Solving large multilevel lot-sizing problems with a simple heuristic algorithm based on segmentation

International Journal of Innovative Computing, Information and Control

Volumn 6, Issue 3, 2010, Pages 817-827

  Kaku, Ikou ^a   Li, Zhaoshi ^a   Xu, Chunhui ^b  

^a AKITA PREFECTURAL UNIVERSITY   (Japan)

^b CHIBA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Genetic algorithm; Heuristics; Multilevel lot sizing problem; Segmentation; Soft optimization approach

Indexed keywords

COST STRUCTURE; IMPROVING PERFORMANCE; LOT SIZING PROBLEMS; MULTILEVEL LOT-SIZING PROBLEM; OPTIMIZATION APPROACH; PRODUCT STRUCTURE; SIMULATION EXPERIMENTS; SOLUTION SPACE; SOLUTION STRUCTURES; TIME INVARIANTS;

GENETIC ALGORITHMS; HEURISTIC ALGORITHMS;

OPTIMIZATION;

EID: 77950100535     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (26)

1. I. Kaku and C. Xu, A soft optimization approach for solving a complicated multilevel lot-sizing problem, Proc. of the 8th Conf. Industrial Management, pp. 3-8, 2006.
2. I. Kaku, Z. S. Li and C. Xu, An effective heuristic algorithm based on segmentation for solving a multilevel lot-sizing problem, International Journal of Biomedical Soft Computing and Human Sciences, 2008.
3. N. Dellaert and J. Jeunet, Solving large unconstrained multilevel lot-sizing problems using a hybrid genetic algorithm, International Journal of Production Research, vol.38, no.5, pp.1083-1099, 2000.
4. N. Dellaert, J. Jeunet and N. Jonard, A genetic algorithm to solve the general multi-level lot-sizing problem with time-varying costs, International Journal of Production Economics, vol.68, no.3, pp.241-257, 2000.
5. E. Steinberg and H. A. Napier, Optimal multi-level lot sizing for requirements planning systems, Management Science, vol.26, no.12, pp.1258-1271, 1980.
6. W. I. Zhangwill, Minimum concave cost flows in certain network, Management Science, vol.14, no.7, pp.429-450, 1968.
7. W. I. Zhangwill, A backlogging model and a multi-echelon model of a dynamic economic lot size production system-a network approach, Management Science, vol.15, no.9, pp.506-527, 1969.
8. W. B. Crowston and H. M. Wagner, Dynamic lot size models for multi-stage assembly system, Management Science, vol.20, no.1, pp.14-21, 1973.
9. P. Afentakis, B. Gavish and U. Kamarkar, Computationally efficient optimal solutions to the lot-sizing problem in multistage assembly systems, Management Science, vol.30, no.2, pp.223-239, 1984.
10. P. Afentakis and B. Gavish, Optimal lot-sizing algorithms for complex product structures, Operations Research, vol.34, no.2, pp.237-249, 1986.
11. L. E. Yelle, Materials requirements lot sizing: A multilevel approach, International Journal of Production Research, vol.17, no.3, pp.223-232, 1979.
12. E. A. Veral and R. L. LaForge, The performance of a simple incremental lot-sizing rule in a multilevel inventory environment, Decision Sciences, vol.16, no.1, pp.57-72, 1985.
13. J. D. Blackburn and R. A. Millen, Improved heuristics for multi-stage requirements planning system, Management Science, vol.28, no.1, pp.44-56, 1982.
14. J. D. Blackburn and R. A. Millen, An evaluation of heuristic performance in multi-stage lot-sizing systems, International Journal of Production Research, vol.23, no.5, pp.857-866, 1985.
15. B. J. Coleman and M. A. McKnew, An improved heuristic for multilevel lot sizing in material requirements planning, Decision Sciences, vol.22, no.1, pp.136-156, 1991.
16. X. Cai, Z. Cui, J. Zeng and Y. Tan, Particle swarm optimization with self-adjusting cognitive selection strategy, International Journal of Innovative Computing, Information and Control, vol.4, no.4, pp.943-952, 2008.
17. C. Lin, Y. Liu and C. Lee, An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications, International Journal of Innovative Computing, Information and Control, vol.4, no.8, pp.2087-2096, 2008.
18. L. Deng, Y. Lin, W. Zheng and Y. Xi, Incorporating justification in the particle swarm optimization for the RCPSP, International Journal of Innovative Computing, Information and Control, vol.4, no.9, pp.2363-2374, 2009.
19. T. Su, J. Jhang and C. Hou, A hybrid artificial neural networks and particle swarm optimization for function approximation, International Journal of Innovative Computing, Information and Control, vol.5, no.2, pp.387-397, 2009.
20. C. Liu and Y. Wang, A new dynamic multi-objective optimization evolutionary algorithm, International Journal of Innovative Computing, Information and Control, vol.4, no.9, pp.2263-2271, 2008.
21. T. Liu, C. Chen and J. Chou, Intelligent design of the automobile fixtures with genetic algorithm, International Journal of Innovative Computing, Information and Control, vol.4, no.10, pp. 2533-2550, 2008.
22. J. Cai, J. Zhang and X. Zhao, A star spectrum outlier mining system based on simulated annealing, International Journal of Innovative Computing, Information and Control, vol.4, no.8, pp.1897-1919, 2008.
23. O. Tang, Simulated annealing in lot sizing problems, International Journal of Production Economics, vol.88, no.2, pp.173-181, 2004.
24. Y. Han, J. Tang, I. Kaku and Y. Liu, The application of hybrid particle swarm optimization algorithm to multilevel lot-sizing problem, INFORMS 2006, Hong Kong, 2006.
25. C. Xu, Soft and hard resolution in optimization analysis, International Journal of Advances in Systems Sciences and Applications, vol.3, no.4, pp.488-496, 2003.
26. C. Xu and P. Ng, A soft approach for hard continuous optimization, European Journal of Operational Research, vol.173, no.1, pp. 18-29, 2006.