Operations Management Strategies for Complex Supply Chains and Urban Logistics

Abstract

The rapid evolution of supply chains and the growing complexity of urban logistics have made developing advanced operations management strategies necessary. The popularity of online shopping, new mobility services, and goods delivery has been steadily rising among consumers, particularly post-COVID-19. This stimulates the demand for omnichannel retailing and urban curb spaces for parking, pickup/drop-off, and loading/unloading. Consequently, both retailers and urban planners are exploring integrated approaches that simultaneously leverage rising online sales and efficiently manage curb space allocation.

However, prompt fulfillment, inventory balancing, and return management are challenging tasks in supply chain management that require coordination between online and offline channels to meet customer expectations and provide a seamless, consistent customer experience across all channels. In urban logistics, balancing the competing demands for various uses of curb spaces requires incorporating different peak times and usage patterns. The variability in curb space capacity and demand requires efficient management strategies to allocate curb space effectively in real-time. This dissertation addresses critical challenges in these domains by proposing integrated strategic frameworks to enhance operational performance and efficiency. By investigating optimal fulfillment and transshipment strategies in an omnichannel retailing context and developing a model for curb space capacity allocation, this research offers a comprehensive solution to the complex interplay between these two domains.

The first step is to enhance the performance and efficiency of the supply chain by investigating optimal fulfillment and transshipment strategies in an omnichannel retailing setting with customer cross-channel return behaviors. We built a dynamic programming model for an omnichannel retailer that operates one online platform and several physical stores. Our study provided insights into a retailer's dynamic fulfillment and transshipment decisions that maximize the retailing system's total profit under uncertainties in customer demand, shipping distances, transshipping costs, cross-channel return rates, etc. More specifically, we addressed the following decisions simultaneously: (i) Order fulfillment: where to fulfill an online order, and (ii) Inventory balancing: when and where to transship items to ensure optimal inventory levels across all locations. We further proposed efficient heuristics to help retailers navigate complex cross-channel fulfillment and transshipment decisions while providing robust performance under various retailing options and scenarios.

In the second step, we integrate the retailer’s strategic and tactical decision-making processes by developing a two-stage stochastic programming model. In this model, the retailer simultaneously makes decisions on inventory allocation, fulfillment, and transshipment. Unlike the first chapter, we focus on developing long-term policies by accounting for a broader range of uncertainties through multiple scenarios. Additionally, we evaluate the value of omnichannel operations by comparing our model with independent-channel operations and other commonly used benchmark policies.

Then, to optimize the performance of urban logistics, we build a capacity allocation model to study the different uses of curb space in the urban transportation system. We consider the interaction between curb space and traffic flow, which are the two essential elements of the transportation system that interact with each other and affect the overall system performance. We first build an open migration network to understand the flow of the vehicles by considering the various uses of the curb space (i.e., parking, pickup/drop-off, and loading/unloading). We then formulate the allocation of capacity needed for various uses with a news-vendor model where our objective is to maximize the profit of the cities. We derive optimal capacity allocation policies and implement numerical experiments. With the model developed, capacity allocation decisions for various curb uses can be made more systematically, improving the overall traffic system.

In conclusion, this dissertation presents a comprehensive set of operations management strategies designed to optimize the efficiency and performance of complex supply chains and urban logistics. The proposed methods have been validated through numerical experiments as both efficient and practical. The insights and techniques developed in this dissertation hold significant promise for a wide range of applications, positioning them as valuable tools for enhancing supply chain management and urban logistics planning.