{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2204"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2204","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Decision-making methods under Uncertainty in Discrete Multi-objective Optimization","abstract":"This dissertation investigates decision-making methods in Uncertain Discrete Multi-objective Optimization Problems (UDMOPs), where uncertainty arises in both objective function and constraint coefficients. The study pursues three main goals: (1) constructing sensitivity regions in the objective space to handle objective-wise uncertainty, (2) constructing sensitivity regions in the decision space to handle feasibility uncertainties, and (3) developing methods to sort, group, and prune uncertain solutions based on their similarity. Each goal proposes a method to explore uncertain solutions and quantify their level of uncertainty. Based on this, solutions are classified as low and high-risk solutions, according to the Decision-Maker (DM)'s preferences and risk tolerance. The proposed approaches employ stochastic optimization techniques to identify low and high-risk solutions, enabling risk-averse decision-making. Numerical experiments, including a real-world application, and benchmark comparisons, show that low or high-risk solutions under uncertainty can outperform the efficient solutions from deterministic model. Overall, the methods provide a more consistent and informative decision support system for DMs under uncertainty.","abstract_html":"This dissertation investigates decision-making methods in Uncertain Discrete Multi-objective Optimization Problems (UDMOPs), where uncertainty arises in both objective function and constraint coefficients. The study pursues three main goals: (1) constructing sensitivity regions in the objective space to handle objective-wise uncertainty, (2) constructing sensitivity regions in the decision space to handle feasibility uncertainties, and (3) developing methods to sort, group, and prune uncertain solutions based on their similarity. Each goal proposes a method to explore uncertain solutions and quantify their level of uncertainty. Based on this, solutions are classified as low and high-risk solutions, according to the Decision-Maker (DM)'s preferences and risk tolerance. The proposed approaches employ stochastic optimization techniques to identify low and high-risk solutions, enabling risk-averse decision-making. Numerical experiments, including a real-world application, and benchmark comparisons, show that low or high-risk solutions under uncertainty can outperform the efficient solutions from deterministic model. Overall, the methods provide a more consistent and informative decision support system for DMs under uncertainty.","abstract_has_math":false,"creators":["Aththanayake, Chathuri Malee"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Weerasena, Lakmali","Ebiefung, Aniekan; Bandara, Damitha; Ma, Ziwei","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-08-31T07:00:00Z","date_published":"2026-08-31T07:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["Decision making--Mathematical models","Sensitivity theory (Mathematics)","Stochastic programming","Uncertainty--Mathematical models"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1016","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Weerasena, Lakmali","Ebiefung, Aniekan; Bandara, Damitha; Ma, Ziwei","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Aththanayake, Chathuri Malee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-08-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-08-31T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral dissertations","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Decision making--Mathematical models","Sensitivity theory (Mathematics)","Stochastic programming","Uncertainty--Mathematical models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/1016"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Mathematics","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation investigates decision-making methods in Uncertain Discrete Multi-objective Optimization Problems (UDMOPs), where uncertainty arises in both objective function and constraint coefficients. The study pursues three main goals: (1) constructing sensitivity regions in the objective space to handle objective-wise uncertainty, (2) constructing sensitivity regions in the decision space to handle feasibility uncertainties, and (3) developing methods to sort, group, and prune uncertain solutions based on their similarity. Each goal proposes a method to explore uncertain solutions and quantify their level of uncertainty. Based on this, solutions are classified as low and high-risk solutions, according to the Decision-Maker (DM)'s preferences and risk tolerance. The proposed approaches employ stochastic optimization techniques to identify low and high-risk solutions, enabling risk-averse decision-making. Numerical experiments, including a real-world application, and benchmark comparisons, show that low or high-risk solutions under uncertainty can outperform the efficient solutions from deterministic model. Overall, the methods provide a more consistent and informative decision support system for DMs under uncertainty."]},{"key":"dc:title","label":"Title","values":["Decision-making methods under Uncertainty in Discrete Multi-objective Optimization"]}]}],"canonical_facts":{"dc:contributor":["Weerasena, Lakmali","Ebiefung, Aniekan; Bandara, Damitha; Ma, Ziwei","College of Engineering and Computer Science"],"dc:creator":["Aththanayake, Chathuri Malee"],"dc:date":["2025-08-01T07:00:00Z"],"dc:date.available":["2026-08-31T07:00:00Z"],"dc:description":["Dept. of Mathematics","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."],"dc:description.abstract":["This dissertation investigates decision-making methods in Uncertain Discrete Multi-objective Optimization Problems (UDMOPs), where uncertainty arises in both objective function and constraint coefficients. The study pursues three main goals: (1) constructing sensitivity regions in the objective space to handle objective-wise uncertainty, (2) constructing sensitivity regions in the decision space to handle feasibility uncertainties, and (3) developing methods to sort, group, and prune uncertain solutions based on their similarity. Each goal proposes a method to explore uncertain solutions and quantify their level of uncertainty. Based on this, solutions are classified as low and high-risk solutions, according to the Decision-Maker (DM)'s preferences and risk tolerance. The proposed approaches employ stochastic optimization techniques to identify low and high-risk solutions, enabling risk-averse decision-making. Numerical experiments, including a real-world application, and benchmark comparisons, show that low or high-risk solutions under uncertainty can outperform the efficient solutions from deterministic model. Overall, the methods provide a more consistent and informative decision support system for DMs under uncertainty."],"dc:identifier":["https://scholar.utc.edu/theses/1016"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Decision making--Mathematical models","Sensitivity theory (Mathematics)","Stochastic programming","Uncertainty--Mathematical models"],"dc:title":["Decision-making methods under Uncertainty in Discrete Multi-objective Optimization"],"dc:type":["Doctoral dissertations","Text"]},"updated_at":"2026-07-24T05:47:28Z"}