{"title":"\"Almost-stable\" matchings in the Hospitals / Residents problem with Couples.","authors":"David F Manlove, Iain McBride, James Trimble","doi":"10.1007/s10601-016-9249-7","DOIUrl":null,"url":null,"abstract":"<p><p>The Hospitals / Residents problem with Couples (hrc) models the allocation of intending junior doctors to hospitals where couples are allowed to submit joint preference lists over pairs of (typically geographically close) hospitals. It is known that a stable matching need not exist, so we consider min bp hrc, the problem of finding a matching that admits the minimum number of blocking pairs (i.e., is \"as stable as possible\"). We show that this problem is NP-hard and difficult to approximate even in the highly restricted case that each couple finds only one hospital pair acceptable. However if we further assume that the preference list of each single resident and hospital is of length at most 2, we give a polynomial-time algorithm for this case. We then present the first Integer Programming (IP) and Constraint Programming (CP) models for min bp hrc. Finally, we discuss an empirical evaluation of these models applied to randomly-generated instances of min bp hrc. We find that on average, the CP model is about 1.15 times faster than the IP model, and when presolving is applied to the CP model, it is on average 8.14 times faster. We further observe that the number of blocking pairs admitted by a solution is very small, i.e., usually at most 1, and never more than 2, for the (28,000) instances considered.</p>","PeriodicalId":55211,"journal":{"name":"Constraints","volume":"22 1","pages":"50-72"},"PeriodicalIF":0.5000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10601-016-9249-7","citationCount":"37","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Constraints","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10601-016-9249-7","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/8/11 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 37
Abstract
The Hospitals / Residents problem with Couples (hrc) models the allocation of intending junior doctors to hospitals where couples are allowed to submit joint preference lists over pairs of (typically geographically close) hospitals. It is known that a stable matching need not exist, so we consider min bp hrc, the problem of finding a matching that admits the minimum number of blocking pairs (i.e., is "as stable as possible"). We show that this problem is NP-hard and difficult to approximate even in the highly restricted case that each couple finds only one hospital pair acceptable. However if we further assume that the preference list of each single resident and hospital is of length at most 2, we give a polynomial-time algorithm for this case. We then present the first Integer Programming (IP) and Constraint Programming (CP) models for min bp hrc. Finally, we discuss an empirical evaluation of these models applied to randomly-generated instances of min bp hrc. We find that on average, the CP model is about 1.15 times faster than the IP model, and when presolving is applied to the CP model, it is on average 8.14 times faster. We further observe that the number of blocking pairs admitted by a solution is very small, i.e., usually at most 1, and never more than 2, for the (28,000) instances considered.
医院/住院医生与夫妇的问题(hrc)模拟了有意向的初级医生分配到医院的模式,允许夫妇提交联合偏好清单,而不是对(通常是地理上接近的)医院。已知不需要存在稳定的匹配,因此我们考虑min bp hrc,即寻找允许最小数量阻塞对的匹配的问题(即“尽可能稳定”)。我们证明了这个问题是np困难的,即使在高度限制的情况下,每对夫妇只发现一个医院对是可接受的。然而,如果我们进一步假设每个单个居民和医院的偏好列表的长度最多为2,我们给出了这种情况下的多项式时间算法。然后,我们提出了最小bp hrc的第一个整数规划(IP)和约束规划(CP)模型。最后,我们讨论了将这些模型应用于随机生成的最小bp hrc实例的经验评估。我们发现,CP模型的平均速度约为IP模型的1.15倍,当将分解应用于CP模型时,其平均速度为8.14倍。我们进一步观察到,对于所考虑的(28,000)个实例,一个解所允许的阻塞对的数量非常小,即通常最多为1,并且不超过2。
期刊介绍:
Constraints provides a common forum for the many disciplines interested in constraint programming and constraint satisfaction and optimization, and the many application domains in which constraint technology is employed. It covers all aspects of computing with constraints: theory and practice, algorithms and systems, reasoning and programming, logics and languages.