Stability and Chaos in Input Pricing for a Service Facility with Adaptive Customer Response to Congestion

Rump, Christopher M.; Stidham, Shaler

doi:10.1287/mnsc.44.2.246

Cited by 53 publications

(29 citation statements)

References 15 publications

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“…Papers that have considered a monopolist's pricing and capacity decisions in the presence of congestion include Mendelson (1985), Mendelson and Whang (1990), Dewan and Mendelson (1990), Stidham (1992), Rump and Stidham (1998), , Van Mieghem (2000), and Dobson and Stavrulaki (2006). With the exception of Dobson and Stavrulaki (2006), none of these papers incorporate customers' locations into their models, but they do consider customers' delay costs.…”

Section: Related Workmentioning

confidence: 99%

Competitive location and capacity decisions for firms serving time‐sensitive customers

Kwasnica

Stavrulaki

2008

Naval Research Logistics

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In this paper we explore how two competing firms locate and set capacities to serve timesensitive customers. Because customers are time-sensitive, they may decline to place an order from either competitor if their expected waiting time is large. We develop a two-stage game where firms set capacities and then locations, and show that three types of subgame perfect equilibria are possible: local monopoly (in which each customer is served by a single firm, but some customers may be left unserved), constrained local monopoly (in which firms serve the entire interval of customers but do not compete with each other), and constrained competition (in which firms also serve the entire interval of customers, but now compete for some customers). We perform a comparative statics analysis to illustrate differences in the equilibrium behavior of a duopolist and a coordinated monopolist.

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Section: Related Workmentioning

confidence: 99%

Competitive location and capacity decisions for firms serving time‐sensitive customers

Kwasnica

Stavrulaki

2008

Naval Research Logistics

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“…So the system designer must choose a service rate and a price (or arrival rate). The new constraint leads to a system in which the first order conditions may not lead to a globally optimal solution (see also Rump & Stidham (1998) and Kim & Mannino (2003)). Plambeck (2000), on the other hand, considers two classes of customersdelay sensitive and delay insensitive.…”

Section: Literature Reviewmentioning

confidence: 99%

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Hall¹,

Kopalle

Pyke

2002

SSRN Journal

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Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in production and supply chain management. The interactions between pricing and production/supply chain performance, however, are not as well understood. Can a firm benefit from knowing the status of the supply chain or production facility when making pricing decisions? How much can be gained if pricing decisions explicitly and optimally account for this status? This paper addresses these questions by examining a make-to-order manufacturer that serves two customer classes -core customers who pay a fixed negotiated price and are guaranteed job acceptance, and "fill-in" customers who make job submittal decisions based on the instantaneous price set by the firm for such orders.We examine four pricing policies that span a range of complexity and required knowledge about the status of the production system at the manufacturer, including the optimal policy of setting a different price for each possible state of the queue. We demonstrate properties of the optimal policy, and we illustrate numerically the financial gains a firm can achieve by following this policy vs. simpler pricing policies. The four policies we consider are (1) state-independent (static) pricing, (2) allowing fill-in orders only when the system is idle, (3) setting a uniform price up to a cut-off state, and (4) general state-dependent pricing. Although general statedependent pricing is optimal in this setting, we find that charging a uniform price up to a cut-off state performs quite well in many settings and presents an attractive trade-off between ease of implementation and profitability. Thus, a fairly simple heuristic policy may actually out-perform the optimal policy when costs of design and implementation are taken into account.2

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“…Customers' decisions in queuing systems with stochastic arrival and service patterns have been studied theoretically in operations research and management science by Edelson and Hilderbrand (1975), Stidham (1985), Dewan and Medelson (1990), van Ackere et al (1995), Rump and Stidham (1998), Zohar et al (2002), among others. Hassin and Haviv (2003) discuss a vast literature, which depicts widely a framework on equilibrium behaviour in stochastic queuing systems.…”

Section: Introductionmentioning

confidence: 99%

Modelling Decisions Under Uncertainty In A Behavioural Queuing System

Delgado¹,

Ackere²,

Sankaranarayanan³

et al. 2011

ECMS 2011 Proceedings Edited By: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho

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In this paper we use an agent-based modelling and simulation approach to model a queuing system with autonomous customers who routinely choose a facility for service. We propose a Cellular Automata model to represent the customers' interactions and study how customers use their own experience and that of their neighbours in order to update their memory and decide what facility to join the next period. We use exponential smoothing to update the customers' expected sojourn time. We incorporate uncertainty regarding these expectations into the customers' decision. We compare the resulting behaviour when customers take into account uncertainty to the case where they ignore uncertainty at both the individual and the system level.

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Stability and Chaos in Input Pricing for a Service Facility with Adaptive Customer Response to Congestion

Cited by 53 publications

References 15 publications

Competitive location and capacity decisions for firms serving time‐sensitive customers

Competitive location and capacity decisions for firms serving time‐sensitive customers

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Modelling Decisions Under Uncertainty In A Behavioural Queuing System

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