Optimal Acquisition Strategy Research Articles

Correction to “Optimal Data Acquisition Strategy for the Development of a Transport Model for Groundwater Remediation” by T. Tucciarelli and G. Pinder

Water Resources ResearchVolume 27, Issue 12 p. 3283-3283 CorrectionsFree Access Correction to “Optimal Data Acquisition Strategy for the Development of a Transport Model for Groundwater Remediation” by T. Tucciarelli and G. Pinder T. Tucciarelli, T. TucciarelliSearch for more papers by this authorG. Pinder, G. PinderSearch for more papers by this author T. Tucciarelli, T. TucciarelliSearch for more papers by this authorG. Pinder, G. PinderSearch for more papers by this author First published: December 1991 https://doi.org/10.1029/91WR02196Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume27, Issue12December 1991Pages 3283-3283 RelatedInformation

Optimal data acquisition strategy for the development of a transport model for groundwater remediation

The reliability of groundwater quality management algorithms is limited in large part by the uncertainty present in the model parameters. Because the field parameter measurement costs and the remediation costs must be supplied by the same financial source, the classical optimization procedure does not minimize the real total remediation investment. This research presents an algorithm able to find the total minimum for the sum of both the measurement and the pumping costs. A chance‐constrained technique is used to cast the optimization problem in stochastic form, relating the concentration covariance matrix to the log‐transmissivity covariance matrix by means of the transport equations and a first‐order approximation for the uncertainty. The simulation model solves the steady state flow equations on a finite element triangular mesh and the transport equations using the backward method of characteristics. The resulting nonlinearly constrained optimization problem is solved using the quasi‐linearity algorithm; this algorithm is designed to find a good initial point for the local minimum search when the feasible domain is not convex.

Optimal Acquisition of Automated Flexible Manufacturing Processes

We formulate the problem of converting a labor-intensive batch production process to one that incorporates flexible automation as a finite-state Markov decision process. Interest rates and the level of automated technology influence both operating and acquisition costs and are treated as random variables. The model specifies the optimal level of capacity to convert to flexible automation. The optimization criterion is the minimization of the sum of expected, discounted costs incurred over a finite planning horizon. The optimal acquisition strategy depends upon the time period, the current interest rate, the current level of technology, and a measure of the remaining capacity that is not automated. We investigate the structure of optimal acquisition strategies using mathematical analysis and simulation. Our objective is to illustrate the qualitative characteristics of optimal strategies for acquiring flexible automation. As a step toward the implementation of the model, we examine the qualitative consequences associated with specifying classes of inventory and acquisition cost functions.

Age differences in information acquisition strategies.

Age differences in information acquisition strategies were investigated by means of a visual 20-questions game. An optimal information acquisition strategy was made increasingly apparent across three experimental trials through strategy cues and demonstration. Three primary measures of information acquisition efficiency were defined and used to assess how closely the participants in three age groups (45-50 years, 60-65 years, 75-80 years) approximated ideal solution strategy. All measures indicate significant age group differences in the efficiency with which information was acquired, with the 45-50 year-old group scoring highest and the 75-80 year-old group scoring lowest. All age groups improved significantly across the three trials. The problem solving efficiency of the 60-65 year-old group was similar to that of the 45-50 year-old group, but the 75-80 year-old group scored significantly lower on performance measures than did the two younger groups combined. When a categorical search strategy was adopted, the oldest participants improved significantly; however, the age differences remained.

A Dynamic Programming Model of the U. S. Strategic Petroleum Reserve

This article develops a stochastic dynamic programming model which may be used to obtain optimal acquisition and sale strategies for the U.S. oil reserve. The model incorporates quota or tariff policies which may be used in conjunction with the stockpile policy. Although the main focus is on U.S. stockpile policy, a joint consumer country policy is also considered. The analysis indicates the importance of the degree of oil supply response in determining the effectiveness of a stockpile policy.