Combining mixed integer programming and constraint programming to solve the integrated scheduling problem of container handling operations of a single vessel
•Integrated scheduling of quay cranes, yard trucks and yard cranes.•Interferences among quay cranes are fully considered.•Both mixed integer programming and constraint programming models are developed.•A hybrid solution strategy relying on commercial optimization solvers.•Large-scale instances solve...
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| Vydáno v: | European journal of operational research Ročník 285; číslo 3; s. 884 - 901 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
16.09.2020
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| ISSN: | 0377-2217, 1872-6860 |
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| Abstract | •Integrated scheduling of quay cranes, yard trucks and yard cranes.•Interferences among quay cranes are fully considered.•Both mixed integer programming and constraint programming models are developed.•A hybrid solution strategy relying on commercial optimization solvers.•Large-scale instances solved within 2 minutes (optimality gaps: less than 3.31%).
In the container terminals of seaports, the container handling system consists of a variety of container handling machines such as quay cranes, internal yard trucks, and yard cranes. This study applies a holistic approach to the integrated scheduling of these machines for the container handling operations of a single vessel. We formulate this special hybrid flow shop scheduling problem through both mixed integer programming (MIP) and constraint programming (CP) techniques. Then we develop an easily-implemented approach that combines the strengths of MIP and CP. First, the MIP model, which only considers quay crane scheduling, is solved by an MIP solver, and a quay crane allocation plan is retrieved from the MIP solution. Then, this quay crane allocation plan is fed to the CP model, warm-starting the branch-and-prune algorithm built in a CP optimizer. Our numerical experiments reveal that this hybrid MIP/CP approach can solve the large-sized instances with up to 1000 containers, 6 quay cranes, 36 yard trucks, and 15 yard cranes to optimality with a gap of less than 3.31%, within a solution time of 2 minutes. If we increase the solution time to 5 minutes, this hybrid approach solves larger instances with up to 1400 containers to optimality with a gap of less than 1.41%. The state-of-the-art dedicated algorithms reported in the literature (which address an easier version of the same problem by ignoring non-crossing constraints and safety margins between quay cranes) are only able to find solutions for real-life instances with up to 500 containers within the solution time of 2930 or 5221 seconds, leaving a 4% or an unknown optimality gap. Thus, this study improves the solution of this integrated scheduling problem in terms of the instance size, solution efficiency, and solution optimality. |
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| AbstractList | •Integrated scheduling of quay cranes, yard trucks and yard cranes.•Interferences among quay cranes are fully considered.•Both mixed integer programming and constraint programming models are developed.•A hybrid solution strategy relying on commercial optimization solvers.•Large-scale instances solved within 2 minutes (optimality gaps: less than 3.31%).
In the container terminals of seaports, the container handling system consists of a variety of container handling machines such as quay cranes, internal yard trucks, and yard cranes. This study applies a holistic approach to the integrated scheduling of these machines for the container handling operations of a single vessel. We formulate this special hybrid flow shop scheduling problem through both mixed integer programming (MIP) and constraint programming (CP) techniques. Then we develop an easily-implemented approach that combines the strengths of MIP and CP. First, the MIP model, which only considers quay crane scheduling, is solved by an MIP solver, and a quay crane allocation plan is retrieved from the MIP solution. Then, this quay crane allocation plan is fed to the CP model, warm-starting the branch-and-prune algorithm built in a CP optimizer. Our numerical experiments reveal that this hybrid MIP/CP approach can solve the large-sized instances with up to 1000 containers, 6 quay cranes, 36 yard trucks, and 15 yard cranes to optimality with a gap of less than 3.31%, within a solution time of 2 minutes. If we increase the solution time to 5 minutes, this hybrid approach solves larger instances with up to 1400 containers to optimality with a gap of less than 1.41%. The state-of-the-art dedicated algorithms reported in the literature (which address an easier version of the same problem by ignoring non-crossing constraints and safety margins between quay cranes) are only able to find solutions for real-life instances with up to 500 containers within the solution time of 2930 or 5221 seconds, leaving a 4% or an unknown optimality gap. Thus, this study improves the solution of this integrated scheduling problem in terms of the instance size, solution efficiency, and solution optimality. |
| Author | Qin, Tianbao Chen, Jiang Hang Sha, Mei Du, Yuquan |
| Author_xml | – sequence: 1 givenname: Tianbao orcidid: 0000-0002-5024-6186 surname: Qin fullname: Qin, Tianbao organization: College of Transport & Communications, Shanghai Maritime University, Shanghai 200135, China – sequence: 2 givenname: Yuquan surname: Du fullname: Du, Yuquan organization: National Centre for Ports and Shipping, Australian Maritime College, University of Tasmania, Launceston TAS 7250, Australia – sequence: 3 givenname: Jiang Hang surname: Chen fullname: Chen, Jiang Hang organization: International Business School Suzhou, Xi’an Jiao Tong - Liverpool University, Suzhou 215123, China – sequence: 4 givenname: Mei surname: Sha fullname: Sha, Mei email: meisha@shmtu.edu.cn organization: College of Transport & Communications, Shanghai Maritime University, Shanghai 200135, China |
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| Keywords | Mixed integer programming OR in maritime industry Container terminal Constraint programming Container unloading/loading problem |
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| Title | Combining mixed integer programming and constraint programming to solve the integrated scheduling problem of container handling operations of a single vessel |
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