This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a bi-objective mixed-integer programming (BOMIP) model is proposed to assign and schedule the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents. The second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to be more similar to the real world, time windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position of the corresponding relief team and the fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the Pareto solution in a reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large-size test problems. Finally, the sensitivity analysis and the managerial suggestions are provided to investigate the impact of some parameters on the Pareto frontier.
Keywords: Disaster management, fatigue effect, time window, multi-objective metaheuristic algorithms
@article{RO_2021__55_6_3399_0,
author = {Nayeri, Sina and Asadi-Gangraj, Ebrahim and Emami, Saeed and Rezaeian, Javad},
title = {Designing a bi-objective decision support model for the disaster management},
journal = {RAIRO. Operations Research},
pages = {3399--3426},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {6},
doi = {10.1051/ro/2021144},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021144/}
}
TY - JOUR AU - Nayeri, Sina AU - Asadi-Gangraj, Ebrahim AU - Emami, Saeed AU - Rezaeian, Javad TI - Designing a bi-objective decision support model for the disaster management JO - RAIRO. Operations Research PY - 2021 SP - 3399 EP - 3426 VL - 55 IS - 6 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021144/ DO - 10.1051/ro/2021144 LA - en ID - RO_2021__55_6_3399_0 ER -
%0 Journal Article %A Nayeri, Sina %A Asadi-Gangraj, Ebrahim %A Emami, Saeed %A Rezaeian, Javad %T Designing a bi-objective decision support model for the disaster management %J RAIRO. Operations Research %D 2021 %P 3399-3426 %V 55 %N 6 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021144/ %R 10.1051/ro/2021144 %G en %F RO_2021__55_6_3399_0
Nayeri, Sina; Asadi-Gangraj, Ebrahim; Emami, Saeed; Rezaeian, Javad. Designing a bi-objective decision support model for the disaster management. RAIRO. Operations Research, Tome 55 (2021) no. 6, pp. 3399-3426. doi: 10.1051/ro/2021144
[1] and , The role of earthquake information management systems (EIMSs) in reducing destruction: A comparative study of Japan, Turkey and Iran. Disaster Prev. Manag. An Int. J. 18 (2009) 150–161. | DOI
[2] and , OR/MS research in disaster operations management. Eur. J. Oper. Res. 175 (2006) 475–493. | Zbl | DOI
[3] and , A realistic variant of bi-objective unrelated parallel machine scheduling problem: NSGA-II and MOACO approaches. Appl. Soft Comput. 50 (2017) 109–123. | DOI
[4] , , , and , Coordination in humanitarian relief chains: Practices, challenges and opportunities. Int. J. Prod. Econ. 126 (2010) 22–34. | DOI
[5] , The multiple traveling salesman problem: An overview of formulations and solution procedures. Omega 34 (2006) 209–219. | DOI
[6] , , , and , Dynamic coordination of ambulances for emergency medical assistance services. Knowledge-Based Syst. 70 (2014) 268–280. | DOI
[7] , Single-machine scheduling with learning considerations. Eur. J. Oper. Res. 115 (1999) 173–178. | Zbl | DOI
[8] , A state-of-the-art review on scheduling with learning effects. Eur. J. Oper. Res. 188 (2008) 315–329. | Zbl | DOI
[9] , , and , Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study. Comput. Environ. Urban Syst. 81 (2020) 101479. | DOI
[10] , , and , A bi-level optimization model for aid distribution after the occurrence of a disaster. J. Clean. Prod. 105 (2015) 134–145. | DOI
[11] and , Prepositioning supplies in preparation for disasters. Eur. J. Oper. Res. 209 (2011) 156–165. | Zbl | DOI
[12] , , and , Discrete choice approach for assessing deprivation cost in humanitarian relief operations. Socioecon. Plann. Sci. 63 (2018) 33–46. | DOI
[13] , , and , The regional cooperation-based warehouse location problem for relief supplies. Comput. Ind. Eng. 102 (2016) 259–267. | DOI
[14] and , Single machine scheduling with learning effect considerations. Ann. Oper. Res. 98 (2000) 273–290. | Zbl | DOI
[15] , and , Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects. Inf. Sci. (Ny) 178 (2008) 2476–2487. | Zbl | DOI
[16] and , Appropriate engineering measures with participation of community for flood disaster reduction: Case of the Tha Chin Basin, Thailand. Arab. J. Sci. Eng. 41 (2016) 4879–4892. | DOI
[17] , and , Coordination in rapidly evolving disaster response systems: The role of information. Am. Behav. Sci. 48 (2004) 295–313. | DOI
[18] , , , and , A biased random-key genetic algorithm for the rescue unit allocation and scheduling problem. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE (2018) 1–6.
