Jianbin Xin

Hybrid MPC for balancing throughput and energy consumption in an automated container terminal

The performance of container terminals needs to be improved to adapt to the growth of handled containers and maintain sustainability. This paper provides a methodology to balance the throughput and energy consumption of equipment inside a container terminal. The behaviors of different types of equipment in the container terminal are modeled as a collection of interconnected hybrid systems. Model predictive control is proposed for balancing the performance of the container terminal. A simulation study illustrates how setting a penalty on energy consumption influences the throughput of the container terminal on the one hand, and sustainability on the other hand.

Energy-efficient container handling using hybrid model predictive control

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

Hierarchical Control of Equipment in Automated Container Terminals

The performance of container terminals needs to be improved to adapt the growth of handled containers and maintain port sustainability. This paper provides a methodology to improve the throughput of the container terminal in an energy-efficient way. The behaviors of equipment are considered as consisting of a higher level and a lower level representing discrete-event dynamics and continuous-time dynamics, respectively. These dynamics need to be controlled. For controlling the higher level dynamics, a minimal makespan problem is solved. For this, the minimal time required for carrying out a task at the lower level is needed. The minimal time for carrying out a task at the lower level is obtained by Pontryagin’s Minimum Principle. The actual operation time, allowed by the higher level for completing a task by one piece of equipment at the lower level, is determined by the scheduling algorithm at the higher level. The lower level dynamics are controlled using optimal control to achieve the minimal energy consumption when the operation time allowed is given. A simulation illustrates how energy-efficient management of equipment for the minimal makespan could be obtained by the proposed methodology.

Hybrid model predictive control for equipment in an automated container terminal

Over the last decades, there has been a significant growth of global freight transport due to the enormous commercial trade. Over 60% of worldwide deep-sea cargo is transported by containers. The increased amount of containers that arrive and depart with container ships provides much pressure for terminal operators. The throughput, i.e., the number of containers handled per hour, should be increased. A container terminal is characterized by a large number of pieces of equipment that operate in a dynamically changing environment. The transport of a container depends on the actions of multiple pieces of equipment that are physically spread all over the container terminal. We are investigating how to effectively manage the volume growth by considering a more integrated way of looking at transport of freight. In particular in this paper, we propose to use the hybrid automaton modeling framework for modeling the handling of containers. Model predictive control is proposed for achieving the desired performance.

Rescheduling of Interacting Machines in Automated Container Terminals

Abstract The current scheduling scheme of container terminals is typically determined offline. This may result in delays of the complete operation, when disturbances (e.g., the operation delays or the breakdown of a machine) appear. This paper provides a method for rescheduling interacting machines in automated container terminals. The rescheduling is carried out using the current state measurements of the machines. These measurements are used to update the processing time of ongoing operations. The effect of rescheduling on both a time-efficient schedule and for an energy-efficient schedule is illustrated in a simulation study. The delay in the container handling system is reduced both for the time-efficient schedule and the energy-efficient schedule. A simulation study illustrates that the energy-efficient schedule is more sensitive to disturbances due to delays of machines than the time-efficient schedule.

Optimizing hybrid operations at large-scale automated container terminals

This work tackles the problem of controlling operations at an automated container terminal. In the context of large supply chains, there is a growing trend for increasing productivity and economic efficiency. New models and algorithms are provided for scheduling and routing equipment that is moving containers in a quay area, loading/unloading ships, transporting them via Automated Guided Vehicles (AGVs) to Automated Stacking Cranes (ASCs), organizing them in stacks. In contrast with the majority of the approaches in the related literature, this work tackles two dynamics of the system, a discrete dynamic, characteristic of the maximization of operations efficiency, by assigning the best AGV and operation time to a set of containers, and a continuous dynamic of the AGV that moves in a geographically limited area. As an assumption, AGVs can follow free range trajectories that minimize the error of the target time and increase the responsiveness of the system. A novel solution framework is proposed and some computational result are reported to show the feasibility of the proposed approach.

Trajectory planning for AGVs in automated container terminals using avoidance constraints: a case study

Abstract Automated guided vehicles (AGVs) are used to transport containers between the quayside and the stacking area in automated container terminals. The behavior of AGVs becomes complex when the trajectories of AGVs need to be scheduled with interacting machines, while satisfying collision avoidance constraints. This paper proposes a new two-level energy-aware approach for generating the trajectories of AGVs in automated container terminals. The higher-level controller decides an energy-efficient schedule based on the minimal-time calculation of all machines. The higher-level controller solves optimal control problems to determine collision-free trajectories of individual AGVs. This obtained control problem of an AGV is then formulated as a mixed integer linear programming problem. Simulation results illustrate the potential of the proposed approach in a case study.