A steady-state genetic algorithm for multi-product supply chain network design

Computers and Industrial Engineering

Volumn 56, Issue 2, 2009, Pages 521-537

  Altiparmak, Fulya ^a   Gen, Mitsuo ^b   Lin, Lin ^b   Karaoglan, Ismail ^c  

^a GAZI UNIVERSITY   (Turkey)

^b WASEDA UNIVERSITY   (Japan)

^c SELCUK UNIVERSITY   (Turkey)

Author keywords

Genetic algorithms; Lagrangean heuristic; Simulated annealing; Supply chain network design

Indexed keywords

ALGORITHMS; ANNEALING; ARCHITECTURAL DESIGN; DESIGN; DISTRIBUTED PARAMETER NETWORKS; DISTRIBUTION OF GOODS; GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; HEURISTIC METHODS; SUPPLY CHAIN MANAGEMENT; SUPPLY CHAINS;

CUSTOMER DEMANDS; DESIGN PROBLEMS; DESIGN TASKS; DIFFERENT SIZES; DISTRIBUTION CENTERS; DISTRIBUTION NETWORKS; ENCODING STRUCTURES; LAGRANGEAN HEURISTIC; MINIMUM COSTS; MULTI PRODUCTS; MULTI STAGES; OPERATIONS MANAGEMENTS; SOLUTION PROCEDURES; STEADY-STATE GENETIC ALGORITHMS; SUPPLY CHAIN NETWORK DESIGN; SUPPLY CHAIN NETWORKS; SUPPLY-CHAIN MANAGEMENTS;

SIMULATED ANNEALING;

EID: 59349101034     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2007.05.012     Document Type: Article

References (30)

1. Altiparmak F., Gen M., Lin L., and Paksoy T. A genetic algorithm for multi-objective optimization of supply chain networks. Computers & Industrial Engineering 51 (2006) 197-216
2. Bielli M., Caramia M., and Carotenuto P. Genetic algorithms in bus network optimization. Transportation Research, Part C 10 (2002) 19-34
3. Chen X., Wan W., and Xu X. Modeling rolling batch planning as vehicle routing problem with time windows. Computers and Operations Research 25 12 (1998) 1127-1136
4. Gen M., and Li Y. Solving multi-objective transportation problem by spanning tree-based genetic algorithm. In: Parmee I. (Ed). Adaptive computing in design and manufacture (1998), Springer-Verlag, NewYork 95-108
5. Gen, M., & Li, Y. (1999). Spanning tree-based genetic algorithm for bicriteria fixed charge transportation problem. Proceedings of the Congress on Evolution of Computers, pp. 2265-2271.
6. Gen M., and Cheng R. Genetic algorithms and engineering optimization (2000), John Wiley and Sons, New York
7. Gen M., Altiparmak F., and Lin L. A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum 28 (2006) 337-354
8. Holland J. Adaptation in natural and artificial systems (1975), University of Michigan Press, Ann Arbor
9. Jain N., and Bagchi T.P. Flowshop scheduling by hybridized GA: Some new results. International Journal of Industrial Engineering 7 3 (2000) 213-223
10. Jang Y.J., Jang S.Y., Chang B.M., and Park. A combined model of network design and production/distribution planning for a supply network. Computers & Industrial Engineering 43 (2002) 263-281
11. Jaramillo J.H., Bhadury J., and Batta R. On the use of genetic algorithms to solve location problems. Computers & Operations Research 29 (2002) 761-779
12. Jayaraman V., and Pirkul H. Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research 133 (2001) 394-408
13. Jayaraman V., and Ross A. A simulated annealing methodology to distribution network design and management. European Journal of Operational Research 144 (2003) 629-645
14. Keskin B.B., and Uster H. A scatter search-based heuristic to locate capacitated transshipment points. Computers & Operations Research 34 10 (2007) 3112-3125
15. Kirkpatrick S., Gerlatt C.D., and Vecchi N.P. Optimization by simulated annealing. Science 220 (1983) 671-680
16. Lundy M., and Mees A. Convergence of an annealing algorithm. Mathematical Programming 34 1 (1986) 111-124
17. Michalewicz Z., Vignaux G.A., and Hobbs M. A non-standard genetic algorithm for the nonlinear transportation problem. ORSA Journal on Computing 3 (1991) 307-316
18. Park Y. A hybrid genetic algorithm for the vehicle scheduling problem with due times and time deadlines. International Journal of Production Economics 73 (2001) 175-188
19. Reeves C. A genetic algorithm for flowshop sequencing. Computers & Operations Research 22 1 (1995) 5-13
20. Reeves C. Modern heuristic techniques for combinatorial problems (1995), John Wiley and Sons, New York
21. Ruiz R., Maroto C., and Alcaraz J. Two new robust genetic algorithms for the flowshop scheduling problem. Omega (2006) 461-476
22. Shapiro J.F. Modeling the supply chain (2000), Duxbury Press, Pacific Grove
23. Syarif, A., & Gen, M., (2000). Genetic algorithm for nonlinear side constrained transportation problem. Proceedings of International Conference on Computers & Industrial Engineering, Beijing, China.
24. Syarif A., Yun Y., and Gen M. Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach. Computers & Industrial Engineering 43 (2002) 299-314
25. Syarif A., and Gen M. Solving exclusionary side constrained transportation problem by using a hybrid spanning tree-based genetic algorithm. Journal of Intelligent Manufacturing 14 (2003) 389-399
26. Syam S.S. A model and methodologies for the location problem with logistical components. Computers & Operations Research 29 (2002) 1173-1193
27. Thomas D.J., and Griffin P.M. Coordinated supply chain management. European Journal of Operational Research 94 (1996) 1-115
28. Yeh W.C. A hybrid heuristic algorithm for multistage supply chain network problem. International Journal of Advance Manufacturing Technology 26 5-6 (2005) 675-685
29. Yeh W.C. A efficient memetic algorithm for multi-stage supply chain network problem. International Journal of Advance Manufacturing Technology 29 7-8 (2006) 803-813
30. Zhou G., Min H., and Gen M. The balanced allocation of customers to multiple distribution centers in the supply chain network: A genetic algorithm approach. Computers & Industrial Engineering 43 (2002) 252-261