Optimal Scheduling and Incentive Compatible Pricing for a Service System with Quality of Service Guarantees

Hsu, Vernon Ning; Xu, Susan H.; Jukić, Boris

doi:10.1287/msom.1080.0226

Cited by 15 publications

(11 citation statements)

References 27 publications

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“…In problems of socially optimal and IC pricing and scheduling, the solution is the same as that without IC constraints, and the optimal scheduling policy is work conserving (Mendelson and Whang 1990, Van Mieghem 2000, Hsu et al 2009). Most studies of revenue/profit maximization restrict the scheduling policy, customers' service class choices, or both (Lederer andLi 1997, Rao andPetersen 1998, Boyaci and Mendelson 2004, Maglaras and Zeevi 2005, Allon and Federgruen 2009, Jayaswal et al 2011, Zhao et al 2012).…”

Section: Literature and Positioningmentioning

confidence: 99%

Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay

Afèche

2013

M&SOM

163

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H ow should a firm design a price/lead-time menu and scheduling policy to maximize revenues from heterogeneous time-sensitive customers with private information about their preferences? We consider this question for a queueing system with two customer types and provide the following results. First, we develop a novel problem formulation and solution method that combines the achievable region approach with mechanism design. This approach extends to menu design problems for other systems. Second, the work conserving c priority rule, known to be delay cost minimizing, incentive-compatible, and socially optimal, need not be revenue maximizing. A strategic delay policy may be optimal: It prioritizes impatient customers, but artificially inflates the lead times of patient customers. This suggests a broader guideline: Revenue-maximizing firms that lack customer-level demand information should also consider customer incentives, not only operational constraints, in their scheduling policies. Third, we identify general necessary and sufficient conditions for optimal strategic delay: a price, a lead-time, and a segment-size condition. We translate these into demand and capacity parameter conditions for cases with homogeneous and heterogeneous valuations for each type. In some cases strategic delay is optimal if capacity is relatively abundant, in others if it is relatively scarce.

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Section: Literature and Positioningmentioning

confidence: 99%

Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay

Afèche

2013

M&SOM

163

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“…Rao and Peterson () study the optimal pricing of priority services in a static service facility with n customers who maximize their own profits. Van Mieghem () and Hsu, Xu, and Jukic () consider socially optimal pricing and scheduling. Afeche and Mendelson () consider pricing and priority auctions under a generalized delay cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rao and Peterson (1998) study the optimal pricing of priority services in a static service facility with n customers who maximize their own profits. Van Mieghem (2000) and Hsu, Xu, and Jukic (2009) consider socially iv Incorporating this demand premium, which is already understood in the literature, will also lead to confounding and difficulty in separating the two types of benefits of free-shipping. optimal pricing and scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Differentiation with Shipping as an Ancillary Service? Free Service, Prioritization, and Strategic Delay

Sainathan

2017

Decision Sciences

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A service provider/retailer offers ancillary service (e.g., shipping by an online retailer) to two types of customers, impatient and patient, who may be heterogeneous both in their delay sensitivities and service valuations. She can use prioritization and/or strategic delay to differentiate them by offering two service classes and charging different prices, potentially resulting in a split in which a single customer type selects both the classes. Her objective is to minimize cost while satisfying individual rationality and incentive compatibility conditions. We characterize the optimal solutions under both exogenous and endogenous capacities. We examine the conditions under which the following strategically important features of service delivery are optimal, and relate them to practical scenarios: (i) free service, (ii) single/differentiated service, (iii) split of customers, and (iv) strategic delay. We find that the presence of these features depends on (i) whether the retailer has limited or sufficient capacity and (ii) whether she sells fashion goods or staple products. A typical explanation for offering free service is that it increases demand from customers. We make an operational case for it by showing that even if demand does not change, free service is still optimal under some scenarios.

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“…In his models, customers trade off between their service time in the system (and the corresponding cost paid) and the effort put into the service, a trade-off that is absent in our model. See Hsu et al (2009) and the references therein for more studies on incentive-compatible pricing and optimal scheduling in service systems. Cachon and Feldman (2011) point out the value of comparing two commonly used pricing schemes, in their case per-use pricing and subscription pricing.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing and Operational Performance in Discretionary Services

Tong

Rajagopalan

2014

Production and Operations Management

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In many services, for example, website or landscape design, the value or quality derived by a customer depends upon the service time, and this valuation differs across customers. Customers procure the service based on the expected value to be delivered, prices charged, and the timeliness of service. We investigate the performance of the optimal pricing scheme as well as two commonly used pricing schemes (fixed fee and time‐based pricing) for such services on important dimensions such as revenue, demand served, and utilization. We propose a novel model that captures the above features and wherein both service rate and demand are endogenous and functions of the pricing scheme. In particular, service time is an outcome of the pricing scheme adopted and the heterogeneous valuations of customers, unlike in the queueing‐based pricing literature. We find that the service system may benefit from a greater variance in consumer valuations, and the performance of pricing schemes is impacted by the shape of the distribution of customers' valuation of service time and the responsiveness desired by customers. Both the fixed fee and time‐based schemes do well relative to the optimal pricing scheme in terms of revenue in many plausible scenarios, but there are substantial differences between the pricing schemes in some important operational metrics. For instance, the fixed fee scheme serves more customers and has higher utilization than the time‐based scheme. We also explore variants of the fixed and time‐based schemes that have better revenue performance and show that the two‐part tariff which is a combination of fixed and time‐based pricing can do as well as the optimal scheme in terms of revenue.

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Optimal Scheduling and Incentive Compatible Pricing for a Service System with Quality of Service Guarantees

Cited by 15 publications

References 27 publications

Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay

Incentive-Compatible Revenue Management in Queueing Systems: Optimal Strategic Delay

Customer Differentiation with Shipping as an Ancillary Service? Free Service, Prioritization, and Strategic Delay

Pricing and Operational Performance in Discretionary Services

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