Societal trends such as urbanization, desire for flexibility, and growing awareness about climate change and sustainability impose severe challenges for distribution logistics providers and require re-thinking and re-designing current distribution strategies. These challenges, however, have also led to a variety of new opportunities that are currently studied both in theory and practice, such as crowd-shipping. A main shortcoming is that recent (scientific) studies typically neglect or oversimplify the impact of non-corporate stakeholders (such as crowd-shippers and end-users) and their behavior. These stakeholders may act selfishly and behave either strategically, or their objectives and utilities may be in conflict with the operators’ goals of efficiency and sustainability.
This project aims to incorporate more realistic stakeholder behavior and (partially unknown) stakeholder preferences to model and solve more realistic (distribution) logistics problems. To this end, we design and implement models and algorithms based on mixed-integer (linear and non-linear) programming, and evaluate them via extensive computational studies.
