Efficient frontier for multi-objective stochastic transportation networks in international market of perishable goods

Journal of Applied Research and Technology

Volumn 12, Issue 4, 2014, Pages 654-665

  Bustos, A ^a   Herrera, L ^b   Jimenez E ^a  

^a INSTITUTO MEXICANO DEL TRANSPORTE   (Mexico)

^b SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

Minimum cost flow; Multi objective optimization; Perishable goods; Stochastic network

Indexed keywords

EID: 84919706202     PISSN: 16656423     EISSN: 24486736     Source Type: Journal    
DOI: 10.1016/S1665-6423(14)70082-3     Document Type: Article

References (32)

1. Portal del Observatorio Estrategico Tecnologico, https://oet.itesm.mx/portal/page/portal/OET/Publica?p-iPortal=3. Oct/10/2011
2. C. Martner et al. "Export logistics chains in Mexico". Cadenas logísticas de exportación en México. Publicación técnica 276. Instituto Mexicano del Transporte. 2005.
3. C. Morales, and M. de la Torre, M. "Characteristics of refrigerated transport in Mexico". Características del transporte refrigerado en México. Publicación técnica 297. Instituto Mexicano del Transporte. 2006.
4. H. Hamacher et al. Multiple objective minimum cost flow problems: A review. European Journal of Operational Research.; 176. Vol. 176, Issue: 3. pp. 1404-1422. 2007.
5. S. Opasanon, and E. Miller-Hooks. Multicriteria adaptive paths in stochastic, time-varying networks. European Journal of Operational Research., 173: pp. 72-91. 2006.
6. M. Fonseca, et al. Solving scalarized multi-objective network flow problems with an interior point method. International Transactions in Operational Research 17(5): pp. 607-636. 2009.
7. A. Eusebio, and J. Figueira. Finding non-dominated solutions in bi-objective integer network flow problems. Computers & Operations Research 36: pp. 2554-2564. 2009.
8. A. Raith and M. Ehrgott. A two-phase algorithm for the bi-objective integer minimum cost flow problem. Computers & Operations Research 36: pp. 1945-1954. 2009.
9. A. Przybylski et al. The biobjective integer minimum cost flow problem-incorrectness of Sedeño-Noda and Gonzalez Martin's algorithm. Computers & Operations Research; 33(5): pp. 1459-1463. 2006.
10. A. Bustos, and L. Herrera. Building an efficient frontier for multi-objective stochastic transportation networks. in: IERC Annual Conference and Expo 2011. Reno, Nevada. 2011.
11. R. Ahuja et al. Network Flows, Theory, algorithms and applications. Prentice Hall. 1997.
12. L. Kantorovich. Mathematical Methods in the Organization and Planning of Production. Management Science 6: pp. 336-422. 1939.
13. F. Hitchcock. The Distribution of a Product from Several Sources to Numerous Localities. Journal of Maths Physics, 20: pp. 224-230. 1941.
14. L. Ford and D. Fulkerson. Flows in Networks. Princeton University Press. 1962
15. A. Weintraub. A Primal Algorithm to Solve Network Flow Problems with Convex Costs. Management Science, 21: pp. 87-97. 1974.
16. R. Helgason and J. Kennington. An Efficient Procedure for Implementing a Dual Simplex Network Flow Algorithm. AIEE Transactions. 9: pp. 63-68. 1977.
17. D. Kelly et al. The Minimum Cost Flow Problem and The Network Simplex Solution Method. National University of Ireland. 1991.
18. C. Coello et al. Evolutionary algorithms for solving multi-objective problems. 2nd edition. Genetic and evolutionary computation series. Springer. 2007.
19. B. Bernábe et al. A Multiobjective Approach for the Heuristic Optimization of Compactness and Homogeneity in the Optimal Zoning. Journal of Applied Research and Technology. Vol 10 No. 3. Pp. 447-457. 2012.
20. R. Marler and J. Arora. Survey of multi-objective optimization methods for engineering. Review article. Struct Multidisc Optim, 26: pp. 369-395. 2004.
21. G. Rangaiah. Multi-Objective Optimization, Techniques and Applications in Chemical Engineering. National University of Singapore. 2008.
22. E. Talbi. Metaheuristics: From Design to Implementation. Canada: John Wiley & sons inc. 2009.
23. K. Deb and A. Kumar. Interactive Evolutionary Multi-Objective Optimization and Decision-Making using Reference Direction Method. 2007. GECCO'07, July 7-11, London, England, United Kingdom. 2007.
24. E. Rincón et al. Multiobjective Algorithm for Redistricting. Journal of Applied Research and Technology. Vol 11 No. 3. Pp. 324-330. 2013.
25. V. Chankong and Y. Haimes. Multi-objective Decision Making: Theory and Methodology. Elsevier-North Holland. 1983.
26. Y. Haimes et al. Multi-objective Optimization in Water Resources Systems: The Surrogate Worth Trade-off Method. Elsevier Scientific Publishing Company. 1975.
27. G. Mavrotas. Generation of efficient solutions in Multi-objective Mathematical Programming problems using GAMS. Effective implementation of the ε-constraint method. School of Chemical Engineering. National Technical University of Athens. 2007.
28. A. Madansky. Inequalities for Stochastic Linear Programming Problems. Management Science, 6(2): pp. 197-204. 1960.
29. A. Shapiro et al. Lectures on stochastic programming, modeling and theory. Society for industrial and applied mathematics and the Mathematical programming society. Philadelphia. 2009.
30. G. Dantzig. Linear programming under uncertainty. Management Science, Vol. 1(pp. 197-206). 1955.
31. T. Santoso et al. A stochastic programming approach for supply chain network design under uncertainty. School of Industrial & Systems Engineering, Georgia Institute of Technology. 2003.
32. E. Kalvelagen. Two stage stochastic linear programming with GAMS. Amsterdam Optimization Modeling Group LLC, Washington DC. 2003.