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10.1016/j.ijdrr.2020.101780 http://scihub22266oqcxt.onion/10.1016/j.ijdrr.2020.101780 32904513!7456293!32904513     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Disaster+Risk+Reduct 2020 ; 50 (ä): 101780 Nephropedia Template TP {{tp|p=32904513|t=2020. Multi-resource scheduling and routing for emergency recovery operations |pdf=|usr=}}{{32904513}} gab.com Text Multi-resource scheduling and routing for emergency recovery operations JO: Int J Disaster Risk Reduct http://www.kidney.de/pget.php?&tw=1&pmid=32904513 #FOAvip Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations. Twit Text FOAVip Multi-resource scheduling and routing for emergency recovery operations ????Int J Disaster Risk Reduct http://www.kidney.de/pget.php?&tw=1&pmid=32904513 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32904513 #FOAvip English Wikipedia <ref>{{Cite pmid|32904513}}</ref> Multi-resource scheduling and routing for emergency recovery operations #MMPMID32904513 Bodaghi B; Shahparvari S; Fadaki M; Lau KH; Ekambaram P; Chhetri P Int J Disaster Risk Reduct 2020[Nov]; 50 (ä): 101780 PMID32904513show ga Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations. äMulti-resource scheduling and routing for emergency recovery operation(epmc).pdf DeepDyve Pubget Overpricing