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作者：Bandi, Chaithanya; Bertsimas, Dimitris; Youssef, Nataly
作者单位：Northwestern University; Massachusetts Institute of Technology (MIT)
摘要：We propose an alternative approach for studying queues based on robust optimization. We model the uncertainty in the arrivals and services via polyhedral uncertainty sets, which are inspired from the limit laws of probability. Using the generalized central limit theorem, this framework allows us to model heavy-tailed behavior characterized by bursts of rapidly occurring arrivals and long service times. We take a worst-case approach and obtain closed-form upper bounds on the system time in a mu...
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作者：Federgruen, Awi; Wang, Min
作者单位：Columbia University; Drexel University
摘要：In this paper, we show how any model with a general shelf-age-dependent holding cost and delay-dependent backlogging cost structure may be transformed into an equivalent model in which all expected inventory costs are level dependent. We develop our equivalency results, first, for periodic review models with full backlogging of stockouts. These equivalency results permit us to characterize the optimal procurement strategy in various settings and to adopt known algorithms to compute such strate...
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作者：Reiman, Martin I.; Wang, Qiong
作者单位：Alcatel-Lucent; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Optimizing multiproduct assemble-to-order (ATO) inventory systems is a long-standing difficult problem. We consider ATO systems with identical component lead times and a general bill of materials. We use a related two-stage stochastic program (SP) to set a lower bound on the average inventory cost and develop inventory control policies for the dynamic ATO system using this SP. We apply the first-stage SP optimal solution to specify a base-stock replenishment policy, and the second-stage SP re...
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作者：Wang, Jianfu; Baron, Opher; Scheller-Wolf, Alan
作者单位：Nanyang Technological University; University of Toronto; Carnegie Mellon University
摘要：This paper provides the first exact analysis of a preemptive M/M/c queue with two priority classes having different service rates. To perform our analysis, we introduce a new technique to reduce the two-dimensionally infinite Markov chain (MC), representing the two class state space, into a one-dimensionally infinite MC, from which the generating function (GF) of the number of low-priority jobs can be derived in closed form. (The high-priority jobs form a simple M/M/c system and are thus easy ...
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作者：Cai, Ning; Song, Yingda; Kou, Steven
作者单位：Hong Kong University of Science & Technology; Chinese Academy of Sciences; University of Science & Technology of China, CAS; National University of Singapore; National University of Singapore
摘要：A general framework is proposed for pricing both continuously and discretely monitored Asian options under one-dimensional Markov processes. For each type (continuously monitored or discretely monitored), we derive the double transform of the Asian option price in terms of the unique bounded solution to a related functional equation. In the special case of continuous-time Markov chain (CTMC), the functional equation reduces to a linear system that can be solved analytically via matrix inversio...
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作者：Bensoussan, Alain; Guo, Pengfei
作者单位：University of Texas System; University of Texas Dallas; City University of Hong Kong; Hong Kong Polytechnic University
摘要：We study a periodic review inventory model with a nonperishable product over an infinite planning horizon. The demand for the nonperishable product arrives according to a Poisson process. Lost sales are unobservable but the stockout times are observable. We formulate the problem as a dynamic programming model with learning on arrival rate according to stockout times and further simplify it by using unnormalized probabilities. We then compare the system performance with those under other two in...
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作者：Ben-Tal, Aharon; Hazan, Elad; Koren, Tomer; Mannor, Shie
作者单位：Technion Israel Institute of Technology; Tilburg University; Princeton University; Technion Israel Institute of Technology
摘要：Robust optimization is a common optimization framework under uncertainty when problem parameters are unknown, but it is known that they belong to some given uncertainty set. In the robust optimization framework, a min-max problem is solved wherein a solution is evaluated according to its performance on the worst possible realization of the parameters. In many cases, a straightforward solution to a robust optimization problem of a certain type requires solving an optimization problem of a more ...
