A New Approach for Solving Multi-layered Facility Location Models under Uncertainty Using fuzzy Simulation

Subject Areas : Industrial Management

Mahdi Yousefi Nejad Attari ¹ , Saeed Kolahi-Randji ² , Ensiyeh Neyshabouri Jami ³

1 - Department of industrial engineering, Azad University, Bonab branch, Bonab, Iran
2 - Young Researchers and Elite Club, Ilkhichi Branch, Islamic Azad University, Ilkhichi, Iran
3 - Associate Prof., Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran

Received: 2016-09-03 Accepted : 2018-01-18 Published : 2017-01-23

Keywords:

Abstract :

Different systems have complex behaviors associated with the uncertainty of an important loss issue. Integration of discrete event systems in order to incorporate the uncertainty presented with the theory of fuzzy collections. Multi-layer facility location models have complex behavior. In this model, customers receive different services in different layers addressing. The study seeks to provide a facility location models with multiple layers of serving and taking into account the density of the system. The proposed model is for a fuzzy nine linear programming model and it is placed in the field of highly complex issues. In order to solve the mathematical model, fuzzy simulation approaches have been used. In this regard, the applicant is to facilitate functions such as minimizing the travel and waiting time in the queue is the applicant. It is noted that after the implementation, the basic models and scenarios created and Arena software results of fuzzy been ranked.

References:

1. Aboolian, R., & Berman, O., & Drezner, Z. (2009). The multiple server center location problem.Annals of Operations Research, 167, 337–352.


2. Abouee-Mehrizi, H., & Babri, S., & Berman, O., & Shavandi, H. (2011). Optimizing capacity, pricing and location decisions on a congested network with balking. Mathematical Methods of Operations Research, 74(2), 233-255.


3. Azzaro-Pantel, C., & Floquet, P., & Pibouleau, L., & Domenech, S. (1997). A fuzzy approach for performance modeling in a batch plant: application to semiconductor manufacturing. IEEE Transactions on Fuzzy Systems, 5(3).


4. Berman, O., & Krass, D., & Wang, J. (2006). Locating service facilities to reduce lost demand.IIE Transactions, 38(11), 933 – 946.


5. Berman, O., & Larson, R.C., & Chiu, S.S., (1985). Optimal Server Location on A Network Operating as an M/G/1 Queue.Operations Research, 33(4), 746-771.


6. Berman, O., & Tong, D., & Krass, D., (2010a). Pricinglocation and capacity planning with elastic demand and congestion. Working paper. University of Toronto.


7. Berman, O., Tong, D., & Krass, D. (2010b). Pricing, location and capacity planning with equilibrium driven demand and congestion working paper. University of Toronto.


8. Berman, O., (2007). Locating capacitated facilities to maximize captured demand. IIE Transactions, 39(11), 1015-1029.


9. Boffey, B., & Galvao, R., & Espejo, L. (2007). A review of congestion models in the location of facilities with immobile servers. European Journal of Operational Research, 178(3), 643–662.


10. Brandeau, M. L., & Chiu, S. S. (1990). A unified family of single-server queueing location models. Operations Research, 38(6), 1034-1044.


11. Chambari, A.H., & Rahmaty, S.H., & Hajipour, V., & Karimi, A. (2011). A bi-objective model for location-allocation problem within queuing framework. World Academy of Science. Engineering and Technology, 5(6), 138-145.


12. Current, J., & Daskin, M., & Schilling, D. (2002). Discrete network location models. facility location: applications and theory. Springer, Heidelberg, 80-118.


13. Dong, M., & Wua, C., & Hou, F. (2009). Shortest path based simulated annealing algorithm for dynamic facility layout problem under dynamic business environment. Expert Systems with Applications, 36(8), 11221–11232.


14. Farahani, R. Z., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689-1709.


15. Hakimi, S.L. (1964). Optimum locations of switching centers and the absolute centers andmedians of a graph.Operation Research, 12(3), 450–459.


16. Hodgson, M.J., & Berman, O. (1997). A billboard location model, Geographical and Environmental Modeling 1, 25–43.


17. Grieco, A. & Nucci, F. & Anglani, A. (2003). Representation of fuzzy time variables in discrete event simulation. Integrated Computer-Aided Engineering, 10(4), 305-318.


18. Love,  R.L., & Morris, J.G., & Wesolowsky, G.O. (1988). Facility Location: Models and Methods. North-Holland Publishing Company, New York, (Cited by Francis).


19. Marianov, V., & Revelle, C. (1995). Siting emergency services in Facility Location: A Survey of Applications and Methods. Springer Series in Operations Research.


20. Marianov, V., & Rios, M,. (2000). A probabilistic quality of service constraint for a location model of switches in ATM Communications networks.Annals of Operations Research, 96, 237–243.


21. Mehdizadeh, E., & Tavarroth, M.R., & Hajipour, V. (2011). A New hybrid Algorithm to Optimize Stochastic-Fuzzy Capacitated Multi-Facility Location-Allocation Problem. Journal of Industrial Engineering Islamic Azad University of Qazvin, 4(7),71–80.


22. Melo, M.T., & Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401–412.


23. Pasandideh, S.H.R., & Niaki, S.D.A. (2010). Genetic application in a facility location problem with random demand within queuing framework.Journal of Intelligent Manufacturing, 23(3), 234-546.


24. Pasandideh, S.H.R., & Niaki, STA, & Hajipour, V. (2013).  A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24(2), 331-348.


25. Shanthikumar, J.G., & Yao, D.D., (1987). Optimal Server Allocation in a System of Multi- Server Stations.Management Science, 33(9), 1173-1180.


26. Shavandi, H., & Mahlooji, H. (2006). A Fuzzy Queuing Location Model with A Genetic Algorithm Congested Systems. Applied Mathematics and Computation, 181(1), 440 -456.


27. Syam, S.S. (2008). A multiple server location–allocation model for service system design.Computers and Operations Research, 35(7), 2248–2265.


28. Wang, D., & Fung, R., & Ip, W. (2009). An immune-genetic algorithm for introduction planning of new products. Computers & Industrial Engineering, 56(3), 902–917.


29. Wang, Q., & Batta, R., & Rump, C. (2002). Algorithms for a facility location problem with stochastic customer demand and immobile servers. Annals of Operations Research, 111, 17–34.


30. Wang, Q., & Batta, R., & Rump, C. (2004). Facility Location Models for Immobile Servers with Stochastic Demand. Naval Research Logistic, 51(1), 137 - 152.


31. Zarrinpoor, N., & Seifbarghy, M. (2011). A competitive location model to obtain a specific market share while ranking facilities by shorter travel time. The International Journal of Advanced Manufacturing Technology, 55(5), 807-816.