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Post-disaster multi-period road network repair: work scheduling and relief logistics optimization

  • Applications of Operations Research (OR) in Disaster Relief Operations (DRO)-Part II
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Abstract

We develop a multi-period bi-level programming model for the post-disaster road network repair work scheduling and relief logistics problem. A maximum relative satisfaction degree-based steady-state parallel genetic algorithm is designed to solve this model. In order to validate and test the effectiveness of the presented mathematical model and method, we use a network generator to create numerical examples with different scales and characteristics of road network. Our numerical analysis of the solutions shows that the proposed mathematical model and method can effectively assist the decision-makers to deal with the road network repair work scheduling and relief logistics optimization problem during the emergency response phase. This mathematical model and the approach being developed are applied to deal with the case of Wenchuan earthquake in China. The results show that the required CPU time is short enough such that it meets the time limitation in the emergency response phase, and the strategy of road network repair scheduling will allow repair of the damaged roads to be completed before the end of the planning time horizon by 14.93%. Furthermore, the strategy of relief logistics can provide an efficient relief allocation and transportation path.

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References

  • Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location-transportation problem for disaster response. Computers and Operations Research, 41, 83–93.

    Google Scholar 

  • Afshar, A., & Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, 46(4), 327–338.

    Google Scholar 

  • Akbari, V., & Salman, F. S. (2017). Multi-vehicle prize collecting arc routing for connectivity problem. Computers and Operations Research, 82, 52–68.

    Google Scholar 

  • Akbari, V., & Salman, F. S. (2017). Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity. European Journal of Operational Research, 257(2), 625–640.

    Google Scholar 

  • Aksu, D. T., & Ozdamar, L. (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E: Logistics and Transportation Review, 61, 56–67.

    Google Scholar 

  • Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers and Industrial Engineering, 56(2), 521–537.

    Google Scholar 

  • Beresford, A., & Pettit, S. (2012). Humanitarian aid logistics: The wenchuan and haiti earthquakes compared. In Relief supply chain management for disasters: Humanitarian aid and emergency logistics, 1st ed. Hershey, PA: IGI Global

  • Berger, J., & Barkaoui, M. (2004). A parallel hybrid genetic algorithm for the vehicle routing problem with time windows. Computers and Operations Research, 31(12), 2037–2053.

    Google Scholar 

  • Berkoune, D., Renaud, J., Rekik, M., & Ruiz, A. (2012). Transportation in disaster response operations. Socio-Economic Planning Sciences, 46(1), 23–32.

    Google Scholar 

  • Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485–498.

    Google Scholar 

  • Bozorgi-Amiri, A., & Khorsi, M. (2016). A dynamic multi-objective location-routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. The International Journal of Advanced Manufacturing Technology, 85(5–8), 1633–1648.

    Google Scholar 

  • Camacho-Vallejo, J. F., González-Rodríguez, E., Almaguer, F. J., & González-Ramírez, R. G. (2015). A bi-level optimization model for aid distribution after the occurrence of a disaster. Journal of Cleaner Production, 105, 134–145.

    Google Scholar 

  • Çelik, M. (2016). Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions. Surveys in Operations Research and Management Science, 21(2), 47–61.

    Google Scholar 

  • Çelik, M., Ergun, Ö., & Keskinocak, P. (2015). The post-disaster debris clearance problem under incomplete information. Operations Research, 63(1), 65–85.

    Google Scholar 

  • Chang, M. S., & Li, D. C. (2010). A sampling-based approximation method applied to stochastic real-time emergency rehabilitation scheduling problem. International Journal of Intelligent Transportation Systems Research, 8(1), 42–55.

    Google Scholar 

  • Chang, S. E., & Nojima, N. (2001). Measuring post-disaster transportation system performance: The 1995 kobe earthquake in comparative perspective. Transportation Research Part A: Policy and Practice, 35(6), 475–494.

    Google Scholar 

  • Chen, Y. W., & Tzeng, G. H. (1999). A fuzzy multi-objective model for reconstructing the post-quake road-network by genetic algorithm. International Journal of Fuzzy Systems, 1(2), 85–95.

    Google Scholar 

  • Chen, Y., Tadikamalla, P. R., Shang, J., & Song, Y. (2017). Supply allocation: Bi-level programming and differential evolution algorithm for natural disaster relief. Cluster Computing. https://doi.org/10.1007/s10586-017-1366-6.

  • Cho, S. H., Jang, H., Lee, T., & Turner, J. (2014). Simultaneous location of trauma centers and helicopters for emergency medical service planning. Operations Research, 62(4), 751–771.

