Dynamic capacity adjustments with reactive customers

Ackere, Ann van; Haxholdt, Christian; Larsen, Erik

doi:10.1016/j.omega.2012.09.007

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…They assume that the experienced service quality in the first period influences a customer's valuation of the service in the second period. Van Ackere et al [26] investigate the impact of service capacity adjustments on customer retention and customer acquisition. Paul et al [27] provide an extensive overview of repeat purchase drivers.…”

Section: Related Literaturementioning

confidence: 99%

Maximizing customer equity subject to capacity constraints

Klein

Kolb

2015

Omega

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Section: Related Literaturementioning

confidence: 99%

Maximizing customer equity subject to capacity constraints

Klein

Kolb

2015

Omega

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“…They exploit some simulation methods with the decision variable of service rate. Recently, van Ackere et al [29] have started to develop a behavioral model in which customers come and go based on waiting time with a simulation method. Here, we address a similar issue using an analytical method of queuing.…”

Section: Literature Reviewmentioning

confidence: 99%

Staffing to Maximize Profit for Call Centers with Impatient and Repeat-Calling Customers

Gong

Tang

et al. 2015

Mathematical Problems in Engineering

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Motivated by call center practice, we study the optimal staffing of many-server queues with impatient and repeat-calling customers. A call center is modeled as an M/M/s+M queue, which is developed to a behavioral queuing model in which customers come and go based on their satisfaction with waiting time. We explicitly take into account customer repeat behavior, which implies that satisfied customers might return and have an impact on the arrival rate. Optimality is defined as the number of agents that maximize revenues net of staffing costs, and we account for the characteristic that revenues are a direct function of staffing. Finally, we use numerical experiments to make certain comparisons with traditional models that do not consider customer repeat behavior. Furthermore, we indicate how managers might allocate staffing optimally with various customer behavior mechanisms.

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“…This work has been extended by van Ackere et al. () to the situation in which facility's manager can adjust the service capacity. Delgado‐Alvarez et al.…”

Section: Introductionmentioning

confidence: 99%

Managing service facilities with endogenous arrival and service rates

Delgado-Alvarez

Ackere

Larsen

2017

Int Trans Operational Res

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We conduct a simulation‐based experiment to analyze how past experiences in a service facility system affect customers’ and service providers’ behavior. We study the problem faced by service providers in deciding by how much to adjust the capacity of their facility when captive repeat customers choose which facility to join based on their expected sojourn times. The customers’ decision‐making process differs from that of service providers in that the former is based on the customers’ experience, whereas the latter is based on the most recent information service providers have regarding demand. Customers use their previous experience and that of their neighbors to update their perceptions of the future average sojourn time, whereas service providers form perceptions of the future arrival rate. We use cellular automata to model the interaction between customers and service providers. We perform simulations to assess the way the customers’ and service providers’ decisions evolve and affect system behavior. Our findings demonstrate that the system we study possesses a certain degree of path dependence. The primary conclusion is that the more conservative service providers are regarding new information, the larger the market share they achieve, and the lower the probability that their facility will shut down.

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Dynamic capacity adjustments with reactive customers

Cited by 19 publications

References 26 publications

Maximizing customer equity subject to capacity constraints

Maximizing customer equity subject to capacity constraints

Staffing to Maximize Profit for Call Centers with Impatient and Repeat-Calling Customers

Managing service facilities with endogenous arrival and service rates

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