-
作者：Kaplan, Edward H.
作者单位：Yale University
摘要：This article presents the first models developed specifically for understanding the infiltration and interdiction of ongoing terror plots by undercover intelligence agents, and does so via novel application of ideas from queueing theory and Markov population processes. The resulting terror queue models predict the number of undetected terror threats in an area from agent activity/utilization data, and also estimate the rate with which such threats can be detected and interdicted. The models tr...
-
作者：Brown, David B.; Smith, James E.; Sun, Peng
作者单位：Duke University
摘要：We describe a general technique for determining upper bounds on maximal values (or lower bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the nonanticipativity constraints that require decisions to depend only on the information available at the time a decision is made and impose a penalty that punishes violations of nonanticipativity. In applications, the hope is that this relaxed version of the problem will be simpler to solve than the original dynamic prog...
-
作者：Denton, Brian T.; Miller, Andrew J.; Balasubramanian, Hari J.; Huschka, Todd R.
作者单位：North Carolina State University; Universite de Bordeaux; University of Massachusetts System; University of Massachusetts Amherst; Mayo Clinic
摘要：The allocation of surgeries to operating rooms (ORs) is a challenging combinatorial optimization problem. There is also significant uncertainty in the duration of surgical procedures, which further complicates assignment decisions. In this paper, we present stochastic optimization models for the assignment of surgeries to ORs on a given day of surgery. The objective includes a fixed cost of opening ORs and a variable cost of overtime relative to a fixed length-of-day. We describe two types of ...
-
作者：Gallego, Guillermo; Sahin, Oezge
作者单位：Columbia University; University of Michigan System; University of Michigan
摘要：We introduce and analyze an intertemporal choice model where customer valuations are uncertain and evolve over time. The model leads directly to the study of call options on capacity that are similar to partially refundable fares. We show that the capacity provider earns significantly higher revenues by selling real options on capacity than on low-to-high pricing. We also investigate the social implications and show that the use of options is both socially optimal and socially efficient.
-
作者：Chod, Jiri; Pyke, David; Rudi, Nils
作者单位：Boston College; University of San Diego; INSEAD Business School
摘要：We consider a manufacturer of mass-customized modular products who orders components under demand uncertainty, and sets prices, produces to order, and trades excess components in a secondary market after this uncertainty is resolved. The sequence of events reflects, in a parsimonious fashion, the considerable reduction in demand uncertainty between the procurement stage and the selling season, typical of industries with long supply lead times and short product life cycles. We prove that, in co...
-
作者：Dogru, Mustafa K.; Reiman, Martin I.; Wang, Qiong
作者单位：Alcatel-Lucent; Alcatel-Lucent
摘要：We consider assemble-to-order inventory systems with identical component lead times. We use a stochastic program (SP) to develop an inventory strategy that allows preferential component allocation for minimizing total inventory cost. We prove that the solution of a relaxation of this SP provides a lower bound on total inventory cost for all feasible policies. We demonstrate and test our approach on the W system, which involves three components used to produce two products. (There are two uniqu...
-
作者：Ghate, Archis; Sharma, Dushyant; Smith, Robert L.
作者单位：University of Washington; University of Washington Seattle; University of Michigan System; University of Michigan
摘要：We present a simplex-type algorithm-that is, an algorithm that moves from one extreme point of the infinite-dimensional feasible region to another, not necessarily adjacent, extreme point-for solving a class of linear programs with countably infinite variables and constraints. Each iteration of this method can be implemented in finite time, whereas the solution values converge to the optimal value as the number of iterations increases. This simplex-type algorithm moves to an adjacent extreme p...
-
作者：Cominetti, Roberto; Correa, Jose R.; Rothvoss, Thomas; San Martin, Jaime
作者单位：Universidad de Chile; Universidad de Chile; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Universidad de Chile
摘要：We analyze a short-term revenue optimization problem involving the targeting of customers for a promotion in which a finite number of perishable items are sold on a last-minute offer. The goal is to select the subset of customers to whom the offer will be made available in order to maximize the expected return. Each client replies with a certain probability and reports a specific value that might depend on the customer type, so that the selected subset has to balance the risk of not selling al...
-
作者：Pasupathy, Raghu
作者单位：Virginia Polytechnic Institute & State University
摘要：The stochastic root-finding problem is that of finding a zero of a vector-valued function known only through a stochastic simulation. The simulation-optimization problem is that of locating a real-valued function's minimum, again with only a stochastic simulation that generates function estimates. Retrospective approximation (RA) is a sample-path technique for solving such problems, where the solution to the underlying problem is approached via solutions to a sequence of approximate determinis...
-
作者：Goh, Joel; Sim, Melvyn
作者单位：National University of Singapore; National University of Singapore; National University of Singapore
摘要：In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible...