    Google Scholar 

  • Colson, B., Marcotte, P., & Savard, G. (2007). An overview of bilevel optimization. Annals of Operations Research, 153(1), 235–256.

    Google Scholar 

  • Duhamel, C., Santos, A. C., Brasil, D., Châtelet, E., & Birregah, B. (2016). Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations. Annals of Operations Research, 247(2), 693–713.

    Google Scholar 

  • Duque, P. A. M., Coene, S., Goos, P., Sörensen, K., & Spieksma, F. (2013). The accessibility arc upgrading problem. European Journal of Operational Research, 224(3), 458–465.

    Google Scholar 

  • Duque, P. M., & Sörensen, K. (2011). A grasp metaheuristic to improve accessibility after a disaster. OR Spectrum, 33(3), 525–542.

    Google Scholar 

  • Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2681-2.

  • Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700–709.

    Google Scholar 

  • Feng, C., & Wang, T. (2005). Seismic emergency rehabilitation scheduling for rural highways. Transportation Planning Journal, 34(2), 177–210.

    Google Scholar 

  • Feng, C. M., & Wang, T. C. (2003). Highway emergency rehabilitation scheduling in post-earthquake 72 hours. Journal of the 5th Eastern Asia Society for Transportation Studies, 5, 3276–3285.

    Google Scholar 

  • Ferrer, J. M., Martín-Campo, F. J., Ortuño, M. T., Pedraza-Martínez, A. J., Tirado, G., & Vitoriano, B. (2018). Multi-criteria optimization for last mile distribution of disaster relief aid: Test cases and applications. European Journal of Operational Research, 269(2), 501–515.

    Google Scholar 

  • Floyd, R. W. (1962). Algorithm 97: Shortest path. Communications of the ACM, 5(6), 345.

    Google Scholar 

  • Gama, M., Santos, B. F., & Scaparra, M. P. (2016). A multi-period shelter location-allocation model with evacuation orders for flood disasters. EURO Journal on Computational Optimization, 4(3–4), 299–323.

    Google Scholar 

  • Gutjahr, W. J., & Dzubur, N. (2016). Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 85, 1–22.

    Google Scholar 

  • Hasani, A., & Mokhtari, H. (2018). Redesign strategies of a comprehensive robust relief network for disaster management. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2018.01.003.

    Google Scholar 

  • Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management, 31(5), 262–280.

    Google Scholar 

  • Hong, J. D., Jeong, K. Y., & Feng, K. (2015). Emergency relief supply chain design and trade-off analysis. Journal of Humanitarian Logistics and Supply Chain Management, 5(2), 162–187.

    Google Scholar 

  • Ibri, S., Nourelfath, M., & Drias, H. (2012). A multi-agent approach for integrated emergency vehicle dispatching and covering problem. Engineering Applications of Artificial Intelligence, 25(3), 554–565.

    Google Scholar 

  • Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225–244.

    Google Scholar 

  • Karamlou, A., & Bocchini, P. (2014). Optimal bridge restoration sequence for resilient transportation networks. Structures Congress, 2014, 1437–1447.

    Google Scholar 

  • Kasaei, M., & Salman, F. S. (2016). Arc routing problems to restore connectivity of a road network. Transportation Research Part E: Logistics and Transportation Review, 95, 177–206.

    Google Scholar 

  • Klingman, D., Napier, A., & Stutz, J. (1974). Netgen: A program for generating large scale capacitated assignment, transportation, and minimum cost flow network problems. Management Science, 20(5), 814–821.

    Google Scholar 

  • Lenstra, J. K., & Kan, A. (1981). Complexity of vehicle routing and scheduling problems. Networks, 11(2), 221–227.

    Google Scholar 

  • Li, A. C., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 48(4), 715–729.

    Google Scholar 

  • Li, Q., Tu, W., & Zhuo, L. (2018). Reliable rescue routing optimization for urban emergency logistics under travel time uncertainty. ISPRS International Journal of Geo-Information, 7(2), 77–97.

    Google Scholar 

  • Liberatore, F., Ortuño, M. T., Tirado, G., Vitoriano, B., & Scaparra, M. P. (2014). A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics. Computers and Operations Research, 42, 3–13.

    Google Scholar 

  • Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socio-Economic Planning Sciences, 46(2), 112–123.

    Google Scholar 

  • Maharjan, R., & Hanaoka, S. (2018). A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 8(1), 2–21.

    Google Scholar 

  • Manopiniwes, W., & Irohara, T. (2017). Stochastic optimisation model for integrated decisions on relief supply chains: Preparedness for disaster response. International Journal of Production Research, 55(4), 979–996.

    Google Scholar 

  • Maya-Duque, P. A., Dolinskaya, I. S., & Sörensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285.

