A simulation-based optimization approach for external trucks appointment scheduling in container terminals

ABSTRACT In Container Terminals (CTs), many containers are daily delivered by a large number of External Trucks (ETs) which lead to several issues such as long waiting times at gates and yards, harmful emissions, and low productivity of the CTs. To resolve these issues, truck appointment systems are used to schedule appointments of ETs and achieve more balance in CTs’ workload. Most of the studies have focused on deterministic modelling of the ETs’ arrival process with considering yard operations or gate operations. Also, a little effort has been devoted to collaboration between trucking companies and CTs when scheduling ETs’ appointments. Unlike previous studies, this paper presents a simulation-based optimization approach to collaboratively schedule ETs’ appointments with considering yard and gate operations as well as their stochastic natures. The proposed approach integrates a simulation model with an MIP model with objective of minimizing turnaround times of ETs and inconveniences resulting from shifting the arrivals of ETs away from their preferred arrival times. The proposed approach is validated against an approach from literature. In addition, its performance is investigated by solving artificial instances inspired by real data. A framework for implementing the proposed system in the IoT-based container terminals is also developed.

[1]  Anne Goodchild,et al.  Using the truck appointment system to improve yard efficiency in container terminals , 2013 .

[2]  Stefan Voß,et al.  Operations research at container terminals: a literature update , 2007, OR Spectr..

[3]  Gang Chen,et al.  Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal , 2016, Ann. Oper. Res..

[4]  Alan L. Erera,et al.  Planning local container drayage operations given a port access appointment system , 2008 .

[5]  Gang Chen,et al.  Optimizing time windows for managing export container arrivals at Chinese container terminals , 2010 .

[6]  Eduardo Lalla-Ruiz,et al.  port-IO: an integrative mobile cloud platform for real-time inter-terminal truck routing optimization , 2017, Flexible Services and Manufacturing Journal.

[7]  Stefan Voß,et al.  Digital transformation in maritime ports: analysis and a game theoretic framework , 2017 .

[8]  Anne Goodchild,et al.  The impact of truck arrival information on container terminal rehandling , 2010 .

[9]  Amr B. Eltawil,et al.  A Dynamic and Collaborative Truck Appointment Management System in Container Terminals , 2017, ICORES.

[10]  Qingcheng Zeng,et al.  Optimization Model for Truck Appointment in Container Terminals , 2013 .

[11]  Chenhao Zhou,et al.  A simulation-based vessel-truck coordination strategy for lighterage terminals , 2018, Transportation Research Part C: Emerging Technologies.

[12]  Kap Hwan Kim,et al.  Negotiating truck arrival times among trucking companies and a container terminal , 2015 .

[13]  Stefan Voß,et al.  Impact on yard efficiency of a truck appointment system for a port terminal , 2017, Ann. Oper. Res..

[14]  Marco Ferretti,et al.  Internet of Things and business processes redesign in seaports: The case of Hamburg , 2016, Bus. Process. Manag. J..

[15]  Gang Chen,et al.  Reducing Truck Emissions at Container Terminals in a Low Carbon Economy: Proposal of a Queueing-based Bi-Objective Model for Optimizing Truck Arrival Pattern , 2013 .

[16]  Frederik Schulte,et al.  Reducing Port-Related Truck Emissions: Coordinated Truck Appointments to Reduce Empty Truck Trips , 2015, ICCL.

[17]  Genevieve Giuliano,et al.  Reducing port-related truck emissions: The terminal gate appointment system at the Ports of Los Angeles and Long Beach , 2007 .

[18]  Katta G. Murty,et al.  Hongkong International Terminals Gains Elastic Capacity Using a Data-Intensive Decision-Support System , 2005, Interfaces.

[19]  Erfan Hassannayebi,et al.  A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem , 2014 .

[20]  George F. List,et al.  Using time-varying tolls to optimize truck arrivals at ports , 2011 .

[21]  Gang Chen,et al.  Terminal appointment system design by non-stationary M(t)/Ek/c(t) queueing model and genetic algorithm , 2013 .

[22]  Kap Hwan Kim,et al.  Collaborative truck scheduling and appointments for trucking companies and container terminals , 2016 .

[23]  Gang Chen,et al.  Managing truck arrivals with time windows to alleviate gate congestion at container terminals , 2013 .

[24]  Rongfang Liu,et al.  Modeling Gate Congestion of Marine Container Terminals, Truck Waiting Cost, and Optimization , 2009 .

[25]  Yun Peng,et al.  A Simulation-Based Dynamic Programming Method for Interchange Scheduling of Port Collecting and Distributing Network , 2018 .

[26]  Kannan Govindan,et al.  Disruption management for truck appointment system at a container terminal: A green initiative , 2016, Transportation Research Part D: Transport and Environment.

[27]  Amr B. Eltawil,et al.  A Simulation Based Study Of The Effect Of Truck Arrival Patterns On Truck Turn Time In Container Terminals , 2016, ECMS.

[28]  Amr B. Eltawil,et al.  Impact of Collaborative External Truck Scheduling on Yard Efficiency in Container Terminals , 2017, ICORES 2017.