Optimisation at Maritime Ports

Large Scale Truck Scheduling at Ningbo Port, China

This page provides resources from the research at University of Nottingham addressing the scheduling of large scale truck scheduling and crane dispatching problems at Ningbo Port, China, the busiest maritime port in the world in terms of cargo tonnage, handling 888.96 million tons cargoes in 2015.

Research sponsored by:

  • National Natural Science Foundation of China (NSFC Grant No. 71471092)
  • Royal Society International Exchange scheme
  • Zhejiang Natural Science Foundation (Grant No. LR17G010001)
  • School of Computer Science, University of Nottingham

Truck Scheduling between Ports

A Mixed-Shift Vehicle Routing problem model is built with a long planning horizon of multiple shifts of short shit servicing short-distance ports, and long shift for inland long-distance dry ports. Transport tasks must be completed satisfying the time constraints. The unit driver cost for long shifts is higher than that of short shifts.

The scheduling of trucks aims to minimise the cost of drivers, travel distance, satisfying the time window constraints.

Truck Dispatching in Container Terminal

A dynamic truck dispatching problem at the Ningbo container terminal, where containers need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations.

The aim is minimise any unnecessary idle time between quay crane operations and truck traveling time to speed up the container transfer process. The unpredictable port status can affect routing plans which need to be generated within a short calculation time.

Datasets

  • A set of Vehicle Routing dataset with Mixed Types of Shifts, derived from a large scale fleet transportation problem at Ningbo Port, China. See [2] in Publications.
  • A set of Vehicle Routing dataset with different constraints and features, derived from a large scale fleet transportation problem at Ningbo Port, China. See [1, 3, 4] in Publications.

The Mixed-Shift VRP model

Publications

  1. B. Chen, R. Qu, R. Bai, W. Laesanklang. A Reinforcement Learning Based Variable Neighborhood Search Algorithm for Open Periodic Vehicle Routing Problem with Time Windows. under review, 2018.
  2. B. Chen, R. Qu, R. Bai, W. Laesanklang, A Hyper-heuristic with Guidance Indicators for Bi-objective Mixed-Shift Vehicle Routing Problem with Time Windows, accepted by Applied Intelligence, 2018
  3. B Chen, R Qu and H. Ishibuchi, Variable Depth Aadaptive Large Neighborhood Search Algorithm for Open Periodic Vehicle Routing Problem with Time Windows, The 19th International Conference on Harbor, Maritime & Multimodal Logistics Modelling and Simulation (HMS'2017), 18-20 September 2017
  4. J Chen, R Bai, H Dong, R Qu and G Kendall, A Dynamic Truck Dispatching Problem in Marine Container Terminal, The 2016 Symposium on Computational Intelligence in Scheduling and Network Design (IEEE CISND'16), December 6-9, 2016
  5. B. Chen, R. Qu, R. Bai and H. Ishibuchi, A Variable Neighbourhood Search Algorithm with Compound Neighbourhoods for VRPTW, The 2016 International Conference on Operations Research and Enterprise Systems (ICORES'16), 23-25 Feb 2016
  6. J. Chen, R. Bai, R. Qu, G. Kendall, A Task Based Approach for A Real-World Commodity Routing Problem, 2013 IEEE Symposium Series on Computational Intelligence (SCCI 2013), 16-19 April, Singapore
  7. R. Bai, G. Kendall, R. Qu, J. Atkin. Tabu assisted guided local search approaches for freight service network design. Information Sciences, 189: 266-281, 2012.

Other Resources

Last updated: 20 September 2018. Maintained by Rong Qu, School of Computer Science, University of Nottingham, UK