Metaheuristic algorithms for supply chain management problems

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Volumn , Issue , 2012, Pages 110-135

  Taleizadeh, Ata Allah ^a   Cárdenas Barrón, Leopoldo Eduardo ^b  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^b SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84900653720     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-4666-2086-5.ch004     Document Type: Chapter

References (46)

1. Aarts, E. H. L., & Korst, J. H. M. (1989). Simulated annealing and Boltzmann machine: A stochastic approach to computing. Chichester, UK: John Wiley and Sons.
2. Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multiobjective optimization of supply chain networks. Computers & Industrial Engineering, 51, 196-215. doi:10.1016/j.cie.2006.07.011 doi:10.1016/j.cie.2006.07.011
3. Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116, 129-138. doi:10.1016/j.ijpe.2008.08.002 doi:10.1016/j.ijpe.2008.08.002
4. Banerjee, A. (1986). A joint economic lot size model for purchaser and vendor. Decision Sciencesk, 17, 292-311. doi:10.1111/j.1540-5915.1986.tb00228.x doi:10.1111/j.1540-5915.1986.tb00228.x
5. Cárdenas-Barrón, L. E. (2007). Optimizing inventory decisions in a multi-stage multi-customer supply chain: A note. Transportation Research Part E, Logistics and Transportation Review, 43, 647-654. doi:10.1016/j.tre.2005.09.011 doi:10.1016/j.tre.2005.09.011
6. Cárdenas-Barrón, L. E. (2011). The derivation of EOQ/EPQ inventory models with two backorders costs using analytic geometry and algebra. Applied Mathematical Modelling, 35, 2394-2407. doi:10.1016/j.apm.2010.11.053 doi:10.1016/j.apm.2010.11.053
7. Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28, 1131-1144. doi:10.1016/j.compchemeng.2003.0 9.014doi:10.1016/j.compchemeng.2003.09.014
8. Chern, C. C., & Hsieh, J. S. (2007). A heuristic algorithm for master planning that satisfies multiple objectives. Computers & Operations Research, 34, 3491-3513. doi:10.1016/j.cor.2006.02.022 doi:10.1016/j.cor.2006.02.022
9. Chopra, S., & Meindl, P. (2007). Supply chain management strategy, planning & operation (3rd ed.). Prentice-Hall of India.
10. Daniel, J. S. R., & Rajendran, C. (2005). Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives. European Journal of Operational Research, 175, 566-592. doi:10.1016/j.ejor.2005.04.039 doi:10.1016/j.ejor.2005.04.039
11. Fawcett, S. E., Ellram, L. M., & Ogden, J. A. (2007). Supply chain management from vision to implementation. New Jersey, USA: Prentice-Hall.
12. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76, 60-68. doi:10.1177/003754970107600201 doi:10.1177/003754970107600201
13. Goyal, S. K. (1977). An integrated inventory model for a single supplier-single customer problem. International Journal of Production Research, 15, 107-111. doi:10.1080/00207547708943107 doi:10.1080/00207547708943107
14. Goyal, S. K. (1988). Joint economic lot size model for purchaser and vendor: A comment. Decision Sciences, 19, 236-241. doi:10.1111/j.1540-5915.1988.tb00264.x doi:10.1111/j.1540-5915.1988.tb00264.x
15. Goyal, S. K., & Gupta, Y. P. (1989). Integrated inventory models: The vendor-buyer coordination. European Journal of Operational Research, 41, 261-269. doi:10.1016/0377-2217(89)90247-6 doi:10.1016/0377-2217(89)90247-6
16. Hariga, M., & Ben-Daya, M. (1999). Some stochastic inventory models with deterministic variable lead time. European Journal of Operational Research, 113, 42-51. doi:10.1016/ S0377-2217(97)00441-4 doi:10.1016/S0377-2217(97)00441-4
17. Heydari, J., Baradaran-Kazemzadeh, R., & Chaharsooghi, S. K. (2009). A study of lead time variation impact on supply chain performance. International Journal of Advanced Manufacturing Technology, 40, 1206-1215. doi:10.1007/s00170-008-1428-2 doi:10.1007/s00170-008-1428-2
18. Hill, R. (1999). The optimal production and shipment policy for the single-vendor singlebuyer integrated production-inventory problem. International Journal of Production Research, 37, 2463-2475. doi:10.1080/002075499190617 doi:10.1080/002075499190617
