Determination of base-stock levels in a serial supply chain: a simulation-based simulated annealing heuristic

International Journal of Logistics Systems and Management

Volumn 1, Issue 2-3, 2005, Pages 149-186

  Daniel, J Sudhir Ryan ^a   Rajendran, Chandrasekharan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

base stocks; heuristics; multi echelon inventory; optimisation; serial supply chain; simulated annealing; simulation

Indexed keywords

EID: 40649092898     PISSN: 17427967     EISSN: 17427975     Source Type: Journal    
DOI: 10.1504/ijlsm.2005.005969     Document Type: Article

References (32)

1. Aarts, E. and Korst, J. (1989) Simulated Annealing and Boltzmann machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, Chichester: Wiley.
2. Aarts, E.H.L., Van Laarhoven, P.J.M., Lenstra, J.K. and Ulder, N.L.J. (1994) ‘A computational study of local search algorithms for jobshop scheduling’, ORSA Journal of Computing, Vol. 6, pp.118–125.
3. Ayers, J.B. (2000) Handbook of Supply Chain Management, St. Lucie Press.
4. Chung, C.-S., Flynn, J. and Stalinski, P. (2001) ‘A single-period inventory placement problem for a serial supply chain’, Naval Research Logistics, Vol. 48, pp.506–517.
5. Clark, A.J. (1960) ‘The use of simulation to evaluate a multiechelon dynamic inventory model’, Naval Research Logistics Quart., Vol. 7, pp.429–446.
6. Clark, A.J. and Scarf, H. (1960) ‘Optimal policies for a multi-echelon inventory problem’, Management Science, Vol. 6, pp.474–490.
7. Daniel, J.S.R. and Rajendran, C. (2004) ‘A simulation-based genetic algorithm for inventory optimization in a serial supply chain’, accepted for publication in International Transactions in Operational Research in 2004.
8. Daniel, J.S.R., Kochugovindan, B. and Rajendran, C. (2001) ‘Optimization of inventory in a serial supply chain using simulation and genetic algorithm’, Proceedings of the First International Conference on Logistics and Supply Chain Mangement (ILSCM 2001), Coimbatore, pp.60–65.
9. Ettl, M., Feign, G.E., Lin, G.Y. and Yao, D.D. (2000) ‘A supply network model with base-stock control and service requirements’, Operations Research, Vol. 48, pp.216–232.
10. Federgruen, A. and Zipkin, P. (1984) ‘Computational issues in an infinite horizon multi-echelon inventory problem with stochastic demand’, Operations Research, Vol. 32, pp.818–836.
11. Gallego, G. and Zipkin, P. (1999) ‘Stock positioning and performance estimation in serial production-transportation systems’, Manufacturing & Service Operations Management, Vol. 1, pp.77–88.
12. Ganeshan, R., Jack, E., Magazine, M.J. and Stephens, P. (1999) ‘A taxonomic review of supply chain management research’, in: S. Tayur, R. Ganeshan and M. Magazine (Eds) Quantitative Models for Supply Chain Management, Boston: Kluwer Academic Publishers.
13. Glasserman, P. and Tayur, S. (1995) ‘A sensitivity analysis for base-stock levels in multi-echelon production-inventory systems’, Management Science, Vol. 41, pp.263–281.
14. Hanssmann, F. (1962) Operations Research in Production and Inventory Control, New York: John Wiley & Sons Inc.
15. Hemamalani, B. and Rajendran, C. (2000) ‘Determination of the number of containers, production kanbans and withdrawal kanbans; and scheduling in kanban flowshops – Part 2’, International Journal of Production Research, Vol. 38, pp.2459–2572.
16. Johnson, D.S., Aragon, C.R., McGeoch, L.A. and Schevon, C. (1989) ‘Optimization by simulated annealing: an experimental valuation: Part I – Graph Partitioning’, Operations Research, Vol. 37, pp.865–891.
17. Kirkpatrick, S., Gelatt Jr., C.D. and Vecchi, M.P. (1983) ‘Optimization by simulated annealing’, Science, Vol. 220, pp.671–680.
18. Lee, H.L. and Billington, C. (1993) ‘Material management in decentralized supply chains’, Operations Research, Vol. 41, pp.835–847.
19. Nahmias, S. (1997) Production and Operations Analysis, Chicago: Irwin.
20. Papoulis, A. (1985) Probability, Random Variables and Stochastic Processes, Singapore: McGraw-Hill.
21. Parthasarathy, S. (1995) ‘Simulated annealing algorithms for scheduling with tardiness objectives in flowline-based manufacturing systems’, Unpublished M.S Thesis, Indian Institute of Technology, Madras.
22. Parthasarathy, S. and Rajendran, C. (1998) ‘Scheduling to minimise mean tardiness and weighted mean tardiness in flowshop and flowline-based manufacturing cell’, Computers and Industrial Engineering, Vol. 34, pp.531–546.
23. Petrovic, D., Roy, R. and Petrovic, R. (1998) ‘Modeling and simulation of a supply chain in an uncertain environment’, European Journal of Operational Research, Vol. 109, pp.299–309.
24. Potts, C.N. and Van Wassenhove, L.N.V. (1991) ‘Single machine tardiness sequencing heuristics’, IIE Transactions, Vol. 23, pp.346–354.
25. Rajendran, C. and Ziegler, H. (1999) ‘Heuristics for scheduling in flowshops and flowline-based manufacturing cells to minimize the sum of weighted flowtime and weighted tardiness of jobs’, Computers and Industrial Engineering, Vol. 37, pp.671–690.
26. Rao, U., Scheller-Wolf, A. and Tayur, S. (2000) ‘Development of a rapid-response supply chain at Caterpillar’, Operations Research, Vol. 48, pp.189–204.
27. Rosenbaum, B.A. (1981) ‘Inventory placement in a two-echelon inventory system: an application’, in: L.B. Schwarz (Ed.) Multi-level Production Inventory Systems: Theory and Practice, Amsterdam: North-Holland Publishing Co, pp.195–207.
28. Schmidt, G. and Wilhelm, W.E. (2000) ‘Strategic, tactical and operational decisions in multi-national logistics networks: a review and discussion of modeling issues’, International Journal of Production Research, Vol. 38, pp.1501–1523.
29. Shang, K.H. and Song, J.S. (2003) ‘Newsvendor bounds and heuristic for optimal inventory policies in serial supply chains’, Management Science, Vol. 49, pp.618–638.
30. Sridhar, J. and Rajendran, C. (1993) ‘Scheduling in a cellular manufacturing system: a simulated annealing approach’, International Journal of Production Research, Vol. 31, pp.2927–2945.
31. Van Houtum, G.J., Inderfurth, K. and Zijm, W.H.M. (1996) ‘Material coordination in stochastic multi-echelon systems’, European Journal of Operational Research, Vol. 95, pp.1–23.
32. Zipkin, P. (2000) Foundations ofInventory Management, New York: McGraw-Hill.