    Google Scholar 

  • Mejia-Argueta, C., Gaytán, J., Caballero, R., Molina, J., & Vitoriano, B. (2018). Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods. International Transactions in Operational Research, 25(3), 1053–1079.

    Google Scholar 

  • Mimura, N., Yasuhara, K., Kawagoe, S., Yokoki, H., & Kazama, S. (2011). Damage from the great east japan earthquake and tsunami-a quick report. Mitigation and Adaptation Strategies for Global Change, 16(7), 803–818.

    Google Scholar 

  • Mollah, A. K., Sadhukhan, S., Das, P., & Anis, M. Z. (2017). A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2017.11.018.

    Google Scholar 

  • 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.

    Google Scholar 

  • Noyan, N., & Kahvecioğlu, G. (2018). Stochastic last mile relief network design with resource reallocation. OR Spectrum, 40(1), 187–231.

    Google Scholar 

  • Özdamar, L., Aksu, D. T., & Ergüneş, B. (2014). Coordinating debris cleanup operations in post disaster road networks. Socio-Economic Planning Sciences, 48(4), 249–262.

    Google Scholar 

  • Ozdamar, L., Aksu, D. T., Yasa, E., & Ergunes, B. (2018). Disaster relief routing in limited capacity road networks with heterogeneous flows. Journal of Industrial & Management Optimization, 12(5), 327–338. https://doi.org/10.3934/jimo.2018011.

    Article  Google Scholar 

  • Özdamar, L., & Demir, O. (2012). A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transportation Research Part E: Logistics and Transportation Review, 48(3), 591–602.

    Google Scholar 

  • Özdamar, L., & Ertem, M. A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65.

    Google Scholar 

  • PAHO: Natural disasters: Protecting the public’s health. Tech. rep., Pan American Health Organization (2000)

  • Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location-routing problem in disaster relief. Computers and Operations Research, 42, 25–39.

    Google Scholar 

  • Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological, 44(4), 521–534.

    Google Scholar 

  • Ren, X., Zhu, J., & Huang, J. (2012). Multi-period dynamic model for emergency resource dispatching problem in uncertain traffic network. Systems Engineering Procedia, 5, 37–42.

    Google Scholar 

  • Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Bozorgi-Amiri, A. (2016). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Computers and Industrial Engineering, 94, 201–215.

    Google Scholar 

  • Safaei, A. S., Farsad, S., & Paydar, M. M. (2018). Emergency logistics planning under supply risk and demand uncertainty. Operational Research. https://doi.org/10.1007/s12351-018-0376-3.

  • Sato, T., & Ichii, K. (1996). Optimization of post-earthquake restoration of lifeline networks using genetic algorithms. In Proceedings Japan Society of Civil Engineers (Vol. 537, pp. 245–256). Dotoku Gakkai. https://doi.org/10.2208/jscej.1996.537_245.

  • Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 21–28.

    Google Scholar 

  • Sheu, J. B. (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E: Logistics and Transportation Review, 43(6), 687–709.

    Google Scholar 

  • Tamura, T., Sugimoto, H., & Kamimae, T. (1994). Application of genetic algorithms to determining priority of urban road improvement. In Proceedings Japan society of civil engineers, pp. 37–37. DOTOKU GAKKAI

  • Tang, C. H., Yan, S., & Chang, C. W. (2009). Short-term work team scheduling models for effective road repair and management. Transportation Planning and Technology, 32(3), 289–311.

    Google Scholar 

  • Tayal, A., Gunasekaran, A., Singh, S. P., Dubey, R., & Papadopoulos, T. (2017). Formulating and solving sustainable stochastic dynamic facility layout problem: A key to sustainable operations. Annals of Operations Research, 253(1), 621–655.

    Google Scholar 

  • Tayal, A., & Singh, S. (2014). Chaotic simulated annealing for solving stochastic dynamic facility layout problem. Journal of International Management Studies, 14(2), 67–74.

    Google Scholar 

  • Tayal, A., & Singh, S. P. (2016). Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem. Annals of Operations Research. https://doi.org/10.1007/s10479-016-2237-x.

    Google Scholar 

  • Teo, K. L., & Yang, X. (2001). Portfolio selection problem with minimax type risk function. Annals of Operations Research, 101(1–4), 333–349.

    Google Scholar 

  • Trunick, P. (2005). Special report: delivering relief to tsunami victims. Logistics Today, 46(2), 1–3.

    Google Scholar 

  • Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30(3), 835–854. https://doi.org/10.1007/s00521-016-2696-7.

    Article  Google Scholar 

  • Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units. European Journal of Operational Research, 235(3), 697–708.

