A simulation based optimization approach to supply chain management under demand uncertainty

Computers and Chemical Engineering

Volumn 28, Issue 10, 2004, Pages 2087-2106

  Jung, June Young ^a   Blau, Gary ^a   Pekny, Joseph F ^a   Reklaitis, Gintaras V ^a   Eversdyk, David ^b  

^a PURDUE UNIVERSITY   (United States)

^b DOW CHEMICAL COMPANY   (United States)

Author keywords

Customer satisfaction level; Demand uncertainty; Safety stock levels; Simulation based optimization; Stochastic optimization; Supply chain management

Indexed keywords

COMPUTER SIMULATION; COST EFFECTIVENESS; CUSTOMER SATISFACTION; INDUSTRIAL MANAGEMENT; OPTIMIZATION; PROBLEM SOLVING; PROCESS ENGINEERING; SCHEDULING;

SUPPLY CHAIN DESIGN; SUPPLY CHAIN MANAGEMENT;

CHEMICAL INDUSTRY;

SUPPLIER;

ARTICLE; CHEMICAL INDUSTRY; MATHEMATICAL COMPUTING; MODEL; SAFETY; SATISFACTION; SIMULATION;

EID: 4143144381     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2004.06.006     Document Type: Article

References (25)

1. Advanced Process Combinatorics Inc. (2004). Available on the web at http://www.combination.com/.
2. Andradottir, S. (1998). A review of simulation optimization techniques. Proceedings of the 1998 Winter Simulation Conference, 151-158.
3. Carson, Y., & Maria, A. 1997. Simulation optimization: methods and applications. Proceedings of the 1997 Winter Simulation Conference, 118-126.
4. Cheng L., Subramanian E., Westerberg A.W. Design and planning under uncertainty: Issues on problem formulation and solution. Computers and Chemical Engineering. 27:2003;781-802
5. DeCroix G.A., Arreola-Risa A. Optimal production and inventory policy for multiple products under resource constraints. Management Science. 44(7):1998;1201-1218
6. Fu, M., & Hu, J. Q. 1997. Conditional Monte-Carlo: Gradient Estimation and Optimization Applications. Kluwer Academic Publishers.
7. Fu M. Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing. 14(3):2002;192-215
8. Graves S.C., Willems S.P. Optimizing strategic safety stock placement in supply chains. Manufacturing and Service Operations Management. 2(1):2000;68-83
9. Gupta A., Maranas C.D. A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty. Industrial and Engineering Chemistry Research. 39(10):2000a;3799-3813
10. Gupta A., Maranas C.D. Midterm supply chain planning under demand uncertainty: Customer demand satisfaction and inventory management. Computers and Chemical Engineering. 27:2003;1219-1227
11. Gupta A., Maranas C.D., McDonald C.M. Midterm supply chain planning under demand uncertainty: Customer demand satisfaction and inventory management. Computers and Chemical Engineering. 24:2000;2613-2621
12. Hopp, J. W., & Spearman, M. L. (2000). Factory Physics. McGraw-Hill.
13. Ierapetritou M.G., Pistikopoulos E.N. Novel optimization approach of stochastic planning models. Industrial and Engineering Chemistry Research. 33(8):1994;1930-1942
14. Karimi I.A., McDonald C.M. Planning and scheduling of parallel semicontinuous process: Short-term scheduling. Industrial and Engineering Chemistry Research. 36(7):1997;2701-2714
15. Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis. McGraw-Hill.
16. Mesquite Software, Inc. (2004). Available on the web at http://www.mesquite.com/.
17. McDonald C.M., Karimi I.A. Planning and scheduling of parallel semicontinuous process: Production planning. Industrial and Engineering Chemistry Research. 36(7):1997;2691-2700
18. Porteus, E. L. (1990). Stochastic inventory theory. In D. Heyman, & M. Sobel (Eds.), Handbooks in Operations Research and Management Science (vol 2., pp. 605-652). Amsterdam: Elservier Science Publishers.
19. Porteus, E. L. (2002). Foundations of Stochastic Inventory Theory. Stanford University Press.
20. Reklaitis G.V. Review of scheduling of process operations. AIChE Symposium Series. 78(214):1982;119-133
21. Shah N., Pantelides N.N. Design of multipurpose batch plants with uncertain production requirements. Industrial and Engineering Chemistry Research. 31(5):1992;1325-1337
22. Subramanyam S., Pekny J.F., Reklaitis G.V. Design of batch chemical plants under market uncertainty. Industrial and Engineering Chemistry Research. 33(11):1994;2688-2701
23. Subramanian D., Pekny J.F., Reklaitis G.V. A simulation-optimization framework for research and development pipeline management. AIChE Journal. 47(10):2000a;2226-2242
24. Subramanian, D. 2001b. A Computational Architecture to Address Combinatorial and Stochastic Aspects of Process Management Problems. Ph.D. Thesis, School of Chemical Engineering, Purdue University.
25. Zipkin P.H. Models for design and control of stochastic, multi-item batch production systems. Operations Research. 34(1):1986;92-104