关联情境下物流服务外包商选择的混合型决策分析方法

现实中影响物流服务外包商选择的诸多因素通常具有主客观特性和关联性,针对此问题给出一种关联情境下物流服务外包商选择的混合型决策分析方法.首先,利用二元语义表示模型分别处理和集结专家给出的语言短语形式的因素关联信息和主观评价信息,并通过多因素关联分析确定因素的重要性和归类；然后,分别定义主客观测度因素的正负理想点,利用范数的概念构建规范化评价矩阵；进一步地,借鉴多准则优化妥协解(VIKOR)法的思想,计算每个备选外包商的群效用值和个体遗憾值以及折衷排序值,并根据折衷排序值选择出最理想的外包商.最后,通过一个实例分析说明给出方法的可行性和实用性.     

医疗体制改革的博弈分析

医疗体制改革是我国制度改革的一项重要内容,建立完善医疗卫生体系也是构建和谐社会的一项基本要求.现阶段,我国在医疗卫生领域中还存在许多突出的问题.本文从博弈论的角度出发,通过建立的博弈模型,分析了医疗机构、政府主管部门及患者三者之间的博弈关系,并对医疗体制改革提出相应对策.     

Dynamic job assignment: A column generation approach with an application to surgery allocation

We consider the assignment of jobs to heterogeneous agents in a dynamic system with a rolling time horizon. An example is a hospital operating theatre where the jobs are surgeries and the agents are the surgeons. The paper is presented in the context of surgery allocation and the system is characterized as follows: Patients are grouped into categories and they arrive continually following a stochastic process. Patients in each group have specific time limits within which they need treatment and if it cannot be accommodated then the patients are outsourced. The service level is the percentage of patients in each group treated within the time limit. Surgery durations are stochastic and depend on the surgeon conducting the surgeries. Each surgeon has limited time available and expected overtime is penalized by a non-decreasing convex function. We develop a column generation approach for the assignment of already arrived patients and tentative future patients to surgeons on specific days. It balances the conflicting objectives of including as many arrived patients as possible within their time limits, maximizing the service level of future patients, and minimizing the expected overtime of surgeons. A computational study is conducted with the model embedded in a rolling time horizon frame. The study indicates that the assignment of patients based on our model increases system performance in terms of service level and reduced overtime compared to a First-Come-First-Served (FCFS) policy when the arrival rates of patients are medium to high compared to the capacity of the system.     

Smart sensors in environmental/water quality monitoring using IoT and cloud services

The growing social awareness and consequent concern for the environment has driven environmental analytical chemistry to a position of great prominence. In recent times, this position has translated into taking advantage of the great benefits provided by cloud computing and the Internet of Things (IoT), which are especially appropriate when devices such as chemical sensors are used. The use of such sensors is very common when in situ monitoring of environmental parameters is performed, but until recently, it was limited to the deployment of a small number of sensors. Currently, this approach has given way to genuine smart sensing systems (for instance, fully consolidated wireless sensor networks) that are able to provide a substantial amount of information. This type of sensor (the so-called smart sensor) is fundamentally characterized by (a) low consumption, versatility, and autonomy, (b) ease of integration with cloud solutions, (c) durability and reliability of IoT platforms and sensors, and (d) easy installation and deployment of sensor nodes. For all these reasons, and given the increasing importance and use of this type of device, a revision of the recent literature relating the development of smart sensors with environmental issues has been conducted, with major contributions being discussed, most notably those addressing the continuous in-line monitoring of water quality.     

Outpatient appointment scheduling given individual day-dependent no-show predictions

This paper examines the combined use of predictive analytics, optimization, and overbooking to schedule outpatient appointments in the presence of no-shows. We tackle the problem of optimally overbooking appointments given no-show predictions that depend on the individual appointment characteristics and on the appointment day. The goal is maximizing the number of patients seen while minimizing waiting time and overtime. Our analysis leads to the definition of a near-optimal and simple heuristic which consists of giving same-day appointments to likely shows and future-day appointments to likely no-shows. We validate our findings by performing extensive simulation tests based on an empirical data set of nearly fifty thousand appointments from a real outpatient clinic. The results suggest that our heuristic can lead to a substantial increase in performance and that it should be preferred to open access under most parameter configurations. Our paper will be of great interest to practitioners who want to improve their clinic performance by using individual no-show predictions to guide appointment scheduling.     

