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Competitive solutions for online financial problems
 ACM Comput. Surv
, 1998
"... This article surveys results concerning online algorithms for solving problems related to the management of money and other assets. In particular, the survey focuses on search, replacement, and portfolio selection problems. ..."
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This article surveys results concerning online algorithms for solving problems related to the management of money and other assets. In particular, the survey focuses on search, replacement, and portfolio selection problems.
Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List
, 2007
"... ..."
Nearly Optimal Competitive Online Replacement
"... This paper studies the following online replacement problem. There is a real function f(t), called the flow rate, defined over a finite time horizon [0; T ]. It is known that m f(t) M for some reals 0 m ! M . At time 0 an online player starts to pay money at the rate f(0). At each time 0 ! t T ..."
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This paper studies the following online replacement problem. There is a real function f(t), called the flow rate, defined over a finite time horizon [0; T ]. It is known that m f(t) M for some reals 0 m ! M . At time 0 an online player starts to pay money at the rate f(0). At each time 0 ! t T the player may changeover and continue paying money at the rate f(t). The complication is that each such changeover incurs some fixed penalty. The player is called online as at each time t the player knows f only over the time interval [0; t]. The goal of the player is to minimize the total cost comprised of cumulative payment flow plus changeover costs. This formulation of the replacement problem has various interesting applications among which are: equipment replacement, supplier replacement, the menu cost problem and mortgage refinancing.
Optimal Joint Preventive Maintenance and Production Policies*
, 2005
"... Abstract: We study joint preventive maintenance (PM) and production policies for an unreliable productioninventory system in which maintenance/repair times are nonnegligible and stochastic. A joint policy decides (a) whether or not to perform PM and (b) if PM is not performed, then how much to pro ..."
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Abstract: We study joint preventive maintenance (PM) and production policies for an unreliable productioninventory system in which maintenance/repair times are nonnegligible and stochastic. A joint policy decides (a) whether or not to perform PM and (b) if PM is not performed, then how much to produce. We consider a discretetime system, formulating the problem as a Markov decision process (MDP) model. The focus of the work is on the structural properties of optimal joint policies, given the system state comprised of the system’s age and the inventory level. Although our analysis indicates that the structure of optimal joint policies is very complex in general, we are able to characterize several properties regarding PM and production, including optimal production/maintenance actions under backlogging and high inventory levels, and conditions under which the PM portion of the joint policy has a controllimit structure. In further special cases, such as when PM setup costs are negligible compared to PM
Ann Oper Res (2013) 208:5–26 DOI 10.1007/s104790131429x On the life and work of Cyrus Derman
, 2013
"... © The Author(s) 2013. This article is published with open access at Springerlink.com Abstract This article provides a brief biographical synopsis of the life of Cyrus Derman and a comprehensive summary of his research. Professor Cyrus Derman was known among his friends as Cy. ..."
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© The Author(s) 2013. This article is published with open access at Springerlink.com Abstract This article provides a brief biographical synopsis of the life of Cyrus Derman and a comprehensive summary of his research. Professor Cyrus Derman was known among his friends as Cy.
Stochastics and Statistics A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system
, 1999
"... We consider a repairable system subject to a continuoustime Markovian deterioration while running, that leads to failure. The deterioration degree is measured with a finite discrete scale; repairs follow general distributions; failures are instantaneously detected. This system is submitted to a pre ..."
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We consider a repairable system subject to a continuoustime Markovian deterioration while running, that leads to failure. The deterioration degree is measured with a finite discrete scale; repairs follow general distributions; failures are instantaneously detected. This system is submitted to a preventive maintenance policy, with a sequential checking procedure: the upstates are divided into two parts, the ‘‘good’ ’ upstates and the ‘‘degraded’ ’ upstates. Instantaneous (and perfect) inspections are then performed on the running system: when it is found in a degraded upstate, it is stopped to be maintained (for a random duration that depends on the degradation degree of the system); when it is found in a good upstate, it is left as it is. The next inspection epoch is then chosen randomly and depends on the degradation degree of the system by time of inspection. We compute the longrun availability of the maintained system and give sufficient conditions for the preventive maintenance policy to improve the longrun availability. We study the optimization of the longrun availability with respect to the distributions of the interinspection intervals: we show that under specific assumptions (often checked), optimal distributions are nonrandom. Numerical examples are studied.
A Note on the Optimal Replacement Problem
"... Abstract: We study the following model for a system the state of which is continuously observed. The set of possible states is a finite set {0,..., L}, where larger values represent increased states of deterioration from the ”new condition ” represented by state 0, to the “totally inoperative condit ..."
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Abstract: We study the following model for a system the state of which is continuously observed. The set of possible states is a finite set {0,..., L}, where larger values represent increased states of deterioration from the ”new condition ” represented by state 0, to the “totally inoperative condition ” of state L. Whenever the system changes state a decision has to be made as to whether it is renovated or it is left unattended. Whenever the system enters a state i < L and the decision to renovate is taken, then a cost c is incurred and its state, immediately, changes to a fixed state l. If the system enters state L then it must be renovated at an increased cost c + A. There is no cost whenever the decision to leave it unattended is taken in a state i < L; in this case the next state will be state j with probability pij and the sojourn time in state i is a random variable with distribution function Fij(·). We provide necessary conditions under which optimal policies are of the “control limit ” type. The results herein generalize those of Derman [1] when the state sojourn times are distributed according to a general state dependent distribution and a renovation may not result in a “new system ” i.e., the renovation state l maybe l> 0.
Repeat Purchase Decisions under Sequential Innovation
, 2003
"... Improving technologies create a “buy or wait? ” dilemma. In this paper, we consider repeat purchases when the consumer faces an infinite stream of new technologies. We develop a probabilistic model and focus on the role of “more variability ” on the process of technological innovation. Similar to r ..."
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Improving technologies create a “buy or wait? ” dilemma. In this paper, we consider repeat purchases when the consumer faces an infinite stream of new technologies. We develop a probabilistic model and focus on the role of “more variability ” on the process of technological innovation. Similar to real options models, we find that variability in the technological process increases value for the consumer. Unlike other results we have seen, more variability lowers the optimal threshold. Using the monotonicity of the strategy in variability, we derive upper and lower bounds on the critical threshold.
Health Care Manage Sci (2007) 10:81–93 DOI 10.1007/s1072900690072 Safetycost tradeoffs in medical device reuse: a Markov decision process model
"... Abstract Healthcare expenditures in the US are approaching $2 trillion, and hospitals and other healthcare providers are under tremendous pressure to rein in costs. One costsaving approach which is gaining popularity is the reuse of medical devices which were designed only for a single use. Devic ..."
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Abstract Healthcare expenditures in the US are approaching $2 trillion, and hospitals and other healthcare providers are under tremendous pressure to rein in costs. One costsaving approach which is gaining popularity is the reuse of medical devices which were designed only for a single use. Device makers decry this practice as unsanitary and unsafe, but a growing number of thirdparty firms are willing to sterilize, refurbish, and/or remanufacture devices and resell them to hospitals at a fraction of the original price. Is this practice safe? Is reliance on singleuse devices sustainable? A Markov decision process (MDP) model is formulated to study the tradeoffs involved in these decisions. Several key parameters are examined: device costs, device failure probabilities, and failure penalty cost. For each of these parameters, expressions are developed which identify the indifference point between using new and reprocessed devices. The results can be used to inform the debate on the economic, ethical, legal, and environmental dimensions of this complex issue.