The optimization and coordination of multi-layer recruitment network for social emergency resources. (April 2019)

• Record Type: Journal Article
• Authors: Meng, Yan-ping
• Is Part Of: Computers & industrial engineering. Volume 130(2019)
• Page Start: 50
• Page End: 61
• Publication Date: 2019-04
• Subjects: Multi-layer network -- Coordination -- Emergency logistics -- Emergency management -- Social emergency resources; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.02.019
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Social emergency resource monitoring system is designed. A time-bound multi-layer recruitment network model is established. The decomposition-coordination solving framework is proposed. The effectiveness of the model and solving framework is verified. Abstract: Social emergency resources (SERs) which are supplied by non-governmental organizations (NGOs), non-agreement enterprises and individual donators played very important roles in large scale emergency relief. The mobilization and recruitment of SERs is complicated and challenging, since the resources are supplied by diverse organizations and individuals at scattered areas, and meanwhile, the recruitment time is limited. In this paper, for monitoring and managing SERs, an integrated social emergency resources monitoring system (SERMS) is presented to compressively optimize the collection and dispatch of SERs, which integrate resources from variety organizations, non-agreement enterprises and individual donators. A time-bound multi-layer recruitment network of SERs is modeled to maximize the amount of collected resources and minimize the logistics costs. A decomposition and coordination framework for multi-layer network is proposed according to the recruitment times at different layers to optimize the network and reduce the computation complexity. The numerical study demonstrates that by coordinating different layer networks, the relief can satisfy additional 10% demand compared to the result of the network without coordination. The proposed model and framework can also be applied in network design and multi-layer location problem.