Sarah Schaumann, PhD
Postdoctoral Associate MIT Center for Transportation & Logistics
- Boston MA
Dr. Schaumann leverages AI, machine learning, and optimization methods to tackle real-world challenges in logistics.
MIT Center for Transportation & Logistics
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Media
Social
Biography
Research Focus
Intelligent & Autonomous Supply Chains
AI-driven, data-centric, and increasingly autonomous decision systems for supply chain planning and execution.
Supply Chain Strategy & Network Architecture
Designing the structural configuration and competitive positioning of supply networks.
Critical Systems & Supply Network Resilience
Ensuring continuity, preparedness, and mission assurance under disruption and geopolitical volatility.
Areas of Expertise
Education
ETH Zürich
PhD
Student and Research Associate
2024
Technische Universität Darmstadt
MSc
Industrial Engineering
2019
Technische Universität Darmstadt
BSc
Industrial Engineering
2016
Languages
- German
- English
Media Appearances
Interview with Sarah Schaumann, Lead Researcher at MIT CTL
Mecalux Group online
2025-02-11
Mecalux interviews Sarah Schaumann, Lead Researcher at MIT CTL, to learn more about the prescriptive intelligence project she leads as part of the MIT–Mecalux research collaboration.
Research Papers
The flying sidekick traveling salesman problem with multiple drops: An effective heuristic approach
Computers & Industrial EngineeringSarah K Schaumann, Abhishake Kundu, Juan C Pina-Pardo, Matthias Winkenbach, Ricardo A Gatica, Stephan M Wagner, Timothy I Matis
2025-09-16
We study the Flying Sidekick Traveling Salesman Problem with Multiple Drops (FSTSP-MD), a multi-modal last-mile delivery model where a single truck and a single drone cooperatively deliver customer packages. In the FSTSP-MD, the drone can be launched from the truck to deliver multiple packages before it returns to the truck for a new delivery operation. The objective is to find the synchronized truck and drone delivery routes that minimize the completion time of the delivery process. We develop an effective heuristic to solve the FSTSP-MD based on an order-first, split-second scheme. The core component of our heuristic is a novel split algorithm that finds FSTSP-MD solutions in polynomial time for a given sequence of customers.
Reconciling Rigor Versus Relevance: Lessons from Humanitarian Fleet Management
Production and Operations ManagementSarah K Schaumann, Bublu Thakur-Weigold, Luk N Van Wassenhove
2024-06-01
This position paper reframes the ongoing relevance versus rigor debate in operations research (OR) as a Kuhnian epistemological crisis, in which the dominant paradigm of quantitative modeling shows signs of exhaustion. Humanitarian fleet management is presented as an empirical case of extensive operations theory, which has not been implemented by the stakeholders who paid for its production. We propose a possible way out of the crisis by combining “hard” and “soft” OR, illustrating the potential with a selected problem structuring method. Optimization solutions can become more productive by first surfacing the organizational context of decision-making. The illustration emphasizes that hard and soft OR are not binary opposites but interlocking, mutually empowering components which expand the evidence base.
Route efficiency implications of time windows and vehicle capacities in first-and last-mile logistics
European Journal of Operational ResearchSarah K Schaumann, Felix M Bergmann, Stephan M Wagner, Matthias Winkenbach
2023-11-16
In this paper, we analyze the route efficiency effects that emerge from combining realistically constrained first-mile pickup and last-mile delivery operations into joint vehicle routes. Specifically, we examine (i) the individual effect of discrete, non-overlapping time window constraints; (ii) the individual effect of vehicle capacity constraints; and (iii) the joint impact of both constraint types on local route efficiency gains due to the integration of pickup and delivery operations. Extending the existent literature on continuum approximation of route distances, we propose closed-form adjustment factors which accurately capture these non-trivial route efficiency effects. To derive the adjustment factors, we conduct extensive numerical experiments and apply a novel hybrid data analysis approach which combines exploratory data analysis with symbolic regression.
