Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution

Computers and Industrial Engineering

Volumn 99, Issue , 2016, Pages 41-62

  Maghsoudlou, Hamidreza ^a   Kahag, Mahdi Rashidi ^a   Niaki, Seyed Taghi Akhavan ^b   Pourvaziri, Hani ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

AHP TOPSIS method; Disruption cost; Multi echelon supply chains; Multi objective particle swarm optimization; Queuing systems

Indexed keywords

ALGORITHMS; ANALYTIC HIERARCHY PROCESS; GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; HIERARCHICAL SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION; PARTICLE SWARM OPTIMIZATION (PSO); QUEUEING NETWORKS; SUPPLY CHAINS; TAGUCHI METHODS;

BI-OBJECTIVE OPTIMIZATION; EPSILON-CONSTRAINT METHOD; IMPERIALIST COMPETITIVE ALGORITHMS; MULTI ECHELON SUPPLY CHAINS; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; NON DOMINATED SORTING GENETIC ALGORITHM (NSGA II); QUEUING SYSTEMS; TOPSIS METHOD;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84978410131     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2016.07.008     Document Type: Article

References (52)

1. Ahmadi-Javid, A., Seddighi, A.H., A location-routing problem with disruption risk. Transportation Research Part E: Logistics and Transportation Review 53 (2013), 63–82.
2. Akhavian, R., Behzadan, A.H., Evaluation of queuing systems for knowledge-based simulation of construction processes. Automation in Construction 47 (2014), 37–49.
3. Al Jadaan, O., Rao, C.R., Rajamani, L., Non-dominated ranked genetic Algorithm for solving multi-objective optimization problems: NRGA. Journal of Theoretical and Applied Information Technology 4 (2008), 60–67.
4. Amin, S.H., Zhang, G., A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling 37 (2013), 4165–4176.
5. Asefi, H., Jolai, F., Rabiee, M., Tayebi Araghi, M.E., A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem. International Journal of Advanced Manufacturing Technology 75 (2014), 1017–1033.
6. Atashpaz-Gargari, E., Lucas, C., Imperialist competitive algorithm: An algorithm for optimization inspired by imperialist competitive. IEEE congress on evolutionary computation, Singapore, 2007 25–28 September, 2007.
7. Baghalian, A., Rezapour, S., Farahani, R.Z., Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research 227 (2013), 199–215.
8. Bhaskar, V., Lallement, P., Modeling a supply chain using a network of queues. Applied Mathematical Modelling 34 (2010), 2074–2088.
9. Cao, E., Wan, C., Lai, M., Coordination of a supply chain with one manufacturer and multiple competing retailers under simultaneous demand and cost disruptions. International Journal of Production Economics 141 (2013), 425–433.
10. Cárdenas-Barrón, L.E., Treviño-Garza, G., An optimal solution to a three echelon supply chain network with multi-product and multi-period. Applied Mathematical Modelling 38 (2014), 1911–1918.
11. Chen, K., Shen, J., Feng, M., Disruptions management of a supply chain under strategic subsidy policy for the demand-stimulating inventory. Computers & Industrial Engineering 76 (2014), 169–182.
12. Cui, T., Ouyang, Y., Shen, Z.-J.M., Reliable facility location design under the risk of disruptions. Operations Research 58 (2010), 998–1011.
13. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2002), 182–197.
14. de Souza, R.M., Morabito, R., Chiyoshi, F.Y., Iannoni, A.P., Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil. European Journal of Operational Research 242 (2015), 274–285.
15. Eberhart, R.C., Kennedy, J., A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science, New York, NY, 1995, 39–43.
16. Ehrgott, M., Gandibleux, X., Multi-objective combinatorial optimization – Theory, methodology, and applications. 2002, State of the Art Annotated Bibliographic Surveys, Kluwer Academic Publishers, Multiple Criteria Optimization, 369–444.
17. Gedik, R., Medal, H., Rainwater, C., Pohl, E.A., Mason, S.J., Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application. Transportation Research Part E: Logistics and Transportation Review 71 (2014), 45–57.
18. Gross, D., Harris, C.M., Fundamental of queueing theory. 3rd ed., 1988, Wiley Inter-science, New York.
19. Hatefi, S.M., Jolai, F., Robust and reliable forward–Reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling 38 (2014), 2630–2647.
20. He, X., Hu, W., Wu, J.H., Wang, C., Improving emergency goods transportation performance in metropolitan areas under multi-echelon queuing conditions. Procedia – Social and Behavioral Sciences 96 (2013), 2466–2479.
21. Hu, X., Sun, L., Liu, L., A PAM approach to handling disruptions in real-time vehicle routing problems. Decision Support Systems 54 (2013), 1380–1393.
22. Huang, S., Liu, J., Yan, Y., Li, X., Queuing analysis of cooperative GBN-ARQ in wireless networks with peers contending for a common helper. AEU – International Journal of Electronics and Communications 68 (2014), 429–436.
