Optimizing Time Sensitive Supply Chain Networks: Restoration, Operations and Design
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Cohen, Yaarit Miriam
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Abstract
This dissertation uses optimization techniques to tackle and solve real-world problems, focusing on time sensitive restoration, operations, and design of supply chain networks.
In network restoration, we consider The Endogenous Network Connectivity Restoration Problem and aim to re-establish physical connectivity between nodes in the network by restoring the edges. In some cases, the progress of restoration activities is endogenous, i.e., depending not only on the structure of the network but also on previous decisions and actions. We model this problem as a Mixed Integer Program (MIP) and present a portfolio of optimal-solution-structure based heuristics. This problem has multiple applications, including in the humanitarian aspect of post-disaster debris clearance.
In supply chain network operations, we consider time sensitive supply networks in humanitarian supply chains. For The Post Disaster Kit Routing Problem, we aim to deliver kits from a regional warehouse to mobile storage units where the kits are distributed to the population affected by a disaster. Those kits are comprised of items required for the survival of the affected population and need to be delivered in a timely manner. We model this problem as a multi-objective MIP while optimizing various aspects of the post-disaster times such as effectiveness, fairness, autonomy, and cost.
In supply chain network design, we consider time sensitive supply for the for-profit supply chains. In Logistic Network Design for Fresh Flowers - a temperature-controlled network for a yearly supply of several types of fresh cut flowers from multiple supply locations, we aim to satisfy demand by different customer types, located in various demand locations. We propose an efficient and effective three-fold heuristic approach, aiming to replace current reliance on air transportation.
In order to illustrate the models' and algorithms' capabilities, each problem is associated with a comprehensive case study comparing results to other known algorithms or current practice solutions.
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Date
2022-12-02
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Text
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Dissertation