Latest revision as of 09:59, 16 February 2021

Abstract

The domain of urban freight transport is becoming increasingly complex. Many urban supply chains are composed of small and independent actors that cannot efficiently organize their highly fragmented supply chains, thereby negatively affecting the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper presents an agent-based simulation model to quantify the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation in the form of a Markov decision process. Based on an extensive literature study, we aggregate data to represent various actors in typical Western European cities. We perform numerical experiments to obtain insights into urban logistics schemes. The results show that most schemes yield significant environmental improvements but that achieving long-term financial viability is challenging for urban consolidation centers in particular. We also demonstrate that interventions, such as subsidies and access restrictions, do not always yield the intended effect. In a backcasting experiment, we identify conditions and schemes to achieve a financially viable urban consolidation center. 

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1287/trsc.2019.0971

• https://research.utwente.nl/en/publications/evaluating-urban-logistics-schemes-using-agentbased-simulation(b8781cdf-bfa2-4aa8-87f4-168fd39454ca).html

• https://pubsonline.informs.org/doi/10.1287/trsc.2019.0971,

https://research.utwente.nl/en/publications/evaluating-urban-logistics-schemes-using-agent-based-simulation,

https://doi.org/10.1287/trsc.2019.0971,

https://www.narcis.nl/publication/RecordID/oai%3Aris.utwente.nl%3Apublications%2Fb8781cdf-bfa2-4aa8-87f4-168fd39454ca,

https://academic.microsoft.com/#/detail/3001243833