A cooperative quay crane-based stochastic model to estimate vessel handling time

Abstract

Having a good estimate of a vessel's handling time is essential for planning and scheduling container terminal resources, such as berth positions, quay cranes (QCs) and transport vehicles. However, estimating the expected vessel handling time is not straightforward, because it depends on vessel characteristics, resource allocation decisions, and uncertainties in terminal processes. To estimate the expected vessel handling time, we propose a two-level stochastic model. The higher level model consists of a continuous-time Markov chain (CTMC) that captures the effect of QC assignment and scheduling on vessel handling time. The lower level model is a multiclass closed queuing network that models the dynamic interactions among the terminal resources and provides an estimate of the transition rate input parameters to the higher level CTMC model. We estimate the expected vessel handling times for several container load and unload profiles and discuss the effect of terminal layout parameters and crane service time variabilities on vessel handling times. From numerical experiments, we find that by having QCs cooperate, the vessel handling times are reduced by up to 15 %. The vessel handling time is strongly dependent on the variation in the QC service time and on the vehicle travel path topology.

Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-Based Automated Container Terminal Under Demand Uncertainty

“…A cyclic operation was considered, in which the loading and unloading processes are sequentially performed [49,50]. An OS dispatching rule widely used in real container terminals was considered, where OSs dedicatedly initiate tasks according to the origin locations of tasks to avoid heavy interference.…”

Loading and unloading trains at the landside of container terminals

“…Stochastic models are becoming increasingly popular because they can efficiently take the complexity of container terminal operations into consideration. Recently, using a semi-open queuing network (SOQN), Roy and de Koster (2018), Roy et al (2020Roy et al ( , 2016, ), and Dhingra et al (2017 have developed separate integrated queuing models to analyze the design choices in seaside operations. The models are useful for analyzing the impact of vehicle dwell point, effect of stack configuration, and vehicle guide path design on throughput times.…”

Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems

“…In another study, Roy and De Koster (2014) develop semi-open queuing network (SOQN) models for a container terminal with AGVs and ALVs. Dhingra et al (2017) study the effect of cooperation between quay cranes on the container terminal performance using a closed queuing network. These studies assume sequential operations of AGVs/ALVs and quay/stack cranes.…”

“…To overcome this difficulty and to make the analysis simpler, existing studies typically use 'sequential' modeling approach, which assumes sequential operations of different resources that can work at least partly simultaneous. For example, Yang, Choi, and Ha (2004) , Roy and De Koster (2014) , Dhingra, Roy, and De Koster (2017) , Roy et al (2015) , and Gupta et al (2017) assume sequential operations of quay/stack cranes and AGVs while modeling the seaside operations at an automated container terminals with AGVs. They assume that the quay crane's container retrieval process can only start after an empty AGV arrives at the quayside.…”