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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorSharkey, Thomas C.
dc.contributorWallace, William A., 1935-
dc.contributorPazour, Jennifer A.
dc.contributorHekimoglu, Hakan
dc.contributor.authorNi, Ni
dc.date.accessioned2021-11-03T09:14:34Z
dc.date.available2021-11-03T09:14:34Z
dc.date.created2020-06-12T12:32:24Z
dc.date.issued2019-08
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2472
dc.descriptionAugust 2019
dc.descriptionSchool of Engineering
dc.description.abstractWhen a disruption forces the terminals (e.g., airports, sea ports, railway stations) in multimodal freight transportation system to close or operate under limited capacity in a specific area, local suppliers who rely on these terminals to receive and send freight have to reroute the freight movement and reschedule manufacturing process, facing risks of failure to meet demand and soaring shipping cost during the restoration periods of terminals. In terms of the freight flowing through terminals, we consider the flows both entering terminals (e.g., flow of raw materials) and leaving terminals (e.g., flow of finished goods) and examine the interdependency between these bidirectional commodity flows. We build multi-period models to find an optimal combination of shipping modes over various transportation means for suppliers to deliver freight with an objective to maximize the flow of finished goods and adhering to a budget on overall shipping cost. We apply the model to Puerto Rico after Hurricane Irma and Maria and analyze the impact of disruptions to local airports, sea ports and highway system on the pharmaceutical manufacturers in Puerto Rico.
dc.description.abstractAs the world becomes increasingly turbulent and interlinked, supply chains are vulnerable to disruptions during extreme events such as hurricanes or earthquakes. It is increasingly important to measure the capability of supply chains to mitigate and recover from disruptions on different segments of supply chains. As a result, challenges for designing and operating resilient supply chains include the impact of disruptions on the production segment of supply chains, for example, damage to the facilities or suppliers of the system, those on the logistics segment, for example, damage to the terminals of multimodal freight transportation system, and those on the distribution segment, for example, damage to the retailers and `last mile' distribution of commodities within a certain area under disrupted local infrastructures (e.g., power, water, sewage, telecommunication and transportation systems). This dissertation focuses on three topics relating to this challenge: (1) designing resilient supply chains against disruptions on the production segment while modeling the impact of unmet demand on its customers, (2) examining the impact of disruptions on the logistics segment and modeling the rerouting of freight movement, and (3) modeling the impact of disruptions on the distribution segment in terms of the distribution of critical commercial services within a specific area where civil infrastructures have been damaged by extreme events.
dc.description.abstractWhen an extreme event occurs in a specific area, damage to the distribution segment as well as the cascading disruptions from damaged local infrastructures within the area will interrupt the distribution of critical commercial services (e.g., cash, fuel, basic food, pharmaceuticals) from retailers to customers. The recovery of supply chains is highly dependent on the restoration of civil infrastructures due to the interdependencies between these two sets of networks. Single-period, multi-commodity disruption models are built to examine the interdependencies and predict the outages in infrastructures and critical commercial services based on damage to infrastructures. Multi-period, multi-commodity restoration models are built to select and schedule the infrastructure restoration tasks after disruptive events with an objective to maximize the aggregated flows of utilities and commodities. We simulate Category 2, 3 and 4 hurricanes and apply the models to an artificial county with a population of a half million people. We especially look into the routing problem of distribution segment delivering basic food and pharmaceuticals within the county under disrupted transportation systems. We analyze the contribution of local businesses to community resilience and how the inventory placement of local businesses impact the post-event performance of the critical commercial services within the county. Insights from the disruption model include that the damage to infrastructures may not necessarily lead to outages due to redundancy of resources in the county. Computational tests of the restoration model show that coordinated infrastructure restoration decisions with critical commercial services into consideration are important in increasing community resilience and help reduce the unmet demand of critical commercial services in a more efficient manner.
dc.description.abstractAfter the disruption on the production segment that causes diminished supply capacities, the supply chain faces the risk of losing customers due to unmet demand. Pre-event mitigation strategies will prepare the supply chain for various potential disruptions and then post-event restoration decisions are the response of it to a specific disruption. We build two-stage stochastic programming models to optimize the selection among assorted pre-event mitigation and post-event restoration strategies with an objective to minimize the cost from disruptions, including the cost of customer loss. We consider customer behaviors which require fulfillment of demand at a certain time after the disruption and those which require that a cumulative percentage of their demand must be met on-time after the disruption or, otherwise, the customer will leave the system. The insights from computational tests include observing that a `chain reaction' occurs after a disruption, which means that the supply chain system tends to lose the same subset of customers even when they are not served by the facilities impacted by disruption. Furthermore, different sets of post-event customer behaviors tend to favor the same pre-event mitigation strategies.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectDecision sciences and engineering systems
dc.titleModeling the impact of large-scale disruptions on supply chain networks and their recovery
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179886
dc.digitool.pid179887
dc.digitool.pid179888
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Industrial and Systems Engineering


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