19. Holland, J. H. (1975). Adoption in neural and artificial systems. Ann Arbor, MI: The University of Michigan Press.
20. Hsiao, J. M., & Lin, C. (2005). A buyer-vendor EOQ model with changeable lead time in supply chain. International Journal of Advanced Manufacturing Technology, 26, 917-921. doi:10.1007/s00170-004-2063-1 doi:10.1007/s00170-004-2063-1
21. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, (pp. 1942-1945).
22. Kennedy, J., & Eberhart, R. (2001). Swarm intelligence. San Diego, CA: Academic Press.
23. Kim, C. O., Jun, J., Baek, J. K., Smith, R. L., & Kim, Y. D. (2005). Adaptive inventory control models for supply chain management. International Journal of Advanced Manufacturing Technology, 26, 1184-1192. doi:10.1007/s00170-004-2069-8 doi:10.1007/s00170-004-2069-8
24. Kirkpatrick, S., Gelatti, C. D., & Vecchi, M. P. (1994). Optimization by simulated annealing. In H. Gutfreund & G. Tolouse (Eds.), Advanced Series in Neurosciences, Vol. 3, Biology and computation: A physicist's choice, (pp. 671-680).
25. Lu, L. (1995). A one-vendor multi-buyer integrated inventory model. European Journal of Operational Research, 81, 312-323. doi:10.1016/0377-2217(93)E0253-T doi:10.1016/0377-2217(93)E0253-T
26. Roghanian, E., Sadjadi, S. J., & Aryanezhad, M. B. (2007). A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Applied Mathematics and Computation, 188, 786-800. doi:10.1016/j.amc.2006.10.032 doi:10.1016/j.amc.2006.10.032
27. Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581-598. doi:10.1016/S0305-0483(99)00080-8 doi:10.1016/S0305-0483(99)00080-8
28. Sakall, U. S. (2010). A note on fuzzy multiobjective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Computers & Industrial Engineering, 59, 1010-1012. doi:10.1016/j.cie.2010.07.008 doi:10.1016/j.cie.2010.07.008
29. Sayadi, M. K., Ramezanian, R., & GhaffariNasab, N. (2010). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computation, 1, 1-10. doi:10.5267/j.ijiec.2010.01.001 doi:10.5267/j.ijiec.2010.01.001
30. Siajadi, H., Ibrahim, R. N., & Lochert, P. B. (2006). A single-vendor multiple-buyer inventory model with a multiple-shipment policy. International Journal of Advanced Manufacturing Technology, 27, 1030-1037. doi:10.1007/s00170-004-2267-4 doi:10.1007/s00170-004-2267-4
31. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and managing the supply chain concepts, strategies and case studies. New York, NY. USA: McGraw-Hill.
32. Su, S., Zhan, D., & Xu, X. (2008). An extended state task network formulation for integrated production-distribution planning in supply chain. International Journal of Advanced Manufacturing Technology, 37, 1232-1249. doi:10.1007/s00170-007-1063-3 doi:10.1007/s00170-007-1063-3
33. Taleizadeh, A. A., Aryanezhad, M. B., & Niaki, S. T. A. (2008). Optimizing multiproduct multi-constraint inventory control systems with stochastic replenishment. Journal of Applied Sciences, 8, 1228-1234. doi:10.3923/jas.2008.1228.1234 doi:10.3923/jas.2008.1228.1234
34. Taleizadeh, A. A., Barzinpour, F., & Wee, H. M. (2011c). Meta-heuristic algorithms to solve the fuzzy single period problem. Mathematical and Computer Modelling, 54, 1273-1285. doi:10.1016/j.mcm.2011.03.038 doi:10.1016/j.mcm.2011.03.038
35. Taleizadeh, A. A., Jolai, F., & Wee, H. M. (2011e). Multi objective supply chain problem using a novel hybrid method of meta goal programming and firefly algorithm. Submitted.
