Data-driven reconfigurable supply chain design and inventory control

In this dissertation, we examine resource mobility in a supply chain that attempts to satisfy geographically distributed demand through resource sharing, where the resources can be inventory and manufacturing capacity. Our objective is to examine how resource mobility, coupled with data-driven analytics, can result in supply chains that without customer service level reduction blend the advantages of distributed production-inventory systems (e.g., fast fulfillment) and centralized systems (e.g., economies of scale, less total buffer inventory, and reduced capital expenditures). We present efficient and effective solution methods for logistics management of multi-location production-inventory systems with transportable production capacity. We present a novel, generalized representation of demand uncertainty and propose data-driven responses to the manage a single location inventory system under such demands.Ph.D

Reinforcement Learning approach of switching bi-stable oscillators to adapt bandgaps of 1D-meta-structures

Meta-structures with dynamic vibrational resonators (DVRs) are programmed to control the propagation of waves and attenuate vibrations over a broadband frequency spectrum. Attributes of DVRs, such as their resonant frequency and mass, determine the location and width of the bandgap, respectively. As a result, to adaptively program bandgaps, one has to modify or tune the eigenvalues of individual DVRs, and a popular approach is to vary the stiffness of each resonator. However, the tunable range of bandgaps is often restricted to maximum change in DVRs’ stiffness. This work presents a novel approach to adaptively program bandgaps of a 1D flexural meta-structure over a broad frequency bandwidth. DVRs with two stable configurations are attached to a beam in developing the meta-structure. A numerical model is developed to illustrate the scope of the novel approach. An experimental investigation then validates the simulated results and shows the extent of the vibration absorption capabilities of the meta-structure. A reinforced learning approach is used to adaptively tune the bandgap over 220 Hz to 840 Hz