct a numerical test based on actual data. The main contributions of this study are as follows. First, we provide the service provider with a service capacity allocation mechanism that can determine the optimal choice of service quality levels, number of service groups of different quality levels, time length of the service registration period, and service prices. Second, we find that under scarce service capacity, the single-type service can be more profitable than the multitype service, and low-quality services have higher marginal profits than high-quality services. However, when service capacity increases to a certain level, the multitype service can be more profitable than the single-type service. Third, we construct a service capacity decision mechanism and demonstrate the influence of service quality on the total number of service personnel and service groups. The conclusions are potentially useful to GS service providers to effectively make better managerial decisions and improve service profits.

Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach

With the rise of “cross-border-e-commerce”, the third-party-forwarding-logistics (3PFL) service becomes increasingly popular. Different from the traditional third-party-logistics (3PL) service, the 3PFL company provides forwarding services cost-effectively by consolidating orders from different e-tailers/platforms. The random arrivals of orders create a big challenge. Different from most of the existing studies, a deep learning based one-step integration optimal decision making approach S2SCL(Seq2Seq based CNN-LSTM) is proposed in this paper which intelligently integrates inventory optimization and demand-forecasting process. The Seq2Seq based forecasting architecture, which integrates CNN and LSTM network, is able to model the system dynamics and dependency-relations in varying demand for logistics services. Besides generating the point forecasting results, the proposed approach can quantify demand uncertainty via a dynamic distribution and make optimal decision on logistics service capacity allocation. Through a case-study analysis with real data obtained from a 3PFL company in China’s Great Bay Area, we compare the proposed S2SCL with two benchmark models, including a one-step statistics based integration approach ARIMA and a two-step optimization based approach PSO-ELM, for two tasks: (1) point forecasting and (2) optimal logistic service capacity (LSC) allocation. Experimental results show that S2SCL outperforms the two benchmark models in both tasks significantly.

Optimization of operation policy on dynamic demand of PSS

Product-Service Systems (PSS) provide a new way of value creation, but recent studies of PSS seldom consider the relationship between customers’ demand on PSS and the service capacity of enterprises. Also, they lack discussions on the relationship between customers’ dynamic demand on PSS and the integrated value of PSS. Thus we get inspired from the operation policies of traditional production-inventory systems and traditional service systems, to promote an integrated operation policy analysis of PSS offering. We formulate customer’s perceived value of PSS as the summation of product value and service value. Then the perceived value is compared to a benchmark value to figure out the customers’ purchasing behavior, so as obtaining the steady statement of the demands. Subsequently, we defined an optimization problem to determine the dynamic service policy, namely service capacity allocation, to maximize the enterprise’s revenue. We adopt a numerical study, which shows that the revenue is a convex function to demand and service capacity and we can obtain the optimal service policy when demands are given. Our study shed light on the integrated analysis of balancing PSS demand and service capacity, as well as promote a new way to analyze the value format of PSS.

Auction Design for the Efficient Allocation of Service Capacity Under Congestion

We consider the problem of efficiently allocating the capacity of a number of service facilities (prone to congestion) to a set of users with private information regarding their willingness to pay for different combinations of throughput versus latency. Auction mechanisms can be used to schedule the service capacity of available facilities. However, the interdependency of users’ valuations implies that simple uniform price adjustment processes (e.g., tatonnement) either fail to effectively clear or are subject to strategic manipulation. In this paper, we propose an iterative auction design and show that (i) it is efficient (i.e., the auction closes with the allocation of service that maximizes the social welfare) and (ii) it is strategy-proof, that is, it is a dominant strategy for users to truthfully reveal their demand for service capacity throughout the auction.

Optimal Scheduling Policies for a Production Service System with Two Items

In this paper, we address the capacity allocation rules for a flexible production service system consisting of one flexible manufacturing facility and one service centre. The manufacturing facility produces two items in the first stage and the service centre serves two customers in the second stage. Each order served by the service centre depletes one unit of dedicated item produced by the manufacturing facility. The manager must make production and service capacity allocation decisions. The objective is to find an effective production and service scheduling rule to minimize the expected total cost over an infinite horizon. The service capacity allocation problem is formulated as a Markov decision process and use this formulation to characterize the structure of the optimal service allocation policy. We show that the optimal service scheduling policy is an extension of the well-known optimal service scheduling policy for classical service system. Then the characteristics of the optimal production policy are studied numerically since the analytic characteristics are not available.

