The dissertation consists of three self-contained papers. In Chapter 1, we study the inventory management problem in a dual sourcing system, where there are two supply sources or modes with different sourcing costs and lead times. We provide closed-form solutions to a robust optimization model for inventory management in a dual sourcing system with general lead times. The fast source is more expensive than the slow source. While the optimal stochastic policy for non-consecutive lead times has been unknown for over 50 years, we prove that the optimal robust policy is a dual index, dual base- stock policy that constrains or “caps” the slow order. Optimality is established in a rolling horizon model that can accommodate non-stationary demand. As the lead time difference grows, the capped dual index policy increasingly smoothes slow orders and, for stationary demand, converges to the tailored base surge policy, which places a constant slow order and has been shown to be asymptotically optimal. In an extensive simulation study, the capped dual index policy performs as well as, and can even outperform, the best heuristics presented in the stochastic inventory literature. While Chapter 1 is mainly concerned with theoretical analysis of the dual sourcing problem, Chapter 2 investigates the practice of dual sourcing policies over product life cycles. Using actual order data from Dell Inc, we study the Days-Sales-of-Inventory (DSI) policy, which are widely deployed in industry, as well as other dual sourcing heuristics for non-stationary demands over a product life cycle. We find that the DSI policies naturally cap the slow orders, which is similar to the capped dual index policy. Our numerical study suggests that DSI policies could perform as well as other heuristic policies but have fewer policy controls. Such good performance is also robust to how the demand coefficient of variation evolves over the product life cycle if the policy controls of the DSI policies are set as newsvendor-type solutions. Our work contributes to the dual sourcing literature and the practice of DSI policies over a product life cycle, which is common in practice but yet under-studied in academia. In Chapter 3, we focus on warehouse operations, the fulfillment side of supply chain management, and study the design of an optimization algorithm to account for human behavior and make the algorithm augment human better in practice through the lens of the order packing process. Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box or bin) focus on bin volume utilization (efficiency) yet tend to overlook human behavioral deviations. We observed the order fulfillment operations at Cainiao, the logistics division of Alibaba which is the largest e-commerce platform in China. We found that workers deviated from the algorithmic prescriptions for more than 5.8% of packages, typically by switching to a larger bin than recommended. This switch increases packing time as well as material and environmental costs. We identify such behavioral deviations and propose a new algorithm that predicts discretionary behavior (e.g., switching to a larger bin) using machine learning techniques to pro-actively adjust the algorithmic prescription. We conducted a large-scale randomized field experiment with the Alibaba Group involving 757 workers and 782,360 packages from August 27, 2018 to September 9, 2018. During the experiment period, we randomly assign orders from Alibaba to either receive our “human-centric bin packing algorithm” (treatment group) or Alibaba’s original algorithm without behavioral adjustment (control group). Our field experiment results show that, by anticipating and incorporating human behavior, our new algorithm reduces the deviation probability of workers from 30.1% to 24.5% and improves their average packing time of targeted pack- ages (i.e., packages for which workers are more likely to deviate) by 4.5%. This idea of incorporating human deviation to improve optimization algorithms could also be generalized to other processes in logistics and operations.

Creator

• Sun, Jiankun

DOI

• https://doi.org/10.21985/n2-evzq-ys24

Subject

• Management
• Business administration
• Operations research

Language

• en

Alternate Identifier

• etdadmin_upload_663394
• http://dissertations.umi.com/northwestern:14688

Keyword

• Dual sourcing
• Behavioral operations
• Inventory management
• Robust optimization
• Field experiment
• Warehouse operations

Date created

• 2019-01-01

Resource type

• Dissertation

Rights statement

• In Copyright