[19] , , and , A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2002) 182–197. | DOI
[20] and , A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE (1995) 39–43. | DOI
[21] , and , An optimization model for humanitarian relief volunteer management. In: Proceedings of the 6th International ISCRAM Conference (2009).
[22] , and , Optimized resource allocation for emergency response after earthquake disasters. Saf. Sci. 35 (2000) 41–57. | DOI
[23] , and , Earthquake early warning systems. Springer (2007). | DOI
[24] and , Technical note – Converting the 0–1 polynomial programming problem to a 0–1 linear program. Oper. Res. 22 (1974) 180–182. | Zbl | DOI
[25] , and , Data challenges in dynamic, large-scale resource allocation in remote regions. Saf. Sci. 87 (2016) 76–86. | DOI
[26] , , , and , Medical relief shelter location problem with patient severity under a limited relief budget. Comput. Ind. Eng. 125 (2018) 720–728. | DOI
[27] , , and , Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transp. Res. Part E Logist. Transp. Rev. 75 (2015) 1–17. | DOI
[28] , and , Models for relief routing: Equity, efficiency and efficacy. Transp. Res. Part E Logist. Transp. Rev. 48 (2012) 2–18. | DOI
[29] and , Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects. Omega 39 (2011) 467–471. | DOI
[30] , , and , A robust model predictive control approach for post-disaster relief distribution. Comput. Ind. Eng. 135 (2019) 1253–1270. | DOI
[31] , Effective implementation of the -constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213 (2009) 455–465. | Zbl
[32] and , A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Comput. Ind. Eng. 113 (2017) 766–778. | DOI
[33] , and , A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp. Res. Part E Logist. Transp. Rev. 49 (2013) 217–249. | DOI
[34] , and , Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect. Neural Comput. Appl. 31 (2019) 7517–7537. | DOI
[35] , Providing spatial decision support for evacuation planning: A challenge in integrating technologies. Disaster Prev. Manag. An Int. J. 10 (2001) 11–20. | DOI
[36] , Risk-averse two-stage stochastic programming with an application to disaster management. Comput. Oper. Res. 39 (2012) 541–559. | Zbl | DOI
[37] , Taguchi methods: A hands-on approach. Addison Wesley Publishing Company (1993).
[38] , , and , Operational analysis framework for emergency operations center preparedness training. In: Proceedings 36th Winter Simulation Conference (2004) 839–848.
[39] , , , and , An integrated resource allocation and distribution model for pre-disaster planning. Comput. Ind. Eng. 91 (2016) 229–238. | DOI
[40] , and , A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl. Soft Comput. 13 (2013) 1728–1740. | DOI
[41] and , Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. Int. J. Prod. Econ. 182 (2016) 324–341. | DOI
[42] and , An exact branch-and-price algorithm for scheduling rescue units during disaster response. Eur. J. Oper. Res. 272 (2019) 352–363. | DOI
[43] and , Pre-positioning of emergency supplies for disaster response. Transp. Res. Part B Methodol. 44 (2010) 521–534. | DOI
[44] , , and , An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Comput. Ind. Eng. 94 (2016) 201–215. | DOI
[45] , , and , Decision support for disaster management. Oper. Manag. Res. 3 (2010) 68–79. | DOI
[46] , , , , and , Towards a business continuity information network for rapid disaster recovery. In: Proceedings of the 2008 International Conference on Digital Government Society of North America (2008) 107–116.
[47] , , and , Development of fuzzy RUASP model – Grasp metaheuristics with time window: Case study of Mount Semeru eruption in East Java. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing (2019) 12081.
[48] and , Resource scheduling for postdisaster management in IoT environment. Wirel. Commun. Mob. Comput. (2019) 7802843.