23. Hwang, C., Yoon, K., Multiple attribute decision making: Methods and applications, A state of the art survey. 1981. 1981, Springer-Verlag, New York, NY.
24. Jain, S., Raghavan, N.S., A queuing approach for inventory planning with batch ordering in multi-echelon supply chains. Central European Journal of Operations Research 17 (2009), 95–110.
25. Karimi, N., Zandieh, M., Karamooz, H.R., Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications 37 (2010), 4024–4032.
26. Khatami, M., Mahootchi, M., Farahani, R.Z., Benders’ decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation Research Part E: Logistics and Transportation Review 79 (2015), 1–21.
27. Lemmens, S., Decouttere, C., Vandaele, N., Bernuzzi, M., A review of integrated supply chain network design models: Key issues for vaccine supply chains. Chemical Engineering Research & Design 109 (2016), 366–384.
28. Li, J., Wang, S., Cheng, T.C.E., Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption. International Journal of Production Economics 124 (2010), 137–150.
29. Madadi, A., Kurz, M.E., Mason, S.J., Taaffe, K.M., Supply chain design under quality disruptions and tainted materials delivery. Transportation Research Part E: Logistics and Transportation Review 67 (2014), 105–123.
30. Mohammad Nezhad, A., Mahlooji, H., A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems. Computers & Operations Research 38 (2011), 1164–1174.
31. Mu, Q., Eglese, R., Disrupted capacitated vehicle routing problem with order release delay. Annals of Operations Research 207 (2013), 201–216.
32. Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V.R., Efficiency of a multi-objective imperialist competitive algorithm: A bi objective location-routing-inventory problem with probabilistic routes. Journal of AI and Data Mining 2 (2014), 105–112.
33. Okabe, T., Jin, Y., Sendhoff, B., A critical survey of performance indices for multi-objective optimisation. The 2003 Congress on Evolutionary Computation, CEC ’03, Vol. 872, 2003, 878–885.
34. Pasandideh, S.H.R., Niaki, S.T.A., Asadi, K., Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences 292 (2015), 57–74.
35. Peng, P., Snyder, L.V., Lim, A., Liu, Z., Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological 45 (2011), 1190–1211.
36. Perron, S., Hansen, P., Le Digabel, S., Mladenović, N., Exact and heuristic solutions of the global supply chain problem with transfer pricing. European Journal of Operational Research 202 (2010), 864–879.
37. Phil, G., Risk management: A common framework for the entire organization. 2015, Butterworth-Heinemann.
38. Pishvaee, M.S., Razmi, J., Torabi, S.A., An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review 67 (2014), 14–38.
39. Qi, L., Shen, Z.-J.M., Snyder, L.V., The effect of supply disruptions on supply chain design decisions. Transportation Science 44 (2010), 274–289.
40. Rahmati, S.H.A., Hajipour, V., Niaki, S.T.A., A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing 13 (2013), 1728–1740.
41. Sarrafha, K., Rahmati, S.H.A., Niaki, S.T.A., Zaretalab, A., A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA. Computers & Operations Research 54 (2015), 35–51.
42. Satty, T.L., The analytic hierarchy process. 1980, McGraw-Hill New York, New York.
43. Sawik, T., Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing. Computers & Operations Research 51 (2014), 11–20.
44. Sever, D., Dellaert, N., van Woensel, T., de Kok, T., Dynamic shortest path problems: Hybrid routing policies considering network disruptions. Computers & Operations Research 40 (2013), 2852–2863.
45. Taguchi, G., Introduction to quality engineering: Designing quality into products and processes. 1st ed., 1986, Asian Productivity Organization, White Plains.
46. Teimoury, E., Modarres, M., Khondabi, I.G., Fathi, M., A queuing approach for making decisions about order penetration point in multiechelon supply chains. International Journal of Advanced Manufacturing Technology 63 (2012), 359–371.
47. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., Baboli, A., Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review 48 (2012), 1152–1168.
48. Vakharia, A.J., Yenipazarli, A., Managing supply chain disruptions. Foundations and Trends in Technology, Information and Operations Management 2 (2008), 243–325.
49. Wang, K.-J., Lee, C.H., A revised ant algorithm for solving location–Allocation problem with risky demand in a multi-echelon supply chain network. Applied Soft Computing 32 (2015), 311–321.
50. Wilson, M.C., The impact of transportation disruptions on supply chain performance. Transportation Research Part E: Logistics and Transportation Review 43 (2007), 295–320.
51. Wu, Y., Dong, M., Fan, T., Liu, S., Performance evaluation of supply chain networks with assembly structure under system disruptions. Computers & Operations Research 39 (2012), 3229–3243.
52. Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S., Multi-attribute decision making: A simulation comparison of selection methods. European Journal of Operational Research 107 (1998), 507–529.