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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Multi-criteria assignment policies to improve global effectiveness of medico-social service sector
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Multi-criteria assignment policies to improve global effectiveness of medico-social service sector

机译:多标准分配政策可提高医疗社会服务部门的全球有效性

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摘要

In this paper, we propose a multi-criteria approach in order to reduce the waiting time on the assignment of users to medico-social institutions. The main goal is to ascertain whether alternative assignment policies can improve global response to users' demands, and to assess the performances of each alternative (scenario) compared to the current practice. We propose a mathematical model of user assignment to medico-social structures and professionals, under constraints of resource capacity, professionals' skills and user requirements (formulated in an individual support project). With alternative assignment policies, users' needs could be covered partially. They also authorize the involvement of several structures and several professionals in the response to the request. A mixed integer linear programming solver is used to solve this assignment problem, following a lexicographic approach in case of partial coverage policy. For some assignment policies that have long calculation times, we propose a simulated annealing approach in order to speed up the resolution. In order to validate simulated annealing approach, we compared it with a greedy randomized adaptive search procedure. The problem is solved for instances where we have real size data for a set of institutions for disabled children and teenagers. We present and compare computational results over several scenarios, highlighting the improvement provided by each alternative assignment policy.
机译:在本文中,我们提出了一种多准则方法,以减少将用户分配给医疗社会机构的等待时间。主要目标是确定替代分配策略是否可以改善对用户需求的全局响应,并与当前实践相比评估每种替代方案(方案)的性能。我们在资源能力,专业人员的技能和用户要求(由单个支持项目制定)的约束下,提出了一种将用户分配给医疗社会结构和专业人员的数学模型。使用替代分配策略,可以部分满足用户的需求。他们还授权几个机构和几个专业人员参与响应请求。在部分覆盖策略的情况下,遵循词典方法,使用混合整数线性规划求解器来解决此分配问题。对于某些计算时间较长的分配策略,我们提出了一种模拟退火方法,以加快分辨率。为了验证模拟退火方法,我们将其与贪婪的随机自适应搜索过程进行了比较。如果我们拥有一组残疾儿童和青少年机构的实际数据,就可以解决该问题。我们介绍并比较了几种情况下的计算结果,重点介绍了每种替代分配策略所提供的改进。

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