Crossdocking: JIT Scheduling with Time Windows
In this paper, we study a problem central to crossdocking that aims to eliminate or minimize storage and order picking activity using JIT scheduling. The problem is modelled naturally as a machine scheduling problem. As the problem is NP-hard, and for real-time applications, we designed and implemen...
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2004
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sg-smu-ink.lkcsb_research-36152016-03-12T07:26:33Z Crossdocking: JIT Scheduling with Time Windows LI, Yanzhi LIM, Andrew Rodrigues, Brian In this paper, we study a problem central to crossdocking that aims to eliminate or minimize storage and order picking activity using JIT scheduling. The problem is modelled naturally as a machine scheduling problem. As the problem is NP-hard, and for real-time applications, we designed and implemented two heuristics. The first uses Squeaky Wheel Optimization embedded in a Genetic Algorithm and the second uses Linear Programming within a Genetic Algorithm. Both heuristics offer good solutions in experiments where comparisons are made with the CPLEX solver 2004-12-01T08:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/2616 info:doi/10.1057/palgrave.jors.2601812 https://www.jstor.org/stable/4101853 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University crossdock machine scheduling just-in-time scheduling heuristics Operations and Supply Chain Management |
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crossdock machine scheduling just-in-time scheduling heuristics Operations and Supply Chain Management LI, Yanzhi LIM, Andrew Rodrigues, Brian Crossdocking: JIT Scheduling with Time Windows |
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In this paper, we study a problem central to crossdocking that aims to eliminate or minimize storage and order picking activity using JIT scheduling. The problem is modelled naturally as a machine scheduling problem. As the problem is NP-hard, and for real-time applications, we designed and implemented two heuristics. The first uses Squeaky Wheel Optimization embedded in a Genetic Algorithm and the second uses Linear Programming within a Genetic Algorithm. Both heuristics offer good solutions in experiments where comparisons are made with the CPLEX solver |
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text |
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LI, Yanzhi LIM, Andrew Rodrigues, Brian |
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LI, Yanzhi LIM, Andrew Rodrigues, Brian |
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LI, Yanzhi |
| title |
Crossdocking: JIT Scheduling with Time Windows |
| title_short |
Crossdocking: JIT Scheduling with Time Windows |
| title_full |
Crossdocking: JIT Scheduling with Time Windows |
| title_fullStr |
Crossdocking: JIT Scheduling with Time Windows |
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Crossdocking: JIT Scheduling with Time Windows |
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crossdocking: jit scheduling with time windows |
| publisher |
Institutional Knowledge at Singapore Management University |
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2004 |
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https://ink.library.smu.edu.sg/lkcsb_research/2616 https://www.jstor.org/stable/4101853 |
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