A rapid increase in international trade volumes during recent years has led to truck congestion at container terminals’ gates during peak hours. This has consequences on the costs of trucking companies waiting long times behind the gates. Furthermore, it leads to containers arriving too late at the hinterland, a lower port attractiveness and high emissions of idling trucks. One possible policy to deal with this issue is to steer trucking companies towards night-time transport. However, this requires hubs with extended opening hours. The objective of this research is to investigate the implication of such policy further by evaluating and simulating the actors and their (inter)actions in the container transport system. This research uses a combination of qualitative (semi-structured interviews with actors) and quantitative (agent-based modelling, discrete event simulation, queueing theory and discrete choice modelling) methods to assess the impact of hubs with extended opening hours on freight transport under various scenarios. These scenarios compare possibilities for a chassis-exchange or a container hub and investigate the choice of trucking companies to use such hubs. Results determine that if a container hub would be introduced, other actors need to partly pay for trucking companies using the hub so that the hub capacity is fully used. This study reveals concrete recommendations on the type and location of the hubs to mitigate waiting times at terminals' gates and transport more containers per day. For example, a chassis-exchange hub can reduce the total waiting time at four container terminals by 420 hours per day. If trucking companies would invest in a chassis-exchange scenario, the profit is estimated to be around two to four thousand euros per day.

Making decisions regarding delayed line haul transport is a very demanding and complex process in E-commerce distribution centres. Automating this process can decrease decision-maker discussion and assessment time and, as a result, allow decision-makers to spend more time on other demanding tasks like sorting and distributing. Automation in this domain increases on-time deliveries and strengthens the E-commerce firms’ competitive position. However, such decisions involve experts’ knowledge, discussions among planners, and complex thought processes. Therefore, it is necessary to involve planners’ inclinations and preferences to automate their decisions. This paper proposes the Behavioural Artificial Intelligence Technology (BAIT) to automate expert decisions in delayed line haul deliveries. BAIT uses fundamental Discrete Choice theory under the hood to capture expert preferences effectively, and incorporates these into a decision-making tool. We use this method in a case study to replicate expert decisions in delayed line haul deliveries at DHL Express. The case study results show that BAIT can accurately replicate expert decisions.

This thesis proposes a truck arrival shift (TAS) policy to control truck arrivals at seaport terminals. The aim is to reduce congestion at terminal gates which is caused by a lack of port-hinterland alignment. We proposed, developed, and applied a modeling framework to assesses the impact of the TAS policy for the use case of the Port of Rotterdam. This policy is designed for the implementation of a Time Slot Management System (TSMS) and takes the behavioural aspect of Truck Operating Companies (TOC) into account. The time of day preferences of TOC for container pick-ups are inferred from the exchange of information between port and hinterland stakeholders using discrete choice modelling (DCM). These preferences are used to shift truck arrivals and consequently reduce the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TAS policy, we developed a simulation platform that resembles terminal operations using discrete-event simulation (DES). For the allocation of trucks to a certain time period, a choice-based heuristic is designed to approximate the optimum configuration of the TAS policy. The optimum TAS policy design shows that significant gain can be obtained at a low shift rate. Moreover, a measurable amount of waiting time gain can be achieved by the application of the designed TAS policy.