Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach

By Admi Syarif, YoungSu Yun, Mitsuo Gen. 2002

343 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4024a544d644c44895938cb5db0220278ec6787a

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In Industrial and Manufacturing Engineering

1. Mir Saman Pishvaee
2. Reza Zanjirani Farahani
3. W. Dullaert
4. Turan Paksoy
5. Lin Lin
6. Fulya Altıparmak
7. Hsiao-Fan Wang
8. Hsin-Wei Hsu

Citations in the corpus

• A closed-loop logistic model with a spanning-tree based genetic algorithm (2010, background): “[30], the crossover rate is suggested to be 0.”
• A genetic algorithm approach for multi-objective optimization of supply chain networks (2006, background): “For example, Jayaraman and Pirkul (2001), Jayaraman and Ross (2003), Yan, Yu, and Cheng (2003), Syam (2002), Syarif, Yun, and Gen (2002), Amiri (2006), Gen and Syarif (2005), and Truong and Azadivar (2005) had considered total cost of supply chain as an objective function in their studies.”
• A memetic algorithm for bi-objective integrated forward/reverse logistics network design (2010, background): “[26] SC, PC*, DisC* Ca, D, UCF, SP, En, SPr MILP L, TA C GA Jayaraman and Ross [27] PC, DisC* Ca, D, UCF, MP, En, SPr MILP L, TA C SA Sung and Song [15] PC, DisC* UC, D, UCF, SP, En, SPr MILP L, NV C TS-based heuristic Miranda and Garrido [28] PC, DisC* Ca, S, UCF, SP, En, MPr MINLP L C LR-based heu”