Efficient methods for a bi-objective nursing home location and allocation problem: A case study
Efficient methods for a bi-objective nursing home location and allocation problem: A case study
复制标题
双目标疗养院选址和分配问题的有效方法：案例研究
DOI：
10.1016/j.asoc.2018.01.014
复制
发表时间：
2018-04
影响因子：
8.7
通讯作者：
Ma Shuan
中科院分区：
文献类型：
--
作者：
Wang Shijin;Ma Shuan
近年来，由于养老问题越来越严重，养老院建设成为一项重要工程。同时，在养老院建设之前，决策者必须确定养老院的位置。有效的区位规划可以实现资源的合理配置和社会公平。本文考虑了养老院选址和分配问题有两个目标。第一个目标是从政府的角度最大限度地减少总建筑成本。对于老年人来说，他们的期望是他们分配的养老院离他们的孩子的社区足够近。因此，第二个目标是最小化总加权距离。为了在合理的计算时间内获得精确解，提出了有效的等式来增强精确ε-约束方法。对于大规模的问题，非支配排序遗传算法（NSGA-II），其中使用了一种新的染色体表示和启发式方法设计的分配子问题。多目标模拟退火（MOSA）算法也适用于该问题。最后，两个数据集的案例研究进行了计算比较与精确的方法和算法。结果表明，改进的ε-约束方法和改进的NSGA-II方法具有良好的性能.
In recent years, due to more and more serious pension issues, nursing home construction becomes an important project. Meanwhile, it is crucial for decision makers to determine the location of a nursing home before its construction. An effective location planning could achieve reasonable resources allocation and social fairness. This paper considers a nursing home location and allocation problem with two objectives. The first objective is to minimize the total construction costs from the perspective of the government. For the elderly people, their expectations are their allocated nursing homes close enough to their children's communities. Therefore, the second objective is to minimize the total weighted distances. To obtain exact solutions within reasonable computation time, valid equalities are proposed to enhance exactε-constraint method. For large-scale problems, a non-dominated sorting genetic algorithm (NSGA-II) is applied, in which a new chromosome representation is used and a heuristic method is designed for the allocation subproblem. A multi-objective simulated annealing (MOSA) algorithm is also adapted to the problem. Finally, a case study with two data sets is conducted and computational comparisons are made with both exact methods and the algorithms. The results show the promising performance of the proposed enhancedε-constraint method and the modified NSGA-II.
登录
查看更多内容
影响因子：
2.7
作者：
Doerner, Karl F.;Gutjahr, Walter J.;Nolz, Pamela C.
通讯作者：
Nolz, Pamela C.
DOI：
10.1109/icsssm.2016.7538609
发表时间：
2016-06
期刊：
2016 13th International Conference on Service Systems and Service Management (ICSSSM)
影响因子：
--
作者：
Shijin Wang;Shuan Ma;Bin Li;Xue Li
通讯作者：
Shijin Wang;Shuan Ma;Bin Li;Xue Li
影响因子：
8.3
作者：
Woraya Neungmatcha;Kanchana Sethanan;M. Gen;S. Theerakulpisut
通讯作者：
Woraya Neungmatcha;Kanchana Sethanan;M. Gen;S. Theerakulpisut
DOI：
10.1016/j.cie.2017.04.036
发表时间：
2017-07
期刊：
Comput. Ind. Eng.
影响因子：
--
作者：
N. Zarrinpoor;M. Fallahnezhad;M. Pishvaee
通讯作者：
N. Zarrinpoor;M. Fallahnezhad;M. Pishvaee
DOI：
--
发表时间：
2001
期刊：
--
影响因子：
--
作者：
Z. Drezner;H. Hamacher
通讯作者：
Z. Drezner;H. Hamacher