Planning hinterland container transportation in congested deep-sea terminals

The size of container ships and the number of containers being transshipped at container terminals have steadily increased over the years. Consequently, it is important to make efficient use of the hinterland capacity. A concept that is used to do this is synchromodal transportation, in which at the very last moment the mode of transportation for a container is decided. Unfortunately, some deep-sea terminals are rather congested and it is unknown by the time the transportation plan is made how many containers can be loaded to and unloaded from a barge. Motivated by this, we study an operational planning problem with uncertainty that is faced by an inland terminal in the port of Amsterdam as a two-stage stochastic problem with recourse. We solve this problem using sample average approximation (SAA) and a fast heuristic using constraints based on stochastic programming (SP). The SAA method gives near-optimal solutions for small instances. For larger instances, the SP-based method is shown to be a good alternative because it is much faster than the SAA method and produces solutions that are less than 1% from the SAA solutions.

[1]  Alexander Shapiro,et al.  The Sample Average Approximation Method for Stochastic Discrete Optimization , 2002, SIAM J. Optim..

[2]  Chung Yee Lee,et al.  The Value of Specific Cargo Information for Substitutable Modes of Inland Transport , 2016 .

[3]  Sandjai Bhulai,et al.  Optimizing barge utilization in hinterland container transportation , 2019 .

[4]  Adil Baykasoğlu,et al.  A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application , 2016 .

[5]  Robert Boute,et al.  Investigating synchromodality from a supply chain perspective , 2017, Transportation Research Part D: Transport and Environment.

[6]  Kris Braekers,et al.  Intermodal Container Routing: Integrating Long-Haul Routing and Local Drayage Decisions , 2019, Sustainability.

[7]  Rob A. Zuidwijk,et al.  The Value of Information in Container Transport , 2015, Transp. Sci..

[8]  David P. Morton,et al.  Monte Carlo bounding techniques for determining solution quality in stochastic programs , 1999, Oper. Res. Lett..

[9]  Alain Martel,et al.  The Stochastic Multiperiod Location Transportation Problem , 2010, Transp. Sci..

[10]  Maria-Eugenia Iacob,et al.  Synchromodal Transport Planning at a Logistics Service Provider , 2016 .

[11]  Loo Hay Lee,et al.  The sample average approximation method for empty container repositioning with uncertainties , 2012, Eur. J. Oper. Res..

[12]  Alexander Shapiro,et al.  The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study , 2003, Comput. Optim. Appl..

[13]  Martijn R. K. Mes,et al.  Service and Transfer Selection for Freights in a Synchromodal Network , 2016, ICCL.

[14]  Teodor Gabriel Crainic,et al.  A Revenue Management Approach for Network Capacity Allocation of an Intermodal Barge Transportation System , 2016, ICCL.

[15]  Alexander Shapiro,et al.  Lectures on Stochastic Programming: Modeling and Theory , 2009 .

[16]  Dong-Ping Song,et al.  Ocean container transport in global supply chains: Overview and research opportunities , 2017 .

[17]  J. F. Benders,et al.  A property of assignment type mixed integer linear programming problems , 1982 .

[18]  Zelda B. Zabinsky,et al.  Addressing capacity uncertainty in resource-constrained assignment problems , 2006, Comput. Oper. Res..

[19]  A. Shapiro,et al.  The Sample Average Approximation Method for Stochastic Programs with Integer Recourse , 2002 .

[20]  R. Pasupathy,et al.  A Guide to Sample Average Approximation , 2015 .

[21]  Rommert Dekker,et al.  Real-time container transport planning with decision trees based on offline obtained optimal solutions , 2014, Decis. Support Syst..

[22]  Martijn R.K. Mes,et al.  Anticipatory freight selection in intermodal long-haul round-trips , 2017 .