Logistics for world-wide crude oil transportation using discrete event simulation and optimal control

Computers and Chemical Engineering

Volumn 28, Issue 6-7, 2004, Pages 897-911

  Cheng, Lifei ^a   Duran, Marco A ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b EXXONMOBIL UPSTREAM RESEARCH COMPANY   (United States)

Author keywords

Dynamic programming; Inventory routing problem; Visual basic for application

Indexed keywords

COMPUTER SIMULATION; DECISION MAKING; DECISION SUPPORT SYSTEMS; INDUSTRIAL MANAGEMENT; LOGISTICS; MARKOV PROCESSES; MATHEMATICAL MODELS; TRANSPORTATION;

OIL INDUSTRY; SIMULATION MODEL; SUPPLY CHAIN MANAGEMENT (SCM);

CRUDE PETROLEUM;

PETROLEUM;

OIL AND GAS PRODUCTION;

COMMERCIAL PHENOMENA; COMPUTER PROGRAM; COMPUTER SIMULATION; COMPUTER SYSTEM; CONFERENCE PAPER; COST BENEFIT ANALYSIS; DECISION MAKING; EVALUATION; MARKETING; OIL INDUSTRY; PETROCHEMICAL INDUSTRY; STATISTICAL MODEL; STOCHASTIC MODEL; TRAFFIC AND TRANSPORT;

EID: 1942500454     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2003.09.025     Document Type: Conference Paper

References (16)

1. Beaujon G.J., Turnquist M.A. A model for fleet sizing and vehicle allocation. Transportation Science. 25(1):1991;19-45.
2. Bellman, R. E. (1957). Dynamic programming. Princeton, NJ: Princeton University Press.
3. Bertsekas, D. P. (1995). Dynamic programming and optimal control (Vol. I). Athena Scientific.
4. Bertsekas, D. P., & Tsitsiklis, N. J. (1996). Neuro-dynamic programming. Athena Scientific.
5. Bodin, L., Golden, B., Assad, A., & Ball, M. (1981). The state of the art in the routing and scheduling of vehicle and crews: executive summary, Working Paper MS/S #81-035, College of Business and Management, University of Maryland.
6. Cheng, L., & Duran M. A. (2002). Just-in-time refinery operation strategy: a hybrid make-to-order and make-to-stock system, Technical Report, ExxonMobil Research and Engineering.
7. Gordon, G. J. (1999). Approximate solutions to markov decision processes, Ph.D. Thesis in School of Computer Science, Carnegie Mellon University.
8. Federgruen, A., & Simchi-Levi, D., 1995. Analysis of Vehicle Routing and Inventory Routing Problems, Handbooks in Operations Research and Management Science (Vol. 8, pp. 297-373). Network Routing, North-Holland, Amsterdam.
9. Kelton, W. D., Sadowski, R. P., & Sadowski, D. A. (1998). Simulation with Arena. New York: McGraw-Hill.
10. Kleywegt A.J., Nori V.S., Savelsbergh M.W.P. The stochastic inventory routing problem with direct deliveries. Transportation Science. 36(1):2002;94-118.
11. Marbach, P. (1998). Simulation-based methods for markov decision processes. Ph.D. Thesis in Department of Electrical Engineering and Computer Science, MIT.
12. Puterman, M. L. (1994). Markov decision processes: discrete stochastic dynamic programming. New York: Wiley.
13. Tesauro G.J. Practical issues in temporal-difference learning. Machine Learning. 8:1992;257-277.
14. Tsitsiklis J.N., Van Roy B. Feature-based methods for large scale dynamic programming. Machine Learning. 22:1996;59-94.
15. Shannon, R. E. (1975). Systems simulation: the art and science. Englewood Cliffs: Prentice-Hall.
16. Van Roy, B., Bertsekas, D. P., Lee, Y., & Tsitsiklis, J. N. (1997). A neuro-dynamic programming approach to retailer inventory management, Technical Report, Laboratory for Information and Decision Systems, MIT.