Solving steel coil ship stowage-planning problem using hybrid differential evolution

As an important optimisation problem in the finished product terminal of an iron and steel enterprise, the steel coil ship stowage-planning problem is to determine the stowing locations for the planned coils on a ship. Although the problem has attracted attention, the research has focused only on the optimisation for the ship. In this study, the problem is investigated from the view of improving operation efficiency of the cranes on the quay and in the warehouse. For this purpose, an integer-programming model is established to minimise the coil dispersion on the ship and the moving distance of the warehouse cranes by determining the stowing locations and loading sequence of the coils. To improve the solution efficiency, a two-level hybrid differential evolution (TLDE) composed of a continuous DE and a discrete DE is designed to assign the coils to the rows on the ship, and then allocate locations for them. Further, a subpopulation-based local search and a human experience-based heuristic are developed to further adjust the coils within each row and to produce initial population for TLDE, respectively. Extensive comparison experiments are performed to demonstrate the proposed algorithm. Numerical results confirm that TLDE is an efficient method for solving the SSPP.

[1]  Lixin Tang,et al.  An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production , 2014, IEEE Transactions on Evolutionary Computation.

[2]  Xianpeng Wang,et al.  An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization , 2016, Inf. Sci..

[3]  Dexian Huang,et al.  An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers , 2009 .

[4]  Min Huang,et al.  A critical chain project scheduling method based on a differential evolution algorithm , 2014 .

[5]  Qiang Zhou,et al.  Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation , 2016 .

[6]  Qingxin Guo,et al.  A pointer-based discrete differential evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[7]  Fei Yang,et al.  A scatter search optimization for ship consolidation plan for strip coils to reduce the number of shuffling , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[8]  José Fernando Álvarez A heuristic for Vessel planning in a reach stacker terminal , 2006 .

[9]  Chung-Yee Lee,et al.  Multiobjective Approaches for the Ship Stowage Planning Problem Considering Ship Stability and Container Rehandles , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Lixin Tang,et al.  Differential Evolution With an Individual-Dependent Mechanism , 2015, IEEE Transactions on Evolutionary Computation.

[11]  Ponnuthurai N. Suganthan,et al.  A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems , 2010, Comput. Oper. Res..

[12]  Elías Revestido Herrero,et al.  Experimentation environment for marine vehicles , 2006 .

[13]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[14]  Ding Ding,et al.  Stowage planning for container ships: A heuristic algorithm to reduce the number of shifts , 2015, Eur. J. Oper. Res..

[15]  Ning Wang,et al.  A novel hybrid differential evolution approach to scheduling of large-scale zero-wait batch processes with setup times , 2012, Comput. Chem. Eng..

[16]  Akira Kitamura,et al.  Optimiation search algorithm of allocation planning for strip coils in hold for shipment by using operational know-how , 2001 .

[17]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[18]  Ling Wang,et al.  An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers , 2008, Comput. Oper. Res..

[19]  Rui Xu,et al.  A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking , 2014 .

[20]  Anna Sciomachen,et al.  Stowing a containership: the master bay plan problem , 2004 .

[21]  Akio Imai,et al.  Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks , 2006, Eur. J. Oper. Res..

[22]  Kun Li,et al.  Integrated Optimization of Finished Product Logistics in Iron and Steel Industry Using a Multi-objective Variable Neighborhood Search , 2015 .

[23]  Guoqiang Wang,et al.  Apply differential evolution with individual-dependent mechanism to solve the ship stowage planning problem of coils in the steel industry , 2016, 2016 IEEE Forum on Integrated and Sustainable Transportation Systems (FISTS).

[24]  Erhan Kozan,et al.  Optimised loading patterns for intermodal trains , 2008, OR Spectr..

[25]  Ren Zhao,et al.  Solve train stowage planning problem of steel coil using a pointer-based discrete differential evolution , 2018, Int. J. Prod. Res..

[26]  Fei Yang,et al.  A scatter search for an integrated train plan of coil consolidation and stowage , 2010, 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM).

[27]  Anna Sciomachen,et al.  A 3D-BPP approach for optimising stowage plans and terminal productivity , 2007, Eur. J. Oper. Res..

[28]  Jiyin Liu,et al.  Modeling and solution for the ship stowage planning problem of coils in the steel industry , 2015 .

[29]  Lixin Tang,et al.  Mixed Integer Linear Programming and Solution for the Multi-tank Train Stowage Planning Problem of Coils in Steel Industry , 2014 .