Demand Management for Attended Home Service Delivery


In many delivery and service operations, the service provider and customer must agree on a day or time window for the service. The time window appointments have direct impact on the efficiency and effectiveness of the service operations, but also on the attractiveness of the service to the customer. In this research, we will study how to increase sustainability and profitability of logistics services by managing and steering the demand. To find the right balance between, sustainability, effectiveness and attractiveness of the service, we consider two types of demand management approaches: (i) selectively determining the time window assortment to be displayed to customers (e.g. dependent on their region) and (ii) providing incentives (financial and non-financial) to dynamically steer demand. One example of a non-financial incentive is the use of green labels that show which logistics choices help the service provider reducing vehicles miles and thereby emissions and pollution.  We will conduct empirical research based on both lab and field experiments and develop optimization methods/machine learning approaches to support decision making in this context.


Transport and Logistics, Supply Chain Management, Operations Management, Marketing-Operations Interface


In many delivery and service operations, the service provider and customer must agree on a day or specific time window for the service. It is common for the service provider to let the customer choose from a menu of different service options.  The customers’ time window choices have a direct impact on the efficiency and sustainability of the delivery operations.  Moreover, the offered time slots (number of options, length, lead-time, prices) determine the attractiveness of the service to the customer. While less options and longer windows lead to more consolidated and efficient delivery operations, they are also less convenient for the customer. This means that the design of the time slot schedule must consider both customer behavioral and logistical planning considerations.

The primary goal of this project is to develop and evaluate different strategies to steer customer behavior in the context of attended delivery or service operations. 

The research has two components: (1) develop optimization-based approaches to best use different demand management tools in attended home delivery (2) conduct field or computational experiments to test the performance of different approaches. This gives rise to the following research questions:

  1. How do customers respond to different demand management approaches?
  2. How to accurately model and incorporate customer behavior in the design of the time slot assortments?
  3. How to build time slot schedules and corresponding delivery routes based on 1) and 2)?

The research contributes to several streams of research. It contributes to (1) the empirical research on customer behavior for logistical services and (2) the operations research literature on time slot management.   


This project is multi-disciplinary (marketing, operations, logistics) and uses different research methodologies. To study and model the behavior of customers, we will use a combination of field data analysis, lab and field experiments. To design operational planning approaches, we use and develop quantitative models based on operations research, econometrics, and machine learning.

Required profile

The candidates should have an interest in the challenges faced by online retailers and logistics service providers in the last mile. He or she should have a quantitative orientation towards problem solving. The candidate should also be open to collaborating with companies on-site and to collect data in the field.

  • Preferred background: MSc. in Industrial engineering, Supply Chain Management, Econometrics, Applied Mathematics, Operations Research, Computer Science or Business Analytics;
  • Excellent study record;
  • Programming experience;
  • International orientation and the capacity to speak and write in English fluently;
  • Commitment and drive to execute excellent PhD Research.

Note that the standard ERIM-requirements apply (GMAT/GRE/TOEFL).

Deadline: 1 September 2021

We encourage applicants to submit as early as possible as we will evaluate applications as they come in.

Expected output

  • The project aims to publish the results of the research in the top journals in operations management, transportation, and logistics.
  • In the project, we collaborate with a startup that offers attended home service. It aims to contribute to actual implementation of new algorithms and strategies in practice.
  • We will present the results at national and international conferences.

Scientific relevance

Most of the work in demand management for attended home delivery is quantitative and theoretical. There is very little empirical work that studies the impact of different incentives and service designs from a customer behavioral perspective.  Our research aims to contribute to this line of literature by providing empirical input.

This research project directly addresses current challenges that are encountered in business. The design and execution of efficient last-mile delivery operations is very challenging. This is especially the case in a dynamic environment with high growth rates. Improved decision making is necessary to improve performance and make the business economically, socially, and environmentally sustainable.

Literature references & data sources

Agatz, N., Campbell, A., Fleischmann, M., & Savelsbergh, M. (2011). Time slot management in attended home delivery. Transportation Science, 45(3), 435-449.

Agatz, N., Fan, Y., & Stam, D. (2020). The impact of green labels on time slot choice and operational sustainability. Production and Operations Management.

Bijmolt, Tammo H. A., Manda Broekhuis, Sander de Leeuw, Christian Hirche, Robert P. Rooderkerk, Rui Sousa, and Stuart Zhu (2021), “Challenges on the Marketing-Operations Interface in Omni-Channel Retail Environments,” Journal of Business Research, 122 (1), 864-874.

Ӧzarik, S.S., Veelenturf, L.P., Van Woensel, T., Laporte G. (2021). Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence. Transportation Research Part E: Logistics and Transportation Review, 148, 102263.

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).

Contact Information

For questions regarding the PhD application and selection procedure, please check the Admissions or send us an e-mail via

Erasmus Research Institute of Management

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