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A Dynamic Multiple Stage, Multiple Objective Optimization Model With An Application To A Wastewater Treatment System

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A Dynamic Multiple Stage, Multiple Objective Optimization Model With An Application To A Wastewater Treatment System

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dc.contributor.author Tarun, Prashant en_US
dc.date.accessioned 2008-08-08T02:31:06Z
dc.date.available 2008-08-08T02:31:06Z
dc.date.issued 2008-08-08T02:31:06Z
dc.date.submitted April 2008 en_US
dc.identifier.other DISS-2000 en_US
dc.identifier.uri http://hdl.handle.net/10106/916
dc.description.abstract Decision-making for complex dynamic systems involves multiple objectives. Various methods balance the tradeoffs of multiple objectives, the most popular being weighted-sum and constraint-based methods. Under convexity assumptions an optimal solution to the constraint-based problem can also be obtained by solving the weighted-sum problems, and all Pareto optimal solutions can be obtained by systematically varying the weights or constraint limits. The challenge is to generate meaningful weights or constraint limits that yield practical solutions. In this dissertation, we utilize the Analytic Hierarchy Process (AHP) and develop a methodology to generate weight vectors successively for a dynamic multiple stage, multiple objective (MSMO) problem. Our methodology has three phases: (1) the input phase obtains judgments on pairs of objectives for the first stage and on dependencies from one stage to the next, (2) the matrix generation phase uses the input phase information to compute pairwise comparison matrices for subsequent stages, and (3) the weighting phase applies AHP concepts, with the necessary weight vectors obtained from expert opinions. We develop two new geometric-mean based methods for computing pairwise comparison matrices in the matrix generation phase. The weight ratios in the pairwise comparison matrices conform to the subjective ratio scale of AHP, and the geometric mean maintains this scale at each stage. Finally, for these two methods, we discuss the consistency of computed pairwise comparison matrices, note the convergence behavior, and apply our three-phase methodology to a problem of evaluating technological processes/units at each stage of an MSMO Wastewater Treatment System (WTS). The WTS is a 20-dimensional, continuous-state, 17-stage, 6-objective, stochastic problem. en_US
dc.description.sponsorship Chen, Victoria en_US
dc.language.iso EN en_US
dc.publisher Industrial & Manufacturing Engineering en_US
dc.title A Dynamic Multiple Stage, Multiple Objective Optimization Model With An Application To A Wastewater Treatment System en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Chen, Victoria en_US
dc.degree.department Industrial & Manufacturing Engineering en_US
dc.degree.discipline Industrial & Manufacturing Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=231
dc.identifier.externalLinkDescription Link to Research Profiles

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