Simulation-based multi-objective model for supply chains with disruptions in transportation

Robotics and Computer-Integrated Manufacturing

Volumn 43, Issue , 2017, Pages 39-49

  Chávez, Hernán ^a   Castillo Villar, Krystel K ^a   Herrera, Luis ^b   Bustos, Agustín ^c  

^a The University of Texas at San Antonio   (United States)

^b SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

^c INSTITUTO MEXICANO DEL TRANSPORTE   (Mexico)

Author keywords

Minimum cost flow; Resilient supply chains; Simulated annealing; Simulation optimization; Stochastic multi objective optimization

Indexed keywords

AGRICULTURAL PRODUCTS; CHAINS; COST ACCOUNTING; COSTS; SIMULATED ANNEALING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SUPPLY CHAINS;

MINIMUM COST FLOW PROBLEM; MINIMUM COST FLOWS; MULTI-OBJECTIVE MODELING; ROAD TRANSPORTATION; ROUTING STRATEGIES; SIMULATION OPTIMIZATION; STOCHASTIC TRANSPORTATION; TRANSPORTATION PROBLEM;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84955478178     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2015.12.008     Document Type: Article

References (32)

1. [1] C. Martner Peyrelongue, C. Morales Pérez, M. De La Torre Romero, A. Bustos Rosales, Cadenas Logisticas de Exportación en Méxcio: Piña Fresca, Generadores Eléctricos, Refrigeradores Instituto Mexicano del Transporte. Technical Report (No. 276), 2005.
2. [2] C. Morales Pérez, M. De La Torre Moreno, Característcias del Transporte Refrigerado en México Instituto Mexicano del Transporte. Technical Report (No. 297), 2006.
3. [3] JOC, US-Mexico Cross-Border Truck, Rail Traffic Up in May. from 〈 http://www.joc.com/international-trade-news/trade-data/united-states-trade-data/us-mexico-cross-border-truck-rail-traffic-may_20130805.html〉, 2013.
4. [4] G. Del Castillo, A. Peschard-Sverdrup, N. Fuentes, A. Corrales, A. Bruges, V. Barraza, Estudio de Puertos de Entrada México-Estados Unidos: Analisis de Capacidades y Recomendaciones para incrementar su eficiencia (Capacities from United States Mexico Border Ports and Recommendations to increase their Efficiency) Peschard Sverdrup International: El Colegio de la Frontera Norte, 2007.
5. [5] Wang, Z., Lin, L., A Simulation-Based Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Travel Times. J. Appl. Math., 2013 (2013).
6. [6] Bustos, A., Herrera, L., Jiménez, E., Efficient Frontier for Multi-Objective Stochastic Transportation Networks in International Market of Perishable Goods. J. Appl. Res. Technol. 12:4 (2014), 654–665.
7. [7] RMOC, Freight Rate Index Historical Data from 〈http://www.freightrateindex.com/index_files/page0014.htm〉, 2014.
8. [8] Sedeño-Noda, A., González-Martın, C., The biobjective minimum cost flow problem. Eur. J. Oper. Res. 124:3 (2000), 591–600.
9. [9] Przybylski, A., Gandibleux, X., Ehrgott, M., The biobjective integer minimum cost flow problem—incorrectness of Sedeño-Noda and Gonzàlez-Martin's algorithm. Comput. Oper. Res. 33:5 (2006), 1459–1463.
10. [10] Opasanon, S., Miller-Hooks, E., Multicriteria adaptive paths in stochastic, time-varying networks. Eur. J. Oper. Res. 173:1 (2006), 72–91.
11. [11] Fonseca, M., Figueira, J.R., Resende, M.G., Solving scalarized multi‐objective network flow problems using an interior point method. Int. Trans. Oper. Res. 17:5 (2010), 607–636.
12. [12] Eusébio, A., Figueira, J.R., Finding non-dominated solutions in bi-objective integer network flow problems. Comput. Oper. Res. 36:9 (2009), 2554–2564.
13. [13] Raith, A., Ehrgott, M., A two-phase algorithm for the biobjective integer minimum cost flow problem. Comput. Oper. Res. 36:6 (2009), 1945–1954.
14. [14] Talbi, E.-G., Metaheuristics: From Design to Implementation, Vol. 74, 2009, John Wiley & Sons, United States.
15. [15] Rangaiah, G.P., Multi-objective Optimization: Techniques and Applications in Chemical Engineering, Vol. 1, 2009, World Scientific, Singapore.
16. [16] M.C. Fu, Simulation optimization, in: Paper presented at the Proceedings of the 33nd conference on Winter simulation, IEEE Computer Society, 2001, pp. 53–61.
17. [17] H. Chavez, K.K. Castillo-Villar, A preliminary simulated annealing for resilience supply chains, in: Paper presented at the Computational Intelligence in Production and Logistics Systems (CIPLS), 2014 IEEE Symposium, IEEE, 2014, pp. 98–105.
18. [18] A.M. Law, M.G. McComas, Simulation-based optimization., Paper presented at the Proceedings of the 32nd conference on Winter simulation, Society for Computer Simulation International, 2000, pp. 46–49.
19. [19] J.R. Swisher, P.D. Hyden, S.H. Jacobson, L.W. Schruben, A survey of simulation optimization techniques and procedures, in: Proceedings of Paper presented at the Simulation Conference, 2000, Winter IEEE, Vol. 1, pp. 119–128, 2000.
20. [20] Jacobson, S.H., Schruben, L.W., Techniques for simulation response optimization. Oper. Res. Lett. 8:1 (1989), 1–9.
21. [21] Y. Carson, A. Maria, Simulation optimization: methods and applications, in: Paper presented at the Proceedings of the 29th conference on Winter simulation, IEEE Computer Society, 1997, pp. 118–126.
22. [22] Haddock, J., Mittenthal, J., Simulation optimization using simulated annealing. Comput. Ind. Eng. 22:4 (1992), 387–395.
23. [23] Van Laarhoven, P.J., Aarts, E.H., Simulated Annealing, 1987, Springer, Netherlands, 7–15.
24. [24] Kirkpatrick, S., Vecchi, M., Optimization by simmulated annealing. Science 220:4598 (1983), 671–680.
25. [25] D.H. Wolpert, W.G. Macready, No free lunch theorems for search: Technical Report SFI-TR-95-02-010, Santa Fe Institute, 1995.
26. [26] Geman, S., Geman, D., Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6 (1984), 721–741.
27. [27] Ackley, D.H., An empirical study of bit vector function optimization. Genet. Algorithms Sim. Annealing 1 (1987), 170–204.
28. [28] Wang, L., Zhang, L., Stochastic optimization using simulated annealing with hypothesis test. Appl. Math. Comput. 174:2 (2006), 1329–1342.
29. [29] G. Mavrotas, Generation of efficient solutions in Multiobjective Mathematical Programming problems using GAMS, Effective implementation of the ε-constraint method. Lecturer, Laboratory of Industrial and Energy Economics, School of Chemical Engineering. National Technical University of Athens, 2007.
30. [30] Ruhe, G., Complexity results for multicriterial and parametric network flows using a pathological graph of Zadeh. Z. Oper. Res. 32:1 (1988), 9–27, 10.1007/BF01920568.
31. [31] Ehrgott, M., Multicriteria Optimization. 2005, Springer Science & Business Media, United States.
32. [32] A. Suppapitnarm, K.A. Seffen, G.T. Parks, A.M. Connor, P.J. Clarkson, Multiobjective Optimisation of Bicycle Frames using Simulated Annealing, 1999.