基于溢出效应与平台服务的竞争网络平台的销售模式研究

网络平台竞争与发展成为平台运营理论与实践的关键问题,急需解决。本文应用博弈论研究存在溢出效应和网络平台竞争环境下平台服务内生的网络平台销售模式选择,即网络平台商是允许制造商在其网络平台上直销产品的平台销售模式,还是网络平台商先从制造商购买产品再销售给消费者的转销模式。主要研究竞争网络平台的三种结构模型:双转销模式、双平台销售模式和混合模式。研究结果显示:网络平台商的利润随正溢出效应增加而增加,随负溢出效应增加而减少。当网络平台服务敏感程度较高且网络平台竞争较为激烈时,两个网络平台商均选择平台销售模式,形成占优均衡;此时,网络平台商获得最高利润,而且网络零售价和网络平台服务水平最高。     

环形路网上带有服务时长的在线TSP问题

为了提高快递揽件的时效性,需要对快递车辆进行有效调度。针对环形路网上服务时长以及需求无法预知的揽件问题,本文提出了以服务总时间尽可能短为目标的环形路网上带有服务时长的在线旅行商问题。用在线算法分析了此问题竞争比的下界,设计了两个在线算法并分析了各自的竞争比,结果表明服务时长可以改善在线车的性能。最后通过简单算例对两个算法进行说明,本文研究结论可以为环形路网上的快递车辆实时调度提供指导。     

The multicriteria analysis for construction of benchmarkers to support the Clinical Engineering in the Healthcare Technology Management

In developing countries, approximately half of the healthcare equipments are not in full use and the main cause of this is the inadequate management of them. The Clinical Engineering might face this problem in the healthcare environment analyzing the equipment with a health technological process perspective to identify opportunities of improvements. Generally, the inadequate management is a result of lack of systemized and contextualized information about the health technological process. The effort to make an adequate management generates an increasing interest in the use of benchmarker. Currently, the benchmarkers used by the Clinical Engineering are not representative of a health technological process as a whole. Health technological process concept is stated in this article. The multicriteria analysis methodology MCDA (Multicriteria Decision Aid) is used to obtain benchmarkers and to identify opportunities of improvements, thus generating conditions for that the Clinical Engineering consolidates its relevant contribution for the healthcare.     

Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach

Aeromedical and ground ambulance services often team up in responding to trauma crashes, especially when the emergency helicopter is unable to land at the crash scene. We propose location-coverage models and a greedy heuristic for their solution to simultaneously locate ground and air ambulances, and landing zones (transfer points). We provide a coverage definition based on both response time and total service time, and consider three coverage options; only ground emergency medical services (EMS) coverage, only air EMS coverage, or joint coverage of ground and air EMS in which the patient is transferred from an ambulance into an emergency helicopter at a transfer point. To analyze this complex coverage situation we develop two sets of models, which are variations of the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). These models address uncertainty in spatial distribution of motor vehicle crash locations by providing coverage to a given set of both crash nodes and paths. The models also consider unavailability of ground ambulances by drawing upon concepts from backup coverage models. We illustrate our results on a case study that uses crash data from the state of New Mexico. The case study shows that crash node and path coverage percentage values decrease when ground ambulances are utilized only within their own jurisdiction.     

Uncertainty and value of information when allocating resources within and between healthcare programmes

A two-stage stochastic mathematical programming formulation has been developed to optimally allocate resources within and between healthcare programmes when there is an exogenous budget and the parameters of the healthcare models are variable and uncertain. This formulation solves the optimal resource allocation problem and calculates the expected value of acquiring additional information to resolve the uncertainties within the allocation. It is shown that the proposed formulation has several advantages over the chance constrained and robust mathematical programming methods.