Title:
Dynamic Prescriptive Analytics for Logistics Service Providers
Dynamic Prescriptive Analytics for Logistics Service Providers
Author(s)
Boerger, Jana
Advisor(s)
Montreuil, Benoit
Wang, He
Wang, He
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Abstract
With the strong growth in e-commerce and distribution volume comes a demand for more efficient logistics infrastructure to support the development. Thus, third-party logistics service providers (3PLs) are facing increased demands for their services. This thesis focuses on the application of analytics-based, data-driven approaches to improve 3PL’s service offerings.
In chapter 2, we introduce a capacity decision-making framework for 3PLs to dynamically manage its assets. We address the three layers of analytics: descriptive, predictive, and prescriptive analytics and show how a 3PL can implement these to transform into a proactive hyperconnected logistics player.
In chapter 3, we introduce the demand and capacity management problem that cold- chain logistics player face when operating temperature-controlled warehouses. At each time step, the 3PL decides whether to change the temperature of their storing rooms (capacity management) and whether to accept or reject an incoming customer request for temperature controlled space (demand management). We show that optimal solutions are not feasible for the problem size and introduce data-driven rollout-based algorithms that outperform greedy heuristics.
In chapter 4, we discuss the development of a simulation model that allows a deeper understanding of the trade-off between order consolidation and timely order fulfillment in multi-order picking systems in warehouses. We propose a domain informed, regret- based threshold policy that addresses the trade-off and compare its performance to greedy heuristics.
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Date Issued
2022-08-24
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Resource Type
Text
Resource Subtype
Dissertation