Data Analytics Approaches To The Design Of Supply Chain Networks

Businesses today are operating in increasingly turbulent environments,with natural disasters and social disorders disrupting normal operations on a more frequent basis.Technological advancements such as mass customization manufacturing also stimulate more volatile demand.As a result,firms need new solutions for more reliable and flexible supply chain networks to cope with both availability uncertainties and demand uncertainties.Due to the rapid development of information technology,the data collected from the supply chain operations,with fast growth in both size and complexity,has provided new opportunities as well as challenges for this issue.This dissertation aims to develop new methodologies to extract information from data and provide effective solutions and managerial insights for the design of supply chain networks.We analyze real applications with different level of objectives that cover different sectors of supply chains,by using data with several degrees of information richness.This dissertation first considers an integrated location,assortment and inventory planning problem faced by an omni-channel retailer,whose offline operational decisions are based on big and high-dimensional transaction data as well as information of items and customers.We use the powerful Mixed Multinomial Logit(MMNL)choice model to capture customer preferences,and develop an integrated location,assortment and inventory optimization model under the MMNL choice model.We derive an efficient solution approach,further obtain solutions using real data,and investigate the implications for the operations in omni-channel retail.The second part of this dissertation studies the reliable hub location problem under random network disruptions.We use the historical data of network disruptions,and develop a reliable hub location model based on data-driven methods and combinatorial optimization.By exploiting structural properties of the model,we propose a mixed-integer linear programming reformulation as well as a constraint generation approach for exact solutions and several heuristics for approximate solutions.Furthermore,we numerically test the efficacy of the proposed model and solution approaches,and provide insights into the design of reliable hub-and-spoke networks.The last part of this dissertation focuses on the reliable flexibility design problem based on marginal or side information of supply and demand uncertainties.We propose a flexibility design model,aiming to determine sparse flexibility structures that satisfy the given service level under the worst-case distribution of supply and demand.We propose a novel concept of flexibility structures,i.e.,the extended probabilistic expander,provide an efficient algorithm for structure generation,and prove the asymptotically optimal sparsity of the proposed structure.Numerical results demonstrate that our design has not only a wide range of applications,but also better performance than a variety of well-known flexibility structures.