An integrated relief network design model under uncertainty: A case of Iran

Abstract In this study, a scenario-based robust approach is suggested for a multi-objective mixed-integer linear programming model in designing the relief network. A new approach to humanitarian inventory grouping problem based on the relief management objectives is proposed. The proposed model simultaneously optimizes inventory groups’ number and corresponding service levels, assignment of relief commodities to groups, relief facility location, and relief service assignment. The proposed model aims to minimize the risk and the total cost of network management and simultaneously maximize the network population coverage. The fault tree analysis technique is used for vulnerability assessment of each demand point. To tackle the proposed optimization model, a hybrid Taguchi-based non-dominated sorting genetic algorithm-II is developed that incorporates an enhanced variable decomposition neighborhood search algorithm with fitness landscape analysis as its local search heuristic. The results illustrate the efficiency of the proposed model and solution algorithm in dealing with the considered disaster management issues.

[1]  K. Chandra Sekaran,et al.  Improved NSGA-II Based on a Novel Ranking Scheme , 2010, ArXiv.

[2]  Pinar Keskinocak,et al.  Pre-Positioning of Emergency Items for CARE International , 2011, Interfaces.

[3]  Julio Ortega Lopera,et al.  A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows , 2013, Comput. Ind. Eng..

[4]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[5]  Mitsuo Gen,et al.  A steady-state genetic algorithm for multi-product supply chain network design , 2009, Comput. Ind. Eng..

[6]  Manoj Kumar Tiwari,et al.  Performance measures based optimization of supply chain network resilience: A NSGA-II + Co-Kriging approach , 2016, Comput. Ind. Eng..

[7]  Karl F. Doerner,et al.  Risk approaches for delivering disaster relief supplies , 2011, OR Spectr..

[8]  Maged M. Dessouky,et al.  Solution approaches for facility location of medical supplies for large-scale emergencies , 2007, Comput. Ind. Eng..

[9]  Bahar Yetis Kara,et al.  Locating temporary shelter areas after an earthquake: A case for Turkey , 2015, Eur. J. Oper. Res..

[10]  Hadi Mokhtari,et al.  Redesign strategies of a comprehensive robust relief network for disaster management , 2018, Socio-Economic Planning Sciences.

[11]  James E. Ward,et al.  A Partial Covering Approach to Siting Response Resources for Major Maritime Oil Spills , 1984 .

[12]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[13]  Chung-Cheng Lu,et al.  Robust weighted vertex p-center model considering uncertain data: An application to emergency management , 2013, Eur. J. Oper. Res..

[14]  B. Wisner,et al.  Environmental Health in Emergencies and Disasters: A Practical Guide , 2003 .

[15]  Shaligram Pokharel,et al.  Optimization models in emergency logistics: A literature review , 2012 .

[16]  Joseph Sarkis,et al.  Integrated aggregate supply chain planning using memetic algorithm – A performance analysis case study , 2013 .

[17]  Han-Lin Li,et al.  A robust optimization model for stochastic logistic problems , 2000 .

[18]  I. Kim,et al.  Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation , 2006 .

[19]  Satish V. Ukkusuri,et al.  Location Routing Approach for the Humanitarian Prepositioning Problem , 2008 .

[20]  Henry H. Willis,et al.  Estimating Terrorism Risk , 2002 .

[21]  Alev Taskin Gumus,et al.  A stochastic location and allocation model for critical items to response large-scale emergencies: A case of Turkey , 2016 .

[22]  Pierre Hansen,et al.  Variable Neighborhood Decomposition Search , 1998, J. Heuristics.

[23]  Philip C. Jones,et al.  Prepositioning supplies in preparation for disasters , 2011, Eur. J. Oper. Res..

[24]  Jyh-Horng Chou,et al.  Hybrid Taguchi-based genetic algorithm for flowshop scheduling problem , 2013 .

[25]  Nezir Aydin,et al.  A stochastic mathematical model to locate field hospitals under disruption uncertainty for large-scale disaster preparedness , 2016 .

[26]  Benita M. Beamon,et al.  Facility location in humanitarian relief , 2008 .

[27]  Mark A. Turnquist,et al.  Pre-positioning of emergency supplies for disaster response , 2010 .

[28]  Chung-Cheng Lu,et al.  Robust vertex p-center model for locating urgent relief distribution centers , 2013, Comput. Oper. Res..

[29]  Karl F. Doerner,et al.  Multi-criteria location planning for public facilities in tsunami-prone coastal areas , 2009, OR Spectr..

[30]  Amirhossein Khosrojerdi,et al.  Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study , 2016 .

[31]  Michael J. Widener,et al.  A hierarchical approach to modeling hurricane disaster relief goods distribution , 2011 .

[32]  D. Mcloughlin.  A Framework for Integrated Emergency Management , 1985 .

[33]  Satish V. Ukkusuri,et al.  A Voronoi-Based Heuristic Algorithm for Locating Distribution Centers in Disasters , 2012 .

[34]  Raha Akhavan-Tabatabaei,et al.  OR models with stochastic components in disaster operations management: A literature survey , 2015, Comput. Ind. Eng..

[35]  İbrahim Akgün,et al.  Risk based facility location by using fault tree analysis in disaster management , 2015 .

[36]  Mahdi Bashiri,et al.  An efficient network for disaster management: Model and solution , 2016 .

[37]  Rajan Batta,et al.  Review of recent developments in OR/MS research in disaster operations management , 2013, Eur. J. Oper. Res..

[38]  Seyed Hessameddin Zegordi,et al.  A Robust Competitive Global Supply Chain Network Design under Disruption: The Case of Medical Device Industry , 2015 .