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dc.contributor.advisorTrafalis, Theodore
dc.contributor.authorAlmaraj, Ismail
dc.date.accessioned2019-05-07T14:37:52Z
dc.date.available2019-05-07T14:37:52Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/11244/319573
dc.description.abstractModeling robust closed- loop supply chain under multiple uncertainties and multiple criteria where imperfect quality production is incorporated is a new research trend in this area. Such integration is essential as it provides meaningful solutions to the practical problems of supply chain management. In this dissertation, we develop three models. In the first model, we consider a novel closed loop supply chain design consisting of multiple periods and multiple echelons. In addition, we assume that the screening is not always perfect, and inspection errors are more likely to take place in practice. We measure the amount of quality loss as conforming products deviate from the specification (target) value. In this model, we develop three robust counterparts models based on box, polyhedral, and combined interval and polyhedral uncertainty sets. We utilize different a priori probability bounds to approximate probabilistic constraints and provide a safe solution. The objective is to minimize the total cost of the supply chain network. As an extension to the first model, the second model considers a robust multi-objective mixed integer linear programming model which includes three objectives simultaneously. The first objective function minimizes the total cost of the supply chain. The second objective function seeks to minimize the environmental influence, and the third objective function maximizes the social benefits. The augmented weighted Tchebycheff method is used to aggregate the three objectives into one objective function and produce the set of efficient solutions. Robust optimization, based on the extended Mulvey et al. (1995) approach, is used to obtain a set of solutions that are robust against the future fluctuation of parameters. In the third model, the affinely adjustable robust formulation based on "wait and see" decisions is presented. That is, the decisions are made over two sequential stages where multiple uncertainties are included. Moreover, we propose a budget dynamic uncertainty set to mimic the dynamic behavior of the market demand over time. The introduced dynamic uncertainty set is formulated according to Vector Autoregressive (VAR) models where the temporal and spatial correlations of customer demand zones are captured. Also, we utilize different a priori probability bounds to approximate probabilistic constraints and provide safe solutions. Finally, numerical examples have been presented to test and analyze the tradeoff between solution robustness and models robustness. The results reveal valuable managerial views. Our proposed models are compatible with several types of industries including steel making, electronic and automobile manufacturing, and various plastic products where return products (either defective or used) can be reused as a raw material, and when environmental and social issues become a company concern.en_US
dc.languageen_USen_US
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectOperations Researchen_US
dc.subjectRobust Optimizationen_US
dc.subjectProduction Systems and Supply Chain Managementen_US
dc.subjectOptimization under Uncertaintyen_US
dc.titleAn Integrated Multi-Echelon Multi-Objective Programming Robust Closed-Loop Supply Chain Under Dynamic Uncertainty Sets and Imperfect Quality Productionen_US
dc.contributor.committeeMemberRadhakrishnan, Sridhar
dc.contributor.committeeMemberRaman, Shivakumar
dc.contributor.committeeMemberBarker, Kash
dc.contributor.committeeMemberNicholson, Charles
dc.date.manuscript2019-05
dc.thesis.degreePh.D.en_US
ou.groupGallogly College of Engineering::School of Industrial and Systems Engineeringen_US
shareok.nativefileaccessrestricteden_US


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Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International