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This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows. 

We consider the problem of finding a system with the best primary performance measure among a finite number of simulated systems in the presence of subjective stochastic constraints on secondary performance measures. When no feasible system exists, the decision maker may be willing to relax some constraint thresholds. We take multiple threshold values for each constraint as a user’s input and propose indifference-zone procedures that perform the phases of feasibility check and selection-of-the-best sequentially or simultaneously. Given that there is no change in the underlying simulated systems, our procedures recycle simulation observations to conduct feasibility checks across all potential thresholds. We prove that the proposed procedures yield the best system in the most desirable feasible region possible with at least a pre-specified probability. Our experimental results show that our procedures perform well with respect to the number of observations required to make a decision, as compared with straight-forward procedures that repeatedly solve the problem for each set of constraint thresholds, and that our simultaneously-running procedure provides the best overall performance.