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Managing volunteer convergence at disaster relief centers

机译:在救灾中心管理志愿者的融合

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Disaster relief centers (DRCs) are the retailers of disaster relief supply chains. Also known as Points of Distribution (PODs), disaster relief centers are ad hoc distribution sites set up in shopping mall parking lots, schools, churches, or warehouses where relief supplies are distributed to beneficiaries. Perhaps the most significant challenges that DRCs face are volunteer and material convergence, which refer to the mass movement of people and supplies, respectively, into affected areas following large-scale disaster events. The handling of donated supplies at DRCs and their subsequent distribution to beneficiaries are carried out almost exclusively by volunteers who also show up and abandon DRCs at random points in time. This paper concerns the assignment of volunteers to tasks at DRCs and takes the following three sources of uncertainty into account: (i) beneficiary arrival and service times, (ii) donation arrival and service times, and (iii) volunteer arrival and abandonment times. We represent the DRC as a queuing system with two parallel queues, one for donors/donations and the other for beneficiaries, and analyze the system in an agent-based simulation environment. In particular, we examine the effectiveness of heuristic policies for assigning volunteers to parallel queues in which performance metrics related to the average time donors and beneficiaries spend in the DRC are minimized. Through extensive computational experimentation, we identify the most effective assignment policies under a variety of experimental conditions.
机译:救灾中心(DRC)是救灾供应链的零售商。救灾中心也称为分发点(POD),是在购物中心停车场,学校,教堂或仓库中设立的临时分发点,向受益人分发救济物资。刚果民主共和国面临的最重大挑战也许是志愿人员和物资的融合,这是指大规模灾难事件发生后,人员和物资分别大规模转移到受灾地区。在刚果民主共和国处理捐赠物资并随后将其分发给受益人几乎全部由志愿者进行,他们还会在随机的时间点出现并放弃刚果民主共和国。本文涉及将志愿者分配到DRC的任务,并考虑到以下三个不确定性来源:(i)受益人到达和服务时间,(ii)捐赠到达和服务时间,以及(iii)志愿者到达和放弃时间。我们将DRC表示为具有两个并行队列的排队系统,一个队列用于捐赠者/捐赠,另一个队列用于受益人,并在基于代理的模拟环境中分析该系统。特别是,我们研究了启发式策略将志愿者分配给并行队列的有效性,在并行队列中,与捐助者和受益者在刚果民主共和国的平均时间相关的绩效指标已降至最低。通过广泛的计算实验,我们确定了各种实验条件下最有效的分配策略。

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