摘要
综合考虑政府、居民、投资者3方需求,构建带约束多目标的养老机构配置优化模型,基于改进免疫算法和GIS技术,对上海市虹口区养老机构的配置优化问题进行研究,分析研究区现有养老机构在空间分布和规模配置上的合理性,提出优化配置方案。研究表明:研究区现有养老机构数量缺口较大、部分养老机构偏离最佳区位、规模与需求存在较大差距;3所位于江湾镇街道的养老机构在现有区位运行欠合理;为充分满足居民养老需求,需在虹口区南部地区增设15所养老机构;通过与一般免疫算法、遗传算法、粒子群算法、模拟退火算法得到的优化结果对比可知,改进免疫算法在此优化问题中的求解效率分别提高45%,38.89%,21.43%,46.34%,求解精度分别提高1.61%,2.73%,5.80%,6.91%。
In recent years,the population is ageing rapidly in Shanghai.Increases in the older population are generating demand for a wide range of elder care services.Residential care homes are the main places for elderly care service.The configuration rationality of residential care homes makes a difference to sustainable urban development and the construction of a harmonious society.However,some issues in the configuration of residential care facilities such as unreasonable spatial layout and insufficient service have become increasingly acute with the growth of the geriatric population.It is important to optimize the layout and number of residential care facilities to ensure the equity and efficiency of public services.In this paper,a method for optimizing the layout and quantity of residential care homes together is studied.Considering the demand of government,residents and investors,a configuration optimization model with multi-objective and constraints for residential care homes is constructed based on maximizing the government equity,maximizing configuration efficiency,minimizing residents traveling cost and maximizing the investors' returns.Then,the software GIS and the improved immune algorithm are used for the configuration optimization of the residential care homes in Hongkou District of Shanghai.According to the calculating results,the configuration rationality of current residential care homes is analyzed and a new configuration scheme is put forward.After comparing the results of the proposed method with those of simulated annealing algorithm,particle swarm algorithm,genetic algorithm,traditional immune algorithm and the existing scheme,the feasibility and superiority of the proposed method is verified validly.The results show that the scale and numbers of residential care homes still cannot satisfy the demand of the elderly in Hongkou District.Furthermore,most of the current residential care homes in Hongkou District deviate from the optimal locations.They may increase the residents' travel cost,weaken the government equity and configuration efficiency,hinder the increase of investment returns and need to be further optimized.According to the optimize results,the rationality of 3 residential care homes that are located in Jiangwanzhen Street should be further improved and 15 new residential care homes should be built in the south of Hongkou District to satisfy the demands of the elderly population.In addition,the computing efficiency of the improved immune algorithm is higher than that of simulated annealing algorithm,particle swarm algorithm,genetic algorithm,basic immune algorithm by 45%,38.89%,21.43%,46.34% respectively,and the antibody affinity value of improved immune algorithm is better than that of the simulated annealing algorithm,particle swarm algorithm,genetic algorithm,basic immune algorithm respectively by 1.61%,2.73%,5.80%,6.91%.The proposed method improves the basic immune algorithm from two aspects including selection operator and mutation operator.Hence,the computation procedure is more efficient and accurate than that of traditional immune algorithm.Due to considering varies stakeholders' requirements,the configuration scheme is reasonable.The method can support knowledge-based policy-making and planning of residential care facilities.The optimized results also can provide references for scientific decision-making on residential care in Hongkou District of Shanghai.
作者
程敏
崔晓
Min Cheng;Xiao Cui(School of Management, Shanghai University, Shanghai 200444, China)
出处
《地理科学》
CSSCI
CSCD
北大核心
2018年第12期2049-2057,共9页
Scientia Geographica Sinica
基金
上海市哲学社会科学规划项目(2016BGL008)资助~~
关键词
养老机构
配置优化
改进免疫算法
GIS
多目标
residential care homes
configuration optimization
improved immune algorithm
GIS
multi-objective
