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Growing Science » International Journal of Industrial Engineering Computations » A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 7 Issue 4 pp. 649-670 , 2016

A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty Pages 649-670 Right click to download the paper Download PDF

Authors: Maryam Rahafrooz, Mahdi Alinaghian

DOI: 10.5267/j.ijiec.2016.3.001

Keywords: Disaster relief Logistics, Relief facility location, Uncertainty, Chance constrained possibilistic programming, Robust optimization, Multi-objective optimization

Abstract: In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total logistics costs. To effectively cope with the uncertainties of the after-disaster environment, the uncertain parameters of the proposed model are considered in the form of fuzzy trapezoidal numbers. The proposed model not only considers relief commodities priority and demand points priority in relief distribution, but also considers the difference between the pre-disaster and post-disaster supply abilities of the suppliers. In order to solve the proposed model, the LP-metric and the improved augmented ε-constraint methods are used. Second, a set of test problems are designed to evaluate the effectiveness of the proposed robust model against its equivalent deterministic form, which reveales the capabilities of the robust model. Finally, to illustrate the performance of the proposed robust model, a seismic region of northwestern Iran (East Azerbaijan) is selected as a case study to model its relief logistics in the face of future earthquakes. This investigation indicates the usefulness of the proposed model in the field of crisis.

How to cite this paper
Rahafrooz, M & Alinaghian, M. (2016). A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty.International Journal of Industrial Engineering Computations , 7(4), 649-670.

Refrences
Belardo, S., Harrald, J., Wallace, W. A., & Ward, J. (1984). A partial covering approach to siting response resources for major maritime oil spills. Management Science, 30(10), 1184-1196.
Ben-Tal, A., & Nemirovski, A. (2002). Robust optimization–methodology and applications. Mathematical Programming, 92(3), 453-480.
Ben-Tal, A., & Nemirovski, A. (2008). Selected topics in robust convex optimization. Mathematical Programming, 112(1), 125-158.
Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, 60(1-4), 357-371.
Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum, 35(4), 905-933.
Bhattacharya, U., Rao, J. R., & Tiwari, R. N. (1992). Fuzzy multi-criteria facility location problem. Fuzzy Sets and Systems, 51(3), 277-287.
Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13.
Dessouky, M., Ordonez, F., Jia, H., & Shen, Z. (2006). Rapid distribution of medical supplies. In Patient Flow: Reducing Delay in Healthcare Delivery (pp. 309-338). Springer US.
Chang, M. S., Tseng, Y. L., & Chen, J. W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 43(6), 737-754.
Darzentas, J. (1987). A discrete location model with fuzzy accessibility measures. Fuzzy Sets and Systems, 23(1), 149-154.
Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy sets and systems, 24(3), 279-300.
Duran, S., Gutierrez, M. A., & Keskinocak, P. (2011). Pre-positioning of emergency items for CARE international. Interfaces, 41(3), 223-237.
Haghani, A., & Afshar, A. (2009). Supply chain management in disaster response.
Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy sets and Systems, 47(1), 81-86.
Hoseinpour, M., & Zare, M. (2009). Seismic Hazard Assessment of Tabriz, a City in the Northwest of Iran.
Horner, M. W., & Downs, J. A. (2010). Optimizing hurricane disaster relief goods distribution: model development and application with respect to planning strategies. Disasters, 34(3), 821-844.
Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems, 111(1), 3-28.
Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE transactions, 39(1), 41-55.
Kongsomsaksakul, S., Yang, C., & Chen, A. (2005). Shelter location-allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 6, 4237-4252.
Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management, 37(2), 99-114.
Luis, E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Disaster relief routing: Integrating research and practice. Socio-economic planning sciences, 46(1), 88-97.
Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
McCall, V. M. (2006). Designing and pre-positioning humanitarian assistance pack-up kits (HA PUKs) to support pacific fleet emergency relief operations. NAVAL POSTGRADUATE SCHOOL MONTEREY CA.
Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1), 76-84.
Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Springer Science & Business Media.
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 49(1), 217-249.
Nolz, P. C., Doerner, K. F., Gutjahr, W. J., & Hartl, R. F. (2010). A bi-objective metaheuristic for disaster relief operation planning. In Advances in multi-objective nature inspired computing (pp. 167-187). Springer Berlin Heidelberg.
Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111(3), 423-447.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
Psaraftis, H. N., Tharakan, G. G., & Ceder, A. (1986). Optimal response to oil spills: the strategic decision case. Operations Research, 34(2), 203-217.
Rao, J. R., & Saraswati, K. (1988). Facility location problem on a network under multiple criteria—fuzzy set theoretic approach.
Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: Methodological, 44(4), 521-534.
Rubin, C. B., Saperstein, M. D., & Barbee, D. G. (1985). Community recovery from a major natural disaster.
Sherali, H. D., Carter, T. B., & Hobeika, A. G. (1991). A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transportation Research Part B: Methodological, 25(6), 439-452.
Soltani, R., Sadjadi, S. J., & Tavakkoli-Moghaddam, R. (2015). Entropy based redundancy allocation in series-parallel systems with choices of a redundancy strategy and component type: A multi-objective model. Appl. Math, 9(2), 1049-1058.
Steuer, R. E. (1986). Multiple criteria optimization: theory, computation, and applications. Wiley.
Zhou, J., & Liu, B. (2007). Modeling capacitated location–allocation problem with fuzzy demands. Computers & Industrial Engineering, 53(3), 454-468.
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Journal: International Journal of Industrial Engineering Computations | Year: 2016 | Volume: 7 | Issue: 4 | Views: 3004 | Reviews: 0

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