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A Multi-Objective Stochastic Programming Model For Resilient And Sustainable Supply Chains Under Uncertainty, Mohammad H. Majdfaghihi
A Multi-Objective Stochastic Programming Model For Resilient And Sustainable Supply Chains Under Uncertainty, Mohammad H. Majdfaghihi
Dissertations
Supply chain resilience (SCRES) and sustainable supply chain management (SSCM) have garnered significant attention in the field of Supply Chain Management (SCM) science. This heightened interest is largely attributable to the increasing frequency and severity of uncertainty and disruptions in global supply chains. Resilience is a beneficial approach to managing uncertainties and preparing for and mitigating the adverse consequences of Supply Chain (SC) disruptions. Sustainability is a key concept in SCM, driven by governmental regulations and heightened stakeholder and customer concerns for environmental and societal well-being. Studies on SCRES have traditionally focused on preparing for and reacting to disruptions and …
Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj
Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj
Dissertations
This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using …