Mixing it Up:Simulation of Mixed Traffic Container Terminals

The development from a completely manual brownfield terminal towards a highly automated one proceeds in a number of steps. To take the first steps, it is crucial to know how introducing Automated Yard Tractors (AYTs) influences port productivity at Mixed-Traffic Terminals (MTTs). In these terminals, (non-automated) road trucks and yard tractors share the same infrastructure. This paper employs discrete-event simulation in order to analyze the performance of a brownfield terminal where manual yard tractors are replaced by AYTs. The results look promising: high utilization rates are reached in MTTs, with a modest number of AYTs. In fact, these utilization rates are reached with the same number of yard tractors as are typically used in comparable terminals with manually operated vehicles. Special attention is paid to the influence of road trucks on terminal congestion during peak hours.</p

An agent-based simulation model for autonomous trailer docking

This paper presents a simulation model of a generic automated planning and control system for the pick-up and docking of semi-trailers by means of autonomous Yard Tractors (YTs) in a collision- and conflict free environment. To support the planning and control of the YTs, we propose a Multi-Agent System (MAS). We illustrate our approach using a case study at a Dutch logistics service provider. To evaluate the proposed system, we design an agent-based simulation model, which is set up in a similar way as the MAS. We conclude with the verification and validation of the simulation model

Towards self-organizing logistics in transportation:a literature review and typology

Deploying self-organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision-making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision-making, require less computational effort, and are therefore faster, reducing the risk that changes during decision-making render the solution invalid. These benefits of self-organizing systems are of interest to many practitioners involved in solving real-world problems in the logistics sector. This study, therefore, identifies and classifies research related to self-organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent-based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self-organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two-fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two-dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.</p

An agent-based simulation model for truck platoon matching

This paper presents a flexible agent-based simulation (ABS) model to serve as a matchmaking system for trucks such that they can form a platoon. A platoon is a formation of trucks which drive closely behind each other in a (semi)autonomous fashion. We focus on an agent-based system which seeks platoon candidates in the proximity of a truck and tries to find a match based on several properties of the trucks. The ABS does so in a dynamic fashion based on real-time (geo)data. Opposed to centralized systems, the matchmaking is done locally among trucks(agents). Due to the spontaneous nature of this matching type, we denote this type of platoon matching as real-time matching. We illustrate the ABS using a case study at a truck stop near the Port of Rotterdam, The Netherlands. The ABS model serves as a testbed for matching algorithms and to study enabling factors for truck platooning