A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas

Liner shipping is a vital component of the world trade. Liner shipping companies usually operate fixed routes and announce their schedules. However, disruptions in sea and/or at ports affect the planned vessel schedules. Moreover, some liner shipping routes pass through the areas, designated by the International Maritime Organization (IMO) as emission control areas (ECAs). IMO imposes restrictions on the type of fuel that can be used by vessels within ECAs. The vessel schedule recovery problem becomes more complex when disruptions occur at such liner shipping routes, as liner shipping companies must comply with the IMO regulations. This study presents a novel mixed-integer nonlinear mathematical model for the green vessel schedule recovery problem, which considers two recovery strategies, including vessel sailing speed adjustment and port skipping. The objective aims to minimize the total profit loss, endured by a given liner shipping company due to disruptions in the planned operations. The nonlinear model is linearized and solved using CPLEX. A number of computational experiments are conducted for the liner shipping route, passing through ECAs. Important managerial insights reveal that the proposed methodology can assist liner shipping companies with efficient vessel schedule recovery, minimize the monetary losses due to disruptions in vessel schedules, and improve energy efficiency as well as environmental sustainability.

[1]  Dong Li,et al.  Multi-objective optimization for planning liner shipping service with uncertain port times , 2015 .

[2]  Maxim A. Dulebenets,et al.  The green vessel scheduling problem with transit time requirements in a liner shipping route with Emission Control Areas , 2017 .

[3]  Jomon Aliyas Paul,et al.  Modeling the effects of port disasters , 2010 .

[4]  Christos A. Kontovas,et al.  Ship speed optimization: Concepts, models and combined speed-routing scenarios , 2014 .

[5]  Reza Malekian,et al.  Estimation of Vessel Emissions Inventory in Qingdao Port Based on Big data Analysis , 2018, Symmetry.

[6]  Kjetil Fagerholt,et al.  Maritime routing and speed optimization with emission control areas , 2015 .

[7]  M. A. Dulebenets,et al.  The Vessel Scheduling Problem in a Liner Shipping Route with Heterogeneous Fleet , 2018 .

[8]  Maxim A. Dulebenets,et al.  A New Simulation Model for a Comprehensive Evaluation of Yard Truck Deployment Strategies at Marine Container Terminals , 2016 .

[9]  Ibrahim Y. Abualhaol,et al.  Modeling the speed-based vessel schedule recovery problem using evolutionary multiobjective optimization , 2018, Inf. Sci..

[10]  Christos A. Kontovas The Green Ship Routing and Scheduling Problem (GSRSP): A conceptual approach , 2014 .

[11]  Xiangtong Qi,et al.  Disruption Recovery for a Vessel in Liner Shipping , 2015, Transp. Sci..

[12]  Habin Lee,et al.  Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions , 2015 .

[13]  Xiangtong Qi,et al.  Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times , 2012 .

[14]  Qiang Meng,et al.  Sailing speed optimization for container ships in a liner shipping network , 2012 .

[15]  Maxim A. Dulebenets,et al.  Green vessel scheduling in liner shipping: Modeling carbon dioxide emission costs in sea and at ports of call , 2017 .

[16]  Qiang Meng,et al.  Robust schedule design for liner shipping services , 2012 .

[17]  T. Notteboom The Time Factor in Liner Shipping Services , 2006 .

[18]  Kwang-Il Kim,et al.  Dynamic Programming-Based Vessel Speed Adjustment for Energy Saving and Emission Reduction , 2018 .

[19]  Maxim A. Dulebenets,et al.  A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping , 2018 .

[20]  Eren Erman Ozguven,et al.  Vessel scheduling in liner shipping: Modeling transport of perishable assets , 2017 .

[21]  Shuaian Wang,et al.  Schedule design for sustainable container supply chain networks with port time windows , 2015, Adv. Eng. Informatics.

[22]  Xiaobo Qu,et al.  Estimation of the perceived value of transit time for containerized cargoes , 2015 .

[23]  Xiangtong Qi,et al.  Real-time schedule recovery in liner shipping service with regular uncertainties and disruption events , 2016 .

[24]  L. Lei,et al.  Container vessel scheduling with bi-directional flows , 2007, Oper. Res. Lett..

[25]  Kjetil Fagerholt,et al.  On two speed optimization problems for ships that sail in and out of emission control areas , 2015 .

[26]  Christos A. Kontovas,et al.  Speed models for energy-efficient maritime transportation: A taxonomy and survey , 2013 .

[27]  David Pisinger,et al.  The Vessel Schedule Recovery Problem (VSRP) - A MIP model for handling disruptions in liner shipping , 2013, Eur. J. Oper. Res..

[28]  Ching-Chih Chang,et al.  Evaluating the effects of speed reduce for shipping costs and CO2 emission , 2014 .

[29]  Qiang Meng,et al.  Liner ship route schedule design with sea contingency time and port time uncertainty , 2012 .

[30]  Henrik Andersson,et al.  Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions , 2014, Transp. Sci..

[31]  Maxim A. Dulebenets Application of Evolutionary Computation for Berth Scheduling at Marine Container Terminals: Parameter Tuning Versus Parameter Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[32]  Kjetil Fagerholt,et al.  Ship routing and scheduling in the new millennium , 2013, Eur. J. Oper. Res..

[33]  Shuaian Wang,et al.  Liner ship route schedule design with port time windows , 2014 .

[34]  Maxim A. Dulebenets Minimizing the Total Liner Shipping Route Service Costs via Application of an Efficient Collaborative Agreement , 2019, IEEE Transactions on Intelligent Transportation Systems.

[35]  D. Ronen,et al.  The effect of oil price on containership speed and fleet size , 2011, J. Oper. Res. Soc..

[36]  Mihalis M. Golias,et al.  The green vessel schedule design problem: consideration of emissions constraints , 2017 .

[37]  Zhiyuan Liu,et al.  Bunker consumption optimization methods in shipping: A critical review and extensions , 2013 .

[38]  Christos A. Frangopoulos,et al.  Intertemporal Static and Dynamic Optimization of Synthesis, Design, and Operation of Integrated Energy Systems of Ships , 2019, Energies.

[39]  Maxim A. Dulebenets,et al.  Advantages and disadvantages from enforcing emission restrictions within emission control areas , 2016 .

[40]  Helen B. Bendall,et al.  A Scheduling Model for a High Speed Containership Service: A Hub and Spoke Short-Sea Application , 2001 .

[41]  Emilio Esposito,et al.  Environmental Sustainability and Energy-Efficient Supply Chain Management: A Review of Research Trends and Proposed Guidelines , 2018 .

[42]  Kjetil Fagerholt,et al.  Ship Routing and Scheduling: Status and Perspectives , 2004, Transp. Sci..