    Google Scholar 

  • Wohlgemuth, S., Oloruntoba, R., & Clausen, U. (2012). Dynamic vehicle routing with anticipation in disaster relief. Socio-Economic Planning Sciences, 46(4), 261–271.

    Google Scholar 

  • Yahyaei, M., & Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: An integration of shelter and supply facility location. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2758-6.

  • Yan, S., & Shih, Y. L. (2009). Optimal scheduling of emergency roadway repair and subsequent relief distribution. Computers and Operations Research, 36(6), 2049–2065.

    Google Scholar 

  • Yan, S., & Shih, Y. L. (2012). An ant colony system-based hybrid algorithm for an emergency roadway repair time-space network flow problem. Transportmetrica, 8(5), 361–386.

    Google Scholar 

  • Yu, L., Yang, H., Miao, L., & Zhang, C. (2017). Rollout algorithms for resource allocation in humanitarian logistics. IISE Transactions,. https://doi.org/10.1080/24725854.2017.1417655.

    Article  Google Scholar 

  • Yu, L., Zhang, C., Yang, H., & Miao, L. (2018). Novel methods for resource allocation in humanitarian logistics considering human suffering. Computers and Industrial Engineering, 119, 1–20.

    Google Scholar 

  • Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty. Computers and Industrial Engineering, 74, 139–148.

    Google Scholar 

  • Zhan, Sl, Liu, N., & Ye, Y. (2014). Coordinating efficiency and equity in disaster relief logistics via information updates. International Journal of Systems Science, 45(8), 1607–1621.

    Google Scholar 

  • Zhang, W., Wang, N., & Nicholson, C. (2017). Resilience-based post-disaster recovery strategies for road-bridge networks. Structure and Infrastructure Engineering, 13(11), 1404–1413.

    Google Scholar 

  • Zhao, M., & Liu, X. (2018). Development of decision support tool for optimizing urban emergency rescue facility locations to improve humanitarian logistics management. Safety Science, 102, 110–117.

    Google Scholar 

  • Zheng, Y. J., & Ling, H. F. (2013). Emergency transportation planning in disaster relief supply chain management: A cooperative fuzzy optimization approach. Soft Computing, 17(7), 1301–1314.

    Google Scholar 

  • Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95.

    Google Scholar 

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Acknowledgements

This paper is supported by National Natural Science Foundation of China (Grant No. 71502059), Hunan Provincial Natural Science Foundation of China (Grant No. 2016JJ3091), and Educational Department of Hunan Province of China (Grant No. 16B169). Additionally, this work is partially sponsored by China Scholarship Council (No. 201706725018). Special thanks are due to this work is supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia, and the Laboratory High-Performance Computing and Stochastic Information Processing (HPCSIP) of Hunan Normal University, Changsha, China.

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  1. Business School, Hunan Normal University, Changsha, Hunan, 410081, China

    Shuanglin Li

  2. School of Electrical Engineering, Computing and Mathematical Science, Curtin University, Perth, 6845, Australia

    Shuanglin Li & Kok Lay Teo

  3. Coordinated Innovation Center for Computable Modeling in Management Science, Tianjin University of Finance and Economics, N0. 25, Zhujiang Road, Hexi District, Tianjin, 300222, China

    Kok Lay Teo

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Appendix A: Detail information for case study

Appendix A: Detail information for case study

The detail information for supply depots and demand nodes are shown in Table 16, where \(S_{m}^{\tau }\) is determined by the capacity of the distribution center \(m \in N_{s}^{\tau }\) and \(D_{n}^{\tau }\) is determined by the number of people in accordance with the Statistical Yearbook 2008, Sichuan, and the percentage of the collapsed houses that can be predicted by satellite images, infrared and aerial photography technology. In addition, the traverse time for each road/link is available from google earth.

Table 16 Supply nodes and demand nodes
Full size table

The damaged nodes detail information are shown in Table 17, where \(s_{d}, d \in N_{r}^{\tau }\) is the required repair time in minutes, which is forecasted using the technology of infrared or aerial photography. Namely, we first get the length of destroyed roads/links each of the damaged nodes by using the technology of infrared or aerial photography, then it is divided by the capability of each repair crew. In this paper, we assume that the capability of each of the repair crew is the same and equals to 0.5 Km/h.

Table 17 The damaged nodes detail information
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In Table 18, the number of vehicles and the occupied OD are obtained from the data of Public Road Bureau, Department of Transportation of Sichuan Province, China.

Table 18 Emergency relief vehicle information
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Li, S., Teo, K.L. Post-disaster multi-period road network repair: work scheduling and relief logistics optimization. Ann Oper Res 283, 1345–1385 (2019). https://doi.org/10.1007/s10479-018-3037-2

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