Robust surgery loading

We consider the robust surgery loading problem for a hospital’s operating theatre department, which concerns assigning surgeries and sufficient planned slack to operating room days. The objective is to maximize capacity utilization and minimize the risk of overtime, and thus cancelled patients. This research was performed in collaboration with the Erasmus MC, a large academic hospital in the Netherlands, which has also provided historical data for the experiments. We propose various constructive heuristics and local search methods that use statistical information on surgery durations to exploit the portfolio effect, and thereby to minimize the required slack. We demonstrate that our approach frees a lot of operating room capacity, which may be used to perform additional surgeries. Furthermore, we show that by combining advanced optimization techniques with extensive historical statistical records on surgery durations can significantly improve the operating room department utilization.     

A Web-based ERP system for business services and supply chain management: Application to real-world process scheduling

A Web-based ERP system developed for attacking business problems and managing real-world business processes ranging from simple office automation procedures to complicated supply chain planning is presented. The system’s Web-aspect provides significant advantages, as the system is distributed through interoperable, cross-platform and highly pluggable Web-service components. The system involves a powerful workflow engine that manages the entire process event flow within the enterprise increasing efficiency and control at the same time. Business processes, when needed, are controlled by the enterprise quality management system and consequently the ISO directives are accurately followed. A real-world process scheduling system developed for the specific needs of Greek Construction Manufacturing Enterprises is illustrated as a detailed paradigm of the system’s capabilities. The problem was formulated to assign project tasks in form of lots to enterprise resources in order that resources idle time and delays in project preparation time were minimized. The problem was solved by a simple and effective heuristic algorithm.     

电信业务管理系统的定单调度

介绍了电信业务计算机综合管理系统（“九七”工程）定单调度的概念、抽象方法、应用和管理。利用数学表达式把业务抽象化，使应用程序的调度只是解决一个数学问题，业务的调整只需对函数组合进行调整，必要时可以重新创建若干基本函数，以适应未来的业务变化。     

基于多源LBS数据的职住平衡对比研究 ——以北京城区为例

采用多源LBS数据(热力图数据、POI数据、微博签到数据), 对北京城区职住关系进行测定和分析, 从不同角度测度北京城区职住关系的时间和空间特征, 研究结果可为改善城市职住关系提供决策参考。比较了LBS数据同传统人口和经济普查数据所测度的职住关系结果的差异性, 讨论差异产生原因, 为更好地利用LBS数据进行职住关系研究提供建议。     

Efficiency and welfare implications of managed public sector hospital waiting lists

A queuing model for public health service waiting lists is developed, and the implications for patient welfare of different systems for managing the waiting list are analysed. If patients are admitted to hospital on a first-come-first-served basis, a welfare gain is achieved by moving from a system of implicit to one of explicit rationing of access to the waiting list. If individual waiting times and hospital admissions are dependent on clinical priority, a further welfare gain is achievable without the use of explicit rationing, by reallocating the total waiting time from the more towards the less seriously ill. On efficiency and welfare criteria, a maximum waiting time guarantee does not appear to be a desirable development.     

城市湿地与城市生态安全关系初探

在分析城市生态安全范畴的基础上,阐明了城市湿地在城市生态安全中的作用和地位,提出了城市湿地目前存在的主要问题和对策.分析成果将增强人们对城市湿地与城市生态安全之间关系的认识,为开展城市湿地的科学保护、建设、开发活动,维护城市生态安全和促进城市可持续发展提供依据.     

基于LBS的连续查询位置隐私保护模型的动态规划算法

针对现有的算法大多都是静态位置隐私保护的,如果将静态算法应用于动态的连续查询中,会导致位置隐私泄露,提出了一种基于连续查询的动态规划改进算法,旨在保护用户的位置隐私,仿真实验结果证明:该算法在匿名处理时间、匿名成功率和轨迹扭曲度等方面优于现有算法.     

河南省公共服务与经济发展协调性动态评价

通过构建公共服务与经济发展协调性评价指标体系,借助熵权TOPSIS法,对河南省2000—2010年的公共服务供给与经济发展协调性进行量化动态评价.结果表明:河南省11年间公共服务供给与经济发展协调等级总体较低,处于"较不协调"和"一般协调"水平,与理想最优的协调状态还有较大差距;公共服务与经济发展协调状态呈U型波动,各年之间的改善系数并未出现持续递增趋势,河南省公共服务与经济发展协调状态存在"恶化"的风险,需要密切关注.最后提出相应对策建议.     

A column generation approach for solving the patient admission scheduling problem

This paper addresses the Patient Admission Scheduling (PAS) problem. The PAS problem entails assigning elective patients to beds, while satisfying a number of hard constraints and as many soft constraints as is possible, and arises at all planning levels for hospital management. There exist a few, different variants of this problem. In this paper we consider one such variant and propose an optimization-based heuristic building on branch-and-bound, column generation, and dynamic constraint aggregation to solve it. We achieve tighter lower bounds than previously reported in the literature and, in addition, we are able to produce new best known solutions for five out of twelve instances from a publicly available repository.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.