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dc.contributor.advisorSabuncuoğlu, İhsan
dc.contributor.authorGören, Selçuk
dc.date.accessioned2016-01-08T18:18:16Z
dc.date.available2016-01-08T18:18:16Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11693/15418
dc.descriptionAnkara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 117-121.en_US
dc.description.abstractIn practice, scheduling systems are subject to considerable uncertainty in highly dynamic operating environments. The ability to cope with uncertainty in the scheduling process is becoming an increasingly important issue. In this thesis we take a proactive approach to generate robust and stable schedules for the environments with two sources of uncertainty: processing time variability and machine breakdowns. The information about the uncertainty is modeled using cumulative distribution functions and probability theory is utilized to derive inferences. We first focus on the single machine environment. We define two robustness (expected total flow time and expected total tardiness) and three stability (the sum of the squared and absolute differences of the job completion times and the sum of the variances of the realized completion times) measures. We identify special cases for which the measures can be optimized without much difficulty. We develop a dominance rule and two lower bounds for one of the robustness measures, which are employed in a branch-and-bound algorithm to solve the problem exactly. We also propose a beam-search heuristic to solve large problems for all five measures. We provide extensive discussion of our numerical results. Next, we study the problem of optimizing both robustness and stability simultaneously. We generate the set of all Pareto optimal points via -constraint method. We formulate the sub-problems required by the method and establish their computational complexity status. Two variants of the method that works with only a single type of sub-problem are also considered. A dominance rule and alternative ways to enforce the rule to strengthen one of these versions are discussed. The performance of the proposed technique is evaluated with an experimental study. An approach to limit the total number of generated points while keeping their spread uniform is also proposed. Finally, we consider the problem of generating stable schedules in a job shop environment with processing time variability and random machine breakdowns. The stability measure under consideration is the sum of the variances of the realized completion times. We show that the problem is not in the class NP. Hence, a surrogate stability measure is developed to manage the problem. This version of the problem is proven to be NP-hard even without machine breakdowns. Two branchand-bound algorithms are developed for this case. A beam-search and a tabu-search based two heuristic algorithms are developed to handle realistic size problems with machine breakdowns. The results of extensive computational experiments are also provided.en_US
dc.description.statementofresponsibilityGören, Selçuken_US
dc.format.extentxvi, 121 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSingle machine schedulingen_US
dc.subjectjob shop schedulingen_US
dc.subjectrobustnessen_US
dc.subjectstabilityen_US
dc.subjectproactive schedulingen_US
dc.subjectbranch-and-bounden_US
dc.subjectbeam searchen_US
dc.subjecttabu searchen_US
dc.subject-constraint methoden_US
dc.subject.lccTS157.5 .G67 2009en_US
dc.subject.lcshScheduling (Management)--Mathematical models.en_US
dc.subject.lcshProductions scheduling.en_US
dc.subject.lcshStability.en_US
dc.titleGenerating robust and stable machine schedules from a proactive standpointen_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


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