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Several studies show that logistics facilities have spread spatially from relatively concentrated clusters in the 1970s to geographically more decentralized patterns away from urban areas. The literature indicates that logistics costs are one of the major influences on changes in distribution structures, or locations and usage of logistics facilities. Quantitative modelling studies that aim to describe or predict these phenomena in relation to logistics costs are lacking, however. This is relevant to design more effective policies concerning spatial development, transport and infrastructure investments as well as for understanding environmental consequences of freight transport. The objective of this paper is to gain an understanding of the responsiveness of spatial logistics patterns to changes in these costs, using a quantitative model that links production and consumption points via distribution centers. The model is estimated to reproduce observed use of logistics facilities as well as related transport flows, for the case of the Netherlands. We apply the model to estimate the impacts of a number of scenarios on the spatial spreading of regional distribution activity, interregional vehicle movements and commodity flows. We estimate new cost elasticities, of the demand for trade and transport together, as well as specifically for the demand for the distribution facility services. The relatively low cost elasticity of transport services and high cost elasticity for the distribution services provide new insights for policy makers, relevant to understand the possible impacts of their policies on land use and freight flows.

The phenomenon of urban sprawl has been studied extensively. Most research so far has focused on residential settlements. A growing number of studies have addressed industrial deconcentration. Our focus in this paper is on logistics sprawl, i.e. the growth and suburban relocation of warehousing activities. Specifically, we investigate the difference in logistics sprawl between monocentric and polycentric systems of cities. The literature suggests that logistics activities, like residential settlements, will gradually move to suburbs as land prices increase in central areas. As research for logistics has mostly focused on monocentric systems, the question is whether this also applies to polycentric systems. We compare two cases, the Paris region in France, representative of a monocentric development, and the Dutch Randstad area as a polycentric case. We use regional statistics on warehouse settlements in both regions for a descriptive analysis of changes since the mid 2000s to derive metrics for concentration. The cases show different patterns of change in concentration. In contrast to Paris, logistics activities within the Randstad have intensified in dense areas. We explore the reasons that may explain this difference and conclude that urban structure, spatial planning policies and the freight hub quality of a region are factors of influence.

Due to the uncertain and dynamic environment around scheduling systems, timely revisions or reschedules of the master plans are essential for achieving optimal utilization. With the recent development of Industry 4.0 technologies, many researchers perceive the creation of cyber-physical systems as a solution for managing systems under uncertainty. This article focuses on a loading facility under uncertain truck arrivals due to road congestion and proposes utilizing real-time truck location information to improve performance. We do this by developing an integrated system consisting of a predictive model using machine learning (MC) classifiers and a mathematical model for real-time slot rescheduling. The ML classifier is used to predict the presence probabilities of all the incoming trucks at a particular slot based on the historical traffic data and the real-time truck location. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) model is developed to solve a Probabilistic Slot Rescheduling Problem (P-SRP), which uses the estimated truck presence probabilities and minimizes the total expected cost of rescheduling. We implemented this by first testing multiple ML classifiers and selected the ANN classifier for prediction as it outperformed others. Our limited experiments showed that the proposed method reduced the total rescheduling cost by 42%. Furthermore, our sensitivity analysis with different congestion levels, complexity, and rescheduling strategy also showed the practicality of the proposed approach.

Joint fact-finding has been advanced as a method for helping stakeholders grappling with technically intensive policy and planning challenges to collaboratively engage in research and arrive at shared sets of facts to inform their decision-making. This paper introduces joint factfinding and considers its application in the context of infrastructure stakeholders aiming to assess and increase the resilience of their infrastructure systems to climate change. A set of evaluative criteria is introduced, which are proposed for assessing joint fact finding processes both procedurally and substantively in terms of the outcomes, considering them to be both arenas for collaborative governance and joint knowledge production efforts. These criteria are applied to a case in Rotterdam, the Netherlands. This case suggests that joint fact-finding can provide value, but also reveals some lessons. For the efforts themselves, these lessons relate to: The practical applicability of the outcomes; the inherently contingent nature of the outcomes when addressing wicked problems; questions of representation from stakeholder groups; and the importance of leadership and good process design. The following observations are made regarding the criteria: While they are typically interdependent, both process and outcomes should be evaluated; and more attention should be paid to the method and metrics of evaluation, while recognizing that there is no single formula or approach that can be applied, given the heterogeneity of the criteria.

In passenger transport, hub-and-spoke networks allow the transportation of small passenger flows with competitive frequencies, in a way that direct line networks cannot. Equivalently, in freight transport, it can be expected that small shipper-receiver flows of high added value commodities transit through hub and spoke networks, while larger shipper-receiver flows of less expensive commodities are transported directly, without transhipment. The objective of this paper is first to present an analytical model, based on inventory theory, explaining how for a given commodity flow, the organisation of freight transport operations is closely related to the characteristics of the shipper-receiver relationship; second to assess empirically this model. Special emphasis is put on the number of transhipments in the transport operation, as an indication of organisation of the transport operations. The theoretical model is a simplified microeconomic model, built on principles of inventory theory. The empirical assessment of this model is based on the French shipment survey ECHO, which provides both the details of the transport operation and of the shipper-receiver relationships. With this database, we first provide a straightforward, graphical verification of the prediction of the theoretical model; secondly, we estimate it econometrically.

Long term projections of cargo throughput are indispensable for port development plans. Although commodity flow projections are useful for governments, port authorities, terminal operating companies and port users, scientific research on commodity flow projections for ports is limited. Existing studies generally use econometric models that assume stable relationships between growth of port throughput and such variables as GDP growth and trade growth. This paper presents a method that was developed as part of the Port Vision 2030 project of Port of Rotterdam Authority. The method combines a model with expert judgement and commodity specific research. This combination enables incorporating disruptions of past growth patterns. The contribution of this paper is the description of this method, its application to the volumes in 2030 of all major commodities handled in the Hamburg – Le Havre range, with four different scenarios, while most studies deal with a few commodities in one port, and often for a shorter period and with less scenarios. The results show that in all scenarios, total throughput is expected to rise, although in three scenarios not as fast as in the previous two decades. Furthermore, intermediates and container flows are expected to continue to grow, while throughput of raw materials (iron ore, crude oil) may decline.