We consider the control of a production facility subject to multiple failure modes. Motivated by a work of Akella-Kumar (1986) and Bielecki-Kumar (1988) on single-failure-mode models, we study hedging-point policies, in which production is controlled to its maximum rate whenever inventory is below a critical level and set to zero whenever inventory is above that level. The maximum production rate varies with the state of the machine.
This article addresses the problem of valuing American call options with caps on dividend-paying assets. Since early exercise is allowed, the valuation problem requires the determination of optimal exercise policies. Options with two types of caps are analyzed: constant caps and caps with a constant growth rate. For constant caps, it is optimal to exercise at the first time at which the underlying asset's price equals or exceeds the minimum of the cap and the optimal exercise boundary for the corresponding uncapped option.
We analyze the performance of an importance sampling estimator for a rare-event probability in tandem Jackson networks. The rare event we consider corresponds to the network population reaching K before returning to ø, starting from ø, with K large. The estimator we study is based on interchanging the arrival rate and the smallest service rate and is therefore a generalization of the asymptotically optimal estimator for an M/M/1 queue.
We investigate the stability of discrete-event systems modeled as generalized semi-Markov processes with event epochs that satisfy (max, +) recursions. We obtain three types of results, under conditions: We show that there exists for each event a cycle time, which is the long-run average time between event occurrences; we characterize the rate of convergence to this limit, bounding the error for finite horizons; and we give conditions for delays (i.e., differences between event epochs) to converge to a stationary regime.
We consider Markovian GSMPs (generalized semi-Markov processes) in which the rates of events are subject to control. A control is monotone if the rate of one event is increasing or decreasing in the number of occurrences of other events. We give general conditions for the existence of monotone optimal controls. The conditions are functional properties for the one-step cost functions and, more importantly, structural properties for the GSMP. The main conditions on costs are submodularity or supermodularity with respect to pairs of events.
We investigate the dependence induced among multiple Markov chains when they are simulated in parallel using a shared Poisson stream of potential event occurrences. One expects this dependence to facilitate comparisons among systems; our results support this intuition. We give conditions on the transition structure of the individual chains implying that the coupled process is an associated Markov chain. Association implies that variance is reduced in comparing increasing functions of the chains, relative to independent simulations, through a routine argument.
Most models of multilevel production and distribution systems assume unlimited production capacity at each site. When capacity limits are introduced, an ineffective policy may lead to increasingly large order backlogs: The stability of the system becomes an issue. In this paper, we examine the stability of a multi-echelon system in which each node has limited production capacity and operates under a base-stock policy.
The material presented in this book originated from research on discrete-event systems when the outputs are monotone functions of the inputs. A discrete-event system is defined as a collection of elementary processes evolving asynchronously and interacting at irregular instants called event epochs. Similar results have often been established for different systems by using inductive sample-path arguments. The authors have identified the underlying structure and formulated general conditions for monotonicity. The material is organized in nine chapters.
The mean-variance model for portfolio selection requires estimates of many parameters. This paper investigates the effect of errors in parameter estimates on the results of mean-variance analysis. Using a small amount of historical data to estimate parameters exposes the model to estimation errors. However, using a long time horizon to estimate parameters increases the possibility of nonstationarity in the parameters. This paper investigates the tradeoff between estimation error and stationarity. A simulation study shows that the effects of estimation error can be surprisingly large.