Allocating capacity in parallel queues to improve their resilience to deliberate attack

We develop models that lend insight into how to design systems that enjoy economies of scale in their operating costs, when those systems will subsequently face disruptions from accidents, acts of nature, or an intentional attack from a well-informed attacker. The systems are modeled as parallel M/M/1 queues, and the key question is how to allocate service capacity among the queues to make the system resilient to worst-case disruptions. We formulate this problem as a three-level sequential game of perfect information between a defender and a hypothetical attacker. The optimal allocation of service capacity to queues depends on the type of attack one is facing. We distinguish between deterministic incremental attacks, where some, but not all, of the capacity of each attacked queue is knocked out, and zero-one random-outcome (ZORO) attacks, where the outcome is random and either all capacity at an attacked queue is knocked out or none is. There are differences in the way one should design systems in the face of incremental or ZORO attacks. For incremental attacks it is best to concentrate capacity. For ZORO attacks the optimal allocation is more complex, typically, but not always, involving spreading the service capacity out somewhat among the servers. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

Stochastic optimization for real time service capacity allocation under random service demand

The problem of repeated allocation of limited renewable service resources to dis- tributed service centers is considered here. The objective is to assure a given Quality of Service expressed through percentage of demand which is satisfied during a specified time period. Resource requirements are not fully known at the time when a decision about the service resource distribution is taken. The problem is addressed by formulating a succession of stochastic optimization prob- lems solved at the time of resource allocation. Solutions of these problems are derived by applying duality theory. We pay special attention to the interplay between performance and risk by introducing the concept of a risk budget. Results of numerical experiments confirm the efficiency of the approach.

On optimal service capacity allocation policy in an advance selling environment in continuous time

Based on some general reasonable assumptions, this paper employs the techniques of calculus of variations and optimal control theory to derive 10 main propositions associated with a service provider who aims at maximizing the present value of revenues/profits over a planning horizon in continuous time. Employing an aggregate pricing-response function that is dependent on excess capacity and a convex service cost formulation, the results show that (i) for a zero discount rate and unanticipated competitive entry, units of service capacity are allocated evenly over time, (ii) for a zero discount rate and anticipated competitive entry, units of allocated service capacity are increasing over time, (iii) for a positive discount rate and unanticipated competitive entry, units of allocated service capacity are decreasing over time, and (iv) for a positive discount rate and anticipated competitive entry, units of allocated service capacity are increasing over time, or are decreasing at first then increasing later. In addition, the questions of how deep a service firm should advance sell, whether advance selling is optimal in continuous time, and whether excess capacity could be an optimal strategy have been addressed. Furthermore, related sensitivity analyses are performed and endpoint constraints together with alternative shapes of the service-cost function are discussed. The managerial implications of the study’s results have been demonstrated.

Optimal multi-period service capacity allocation and pricing allowing for uncertain competitive entry

A nonlinear programming model is formulated in this paper to determine the optimal scheme of capacity allocation and prices over a multi-period planning horizon for a service provider in the absence and presence of uncertain competitive entry. The model is solved for constant, decreasing, and increasing price sensitivities employing a combination of analytical and numerical methods. The study highlights the importance of advance selling of service prior to its eventual consumption in the spot period and investigates the impact of uncertain competitive entry on the optimal capacity allocation policy and its related profit if the entry is more or less likely or if the rival is more or less influential. The findings of the study reveal that the conclusions drawn from a two-period model are not necessarily generalizable to a model of a multi-period planning horizon.

Optimal Service Capacity Allocation Problem in Tandem Production Systems and Its Solution via Second Order Cone Programming

Optimal Service Capacity Allocation Problem in Tandem Production Systems and Its Solution via Second Order Cone Programming