AdvisorHigle, Julia L.
Committee ChairHigle, Julia L.
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractTechnological and industrial advances have resulted in the growth of large enterprises.Optimization models have been developed to increase the efficiency of partsof these systems, but models that optimize entire enterprises are frequently immenseand very complex to solve. Sequential solution techniques have resulted, which leadto useful, but not globally optimal, solutions. For example, airlines develop flightschedules based on strategic business objectives, and sequentially plan operationalprocesses to execute the schedule. Proven models that exist for the operationalsubproblems are solved sequentially, begin with a flight schedule, and allow limitedfeedback in the planning process. Since small changes to the individual parts haveproduced millions of dollars in improvement, an overall optimal solution could yielda significant increase in the airline's profit.We consider a modelling paradigm that moves toward integrated methods for theairline schedule planning phase using surrogate representations of the operationalproblems. In this context, surrogate models are relatively easy to solve, yet suffi-ciently representative of the operational problem to reflect its impact on schedulechoices. To illustrate, we develop surrogate models of maintenance scheduling, crewscheduling, and revenue generation. We solve the master schedule problem with eachsurrogate model using well-known decomposition techniques, and then combine thesurrogates into a single model that is readily decomposed into multiple subproblemsand solved.The model developments include additional considerations in constructing surrogatemodels. For example, to demonstrate validation of a surrogate's utility, wecompared the feasibility indications from the maintenance subproblem surrogate tothose from a larger, exact model of maintenance feasibility. The crew scheduling surrogatemodel development incorporates disruptions in the master schedule, drivingthe schedule to account for both crew costs and the impact of random disruptions.Finally, in the revenue management subproblem, we consider random demand thatimpacts a schedule's profitability.While surrogate solutions are inherently of little utility operationally, the resultsare useful for shaping the master schedule towards a global optimum. The paradigmallows for consideration of the subproblems in initial planning, so that solutionsobtained from the full models are based on a schedule that may lead to a betteroverall bottom line.
Degree ProgramSystems & Industrial Engineering