[49] , , and , Solving a bi-objective unrelated parallel batch processing machines scheduling problem: A comparison study. Comput. Oper. Res. 88 (2017) 71–90. | DOI
[50] , , , Assignment of medical staff to operating rooms in disaster preparedness: A novel stochastic approach. IEEE Trans. Eng. Manag. 67 (2019) 593–602. | DOI
[51] , An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transp. Res. Part E Logist. Transp. Rev. 43 (2007) 687–709. | DOI
[52] , Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transp. Res. Part E Logist. Transp. Rev. 46 (2010) 1–17. | DOI
[53] and , Optimal allocation of emergency medical resources in a mass casualty incident: patient prioritization by column generation. Eur. J. Oper. Res. 252 (2016) 623–634. | Zbl | DOI
[54] , , and , Optimal prediction of catastrophes in autoregressive moving-average processes. J. Time Ser. Anal. 17 (1996) 511–531. | Zbl | DOI
[55] , Introduction to quality engineering: Designing quality into products and processes. Unipub/Kraus (1986).
[56] , , and , Modeling and analysis of decision making problem for mitigating natural disaster risks. Eur. J. Oper. Res. 122 (2000) 461–468. | Zbl | DOI
[57] , , and , A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Appl. Soft Comput. 13 (2013) 4750–4762. | DOI
[58] UN ISDR, Hyogo framework for action 2005–2015: Building the resilience of nations and communities to disasters. In: Extract from the final report of the World Conference on Disaster Reduction (A/CONF. 206/6) (2005).
[59] , , , and , Hybrid scheduling algorithm in early warning systems. Future Gener. Comput. Syst. 79 (2018) 630–642. | DOI
[60] , , and , A multi-criteria optimization model for humanitarian aid distribution. J. Glob. Optim. 51 (2011) 189–208. | Zbl | DOI
[61] , and , Improving emergency response collaboration and resource allocation by task network mapping and analysis. Saf. Sci. 70 (2014) 9–18. | DOI
[62] and , Single machine scheduling with sum-of-logarithm-processing-times based and position based learning effects. Optim. Lett. 8 (2014) 971–982. | Zbl | DOI
[63] , , and , Optimisation of treatment scheme for water inrush disaster in tunnels based on fuzzy multi-criteria decision-making in an uncertain environment. Arab. J. Sci. Eng. 44 (2019) 8249–8263. | DOI
[64] , and , Intelligent decision support for centralized coordination during emergency response. In: Proceedings of the 8th International ISCRAM Conference (2011).
[65] , and , Operational emergency response under informational uncertainty: A fuzzy optimization model for scheduling and allocating rescue units, edited by and , in: Proc. of the 9th International ISCRAM Conference. Simon Fraser University, Vancouver, BC (2012).
[66] , and , Decision modeling for assignments of collaborative rescue units during emergency response. In: 46th Hawaii International Conference on System Sciences. IEEE (2013) 166–175.
[67] , , and , Emergency response in natural disaster management: Allocation and scheduling of rescue units. Eur. J. Oper. Res. 235 (2014) 697–708. | Zbl | DOI
[68] , , , , and , Global optimization for multi-stage construction of rescue units in disaster response. Sustain. Cities Soc. 51 (2019) 101768. | DOI
[69] , , and , The single-machine total weighted tardiness scheduling problem with position-based learning effects. Comput. Oper. Res. 39 (2012) 1109–1116. | Zbl | DOI
[70] , and , Study on the model for the assignment of rescue workers in emergency rescue. Chinese J. Manag. Sci. 21 (2013) 152–160.
[71] and , A bi-objective identical parallel machine scheduling problem with controllable processing times: A just-in-time approach. Int. J. Adv. Manuf. Technol. 77 (2015) 545–563. | DOI
[72] , , , and , A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis. Eur. J. Oper. Res. 250 (2016) 855–864. | Zbl | DOI
[73] , , , and , Multistage assignment optimization for emergency rescue teams in the disaster chain. Knowledge-Based Syst. 137 (2017) 123–137. | DOI
[74] , , and , Emergency decision making for natural disasters: An overview. Int. J. Disaster Risk Reduct. 27 (2018) 567–576. | DOI
[75] , Evolutionary algorithms for multiobjective optimization: Methods and applications (1999).
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