Optimization of emergency services under decentralized capacity sources

Abstract

Emergency response systems are critical for saving lives, yet many struggle to maintain quick and reliable service. In High-Income Countries (HICs), rising demand and staff shortages put pressure on response times, while in Low- and Middle-Income Countries (LMICs), the lack of centralized coordination leads to significant delays. This research addresses these global challenges by exploring innovative capacity sources, such as the use of lay-person volunteers as community first responders (CFRs) and consolidation of independent ambulance providers through an emergency platform. These capacity sources operate independently from the emergency service and their location and availability is therefore unpredictable and uncontrollable.

This project investigates how to integrate and manage decentralized, unpredictable capacity sources. The existing methodology in literature on emergency systems cannot capture this level of capacity-side uncertainty and new models need to be developed. The project will address challenges related to the estimation of performance, providing incentives for commitment, optimization of dispatch decisions, and resource allocation. The research will use mathematical modelling, including Integer Linear Programming, Markov Decision Processes, and Game Theory, to quantify performance and optimize operational processes in the presence of uncertain capacity. Partnerships with emergency providers in Kenya, Indonesia, Sweden and the Netherlands will ensure the embedding of theoretical advancements in real-world settings. By combining academic innovation with practical relevance, this project creates a solutions for addressing pressing challenges in emergency care.

Faculty to contact: Dr. Pieter van den Berg

Keywords

Operations Management, Emergency Response, Public Sector, Platform Economy, Community First Responders.

Topic

Setting

Emergency response systems increasingly rely on independent, unpredictable capacity sources such as commercial ambulances operating on emergency platforms and Community First Responders (CFRs) who volunteer through smartphone applications. These responders are not scheduled, positioned, or controlled by the emergency service. Their location, availability, commitment, and capabilities fluctuate, creating a fundamentally different type of uncertainty than the demand-side uncertainty traditionally studied in emergency service research.

This shift creates major operational challenges. Emergency platforms in LMICs depend on commercial ambulances that strategically choose when and where to be available, driven by revenue considerations rather than system-wide performance. CFR systems in HICs rely on volunteers who decide whether to accept alerts based on personal convenience and perceived value, resulting in acceptance rates well below 50%. In both cases, emergency services cannot use demand-side pricing, cannot force responders to relocate, and therefore cannot guarantee coverage in low-demand areas.

Academic research has only begun to explore these issues. Work on CFRs focuses mainly on dispatching for cardiac arrest, while research on emergency platforms has only just started to appear. The ride-hailing literature offers useful analogies, but its core mechanisms—surge pricing, access limitations, selective service areas—are ethically unacceptable in emergency care. This leaves emergency services without the analytical tools needed to integrate decentralized capacity sources effectively.

The PhD project will address this gap by developing new operational models that capture how decentralized responders behave and how emergency services can influence and coordinate them. The project will combine game theory, information design, machine learning, and optimization, using real-world data from partners in Kenya, Indonesia, Sweden, and the Netherlands.

Research Problem

Emergency services lack the analytical and operational frameworks needed to integrate independent, unpredictable responders whose behavior cannot be directly controlled. This limits their ability to estimate performance, influence responder decisions, and dispatch heterogeneous responders effectively.

Central Research Question

How can emergency services most effectively integrate and manage independent, unpredictable capacity sources to improve response times and reliability?

Approach

Mathematical Modelling, Machine Learning, Simulation, Optimization, Game Theory.

Required profile

We seek a candidate who has:

• A master in operations research, operations management, business analytics, mathematics, industrial engineering, computer science or similar
• Programming skills
• Good communication skills in English

Required by ERIM

All application documents required by ERIM can be found here.

Applications will be evaluated as they come in.

Expected output

We expect you to, together with the supervisory team, conduct research that is publishable in top academic journals. See Erim journals list ejl for a list of Primary Star (P*), and Primary (P) journals.

Cooperation

This PhD position is part of the Vidi project awarded to dr. Pieter L. van den Berg. Besides this PhD project, the project will also fund a PostDoc position. During the PhD project, you are expected to work together with the PostDoc as well as the industry partners.

Societal relevance

To ensure the societal relevance of the project, you are expected to collaborate with industry partners in the emergency response sector. The envisioned supervisory team has a strong record on having practical impact with high quality research.

Scientific relevance

The Supply Chain Management group at RSM has a strong track record of publishing research in highly ranked management journals. In addition to undertaking research visits, PhD students are actively encouraged to participate in all major international conferences, and are supported in their travel to disseminate their research findings.

Literature references & data sources

See the following two papers for an impression of the type of research conducted in this project.

P.L. van den Berg, S.G. Henderson, C.J. Jagtenberg, M. Li. Modeling the Impact of Community First Responders. Management Science 71(2): 992-1008, 2025.

P.L. van den Berg, A. Calmon, A. Gernert, S. Lemmens, M. Rabinovich, G. Romero. The Value of Time- and Location-Commitment for Decentralized Emergency Medical Services. To appear in Manufacturing & Service Operations Management, 2026.

Employment conditions

ERIM offers fully-funded and salaried PhD positions, which means that accepted PhD candidates become employees (promovendi) of Erasmus University Rotterdam. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (CAO).

Erasmus University Rotterdam aspires to be an equitable and inclusive community. We nurture an open culture, where everyone is supported to fulfil their full potential. We see inclusivity of talent as the basis of our successes, and the diversity of perspectives and people as a highly valued outcome. EUR provides equal opportunities to all employees and applicants regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, functional impairment, citizenship, or any other aspect which makes them unique. We look forward to welcoming you to our community.

Contact information

For questions regarding the PhD application and selection procedure, please check the Admissions or contact the PhD Doctoral Office at phdadmissions@erim.eur.nl.