36. Taleizadeh, A. A., Niaki, S. T., & Aryanezhad, M. B. (2009a). A hybrid method of pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint inventory control systems with random fuzzy replenishments. Mathematical and Computer Modelling, 49, 1044-1057. doi:10.1016/j.mcm.2008.10.013 doi:10.1016/j.mcm.2008.10.013
37. Taleizadeh, A. A., Niaki, S. T., & Aryanezhad, M. B. (2010b). Replenish-up-to multi chanceconstraint inventory control system with stochastic period lengths and total discount under fuzzy purchasing price and holding costs. International Journal of Systems Science, 41, 1187-1200. doi:10.1080/00207720903171761 doi:10.1080/00207720903171761
38. Taleizadeh, A. A., Niaki, S. T., & Barzinpour, F. (2011a). Multi-buyer multi-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead time. Applied Mathematics and Computation, 217, 9234-9253. doi:10.1016/j.amc.2011.04.001 doi:10.1016/j.amc.2011.04.001
39. Taleizadeh, A. A., Niaki, S. T., & Hosseini, V. (2009b). Optimizing multi product multi constraints bi-objective newsboy problem with discount by hybrid method of goal programming and genetic algorithm. Engineering Optimization, 41, 437-457. doi:10.1080/03052150802582175 doi:10.1080/03052150802582175
40. Taleizadeh, A. A., Niaki, S. T., & Makui, A. (2012). Multiproduct multiple-buyer single-vendor supply chain problem with stochastic demand, variable lead-time, and multi-chance constraint. Expert Systems with Applications, 39, 5338-5348. doi:10.1016/j.eswa.2011.11.001 doi:10.1016/j.eswa.2011.11.001
41. Taleizadeh, A. A., Niaki, S. T., & Seyed-Javadi, S. M. (2011). Multi-product multi-chance-constraint stochastic inventory problem with dynamic demand and partial back-ordering: A harmony search algorithm. Journal of Manufacturing Systems, 31(2). doi:10.1016/j.jmsy.2011.05.006
42. Taleizadeh, A. A., Niaki, S. T., Shafii, N., Ghavamizadeh Meibodi, R., & Jabbarzadeh, A. (2010a). A particle swarm optimization approach for constraint joint single buyer single vendor inventory problem with changeable lead-time and (r,Q) policy in supply chain. International Journal of Advanced Manufacturing Technology. International Journal of Advanced Manufacturing Technology, 51, 1209-1223. doi:10.1007/s00170-010-2689-0 doi:10.1007/s00170-010-2689-0
43. Wee, H. M., & Yang, P. C. (2007). A mutual beneficial pricing strategy of an integrated vendor-buyers inventory system. International Journal of Advanced Manufacturing Technology, 34, 179-187. doi:10.1007/s00170-006-0581-8 doi:10.1007/s00170-006-0581-8
44. Xu, J., & Zhao, L. (2008). A class of fuzzy rough expected value multi-objective decision making model and its application to inventory problems. Computers & Mathematics with Applications (Oxford, England), 56, 2107-2119. doi:10.1016/j.camwa.2008.03.040 doi:10.1016/j.camwa.2008.03.040
45. Yang, X. S. (2008). Nature-inspired meta-heuristic algorithms. UK: Luniver Press.
46. Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In O. Watanabe & T. Zeugmann (Eds.), Stochastic algorithms: Foundations and applications SAGA (Vol. 5792, pp. 169-178). Lecture Notes in Computer Science Berlin, Germany: Springer-Verlag. doi:10.1007/978-3-642-04944-6_14 doi:10.1007/978-3-642-04944-6_14