Two-echelon inventory model with service level constraint and controllable lead time sensitive to order quantity

The decision-making and optimization of two-echelon inventory coordination were analyzed with service level constraint and controllable lead time sensitive to order quantity.First,the basic model of this problem was established and based on relevant analysis,the original model could be transformed by minimax method.Then,the optimal order quantity and production quantity influenced by service level constraint were analyzed and the boundary of optimal order quantity and production quantity was given.According to this boundary,the effective method and tactics were put forward to solve the transformed model.In case analysis,the optimal expected total cost of two-echelon inventory can be obtained and it was analyzed how service level constraint and safety factor influence the optimal expected total cost of two-echelon inventory.The results show that the optimal expected total cost of two-echelon inventory is constrained by the higher constraint between service level constraint and safety factor.

Research of Order Allocation of E-commerce Logistics Service Supply Chain Considering Customer Evaluation

Based on the importance of customer evaluation for developing e-commerce enterprises,this paper analyzes the customer evaluation as a fuzzy variable and establishes a multi-objective mixed integer order allocation planning model by considering customer satisfaction,which maximizes customer praise and minimizes procurement cost.As the optimization goal,transaction cost is optimized for the order allocation of the secondary e-commerce logistics service supply chain.In order to defuzzify the customer evaluation,a fuzzy evaluation method is designed to transform the customer evaluation from fuzzy language evaluation to numerical measurement.Finally,the feasibility and effectiveness of the model are verified by using a specific example,and the order is made for the e-commerce enterprise.The allocation provides a theoretical reference.

Urban road segment level of service based on bicycle users' perception under mixed traffic conditions

During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.

基于一阶逻辑的GIService服务发现

针对GIService服务发现,提出了基于一阶逻辑的GIService服务描述与匹配方法。使用一阶逻辑工具描述服务接口、运行条件和程序逻辑等GIService特征,提出基于描述逻辑与霍尔规则相似度计算的GIService服务匹配方法。实验案例证明,基于一阶逻辑的GIService服务发现可以全面表达GIService特征,得到更加准确的服务匹配结果。

Target reliability index for serviceability limit state of single piles

The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) of piles. Based on a two-parameter,hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index(βsls) is obtained using the mean value first order second moment(MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation(MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement(slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety(FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.

Residents'Satisfaction with Public Services in Mountainous Areas:An Empirical Study of Southwestern Sichuan Province,China

Enhancing the efficiency of public services is essential to residents in mountainous areas. It is also important to promote sustainable development of these regions. Analysing residents' satisfaction with public services in mountainous areas can help in evaluating outcomes of fiscal investment and identifying potential coping approaches for improving public service efficiencies. The residents' satisfaction with public services and the factors that influence such satisfaction were examined in this study. A study of 12 towns located in the southwestern Sichuan Province was performed using an entropy-weighted analytic hierarchy process(EWAHP), the technique for order preference by similarity to ideal solution(TOPSIS) and Tobit regression methods. The results indicate that: 1) the spatial distribution of satisfaction with public services is non-uniform, and the spatial distribution structure varies for different types of public services. 2) Residents' satisfaction with public services is influenced by both objective and subjective factors. Population density, economic distance, social and cultural divisions and elevation are the major objective factors, whereas bounded rationality, the hierarchy of needs and service expectations are the main subjective factors. The most effective strategies for enhancing residents' satisfaction with public services are likely to be clustering the population, choosing supply centres with different public services, regulating the cultural division in ethnic minority towns, selecting supply priorities in accordance with residents' needs, implementing targeted intervention policies and establishing ‘bottom-up' and ‘top-down' integrated decision-making mechanisms.

An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of CSPs.Many independent criteria should be considered when evaluating the services provided by different CSPs.It is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud alternatives.An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy.Moreover,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and consistency.According to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP approach.Furthermore,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.

医院和科室上下联动事前-事中-事后闭环服务抗菌药物管理模式构建与实践

目的 促进抗菌药物的合理应用。方法 基于循证证据建立医院和科室上下联动事前-事中-事后闭环服务的抗菌药物管理模式。分析闭环服务的关键节点和服务内容,落实网络化服务体系,完善特殊患者和限制级抗菌药物专项服务等闭环服务的网络干预系统,并比较实施前(2020年)和实施后(2021年至2023年)医院抗菌药物使用强度(AUD)及不合理医嘱类型。结果 2021年、2022年、2023年医院AUD分别为39.40 DDDs、32.10 DDDs、38.09 DDDs,均较2020年的57.80 DDDs低,且符合我国综合医院AUD的限定标准(40 DDDs)。2021年,抗菌药物不合理医嘱类型排名前3的分别为抗菌药物预防/治疗方案不合理、药物选择不合理、联合用药不合理;2022年,抗菌药物不合理医嘱类型排名前3的分别为抗菌药物预防/治疗方案不合理、用药疗程不合理、给药途径和剂量不合理;2023年,抗菌药物不合理医嘱类型排名前3的分别为给药途径和剂量不合理、用药疗程不合理、抗菌药物预防/治疗方案不合理。结论 医院和科室上下联动事前-事中-事后闭环服务的抗菌药物管理模式可有效降低医院AUD,促进抗菌药物不合理医嘱类型的转变,从药物治疗方案优化逐步过渡到抗菌药物剂量和疗程精细化。

求解有服务顺序限制的MDMOVRPTW的IHHO算法

针对有服务顺序限制的带时间窗的多需求多目标车辆路径问题(multi-demand and multi-objective vehicle routing problem with time window,MDMOVRPTW),在考虑多种需求由不同车辆按顺序服务等约束条件的同时,构建了最小化配送成本和最大化客户满意度的多目标模型。根据模型的特点设计了改进的哈里斯鹰优化(improved Harris hawks optimization,IHHO)算法,随机地将种群中部分支配解作为父代解,用临时组合算子和4种交叉算子搜索新解。最后,算例测试结果表明,相较于传统的哈里斯鹰优化算法,IHHO算法的求解性能得到了有效改善,各操作算子中交叉算子2的求解效果最好。将IHHO算法用于实例中,求解结果得到了改善,充分验证了IHHO算法的有效性。

基于互联网医院建设的肿瘤患者自助开单服务探索与实践

目的:探索自助开单服务模式的设计及实施效果,为患者提供高效、便捷的就医服务,提升患者服务效率。方法:采用χ~2检验、独立样本t检验对使用自助开单服务的患者及同期使用APP自约号来院就诊的患者的性别、年龄、来源、预约挂号至就诊的等待时间进行比较;测量系统应用后为患者节省的就医成本并对患者的就医流程进行比较。结果:使用自助开单服务的患者相比自约号的患者,老年人比例提升了5.4%(P=0.02)、京外患者比例下降了3.6%(P=0.028),患者预约挂号至就诊的平均等待时间减少了51.8个小时(P<0.01),平均为每位患者节省费用1 790元/次。结论:自助开单服务系统的应用有助于优化就医流程、缩短预约挂号到就诊的等待时间,节省就医成本,促进医疗资源合理配置,提高医疗服务效率。

OOPN-SOA架构下全球供应链业务优化研究

以全球供应链为研究对象,讨论全球供应链业务优化问题,区别于以往以博弈论工具为主求解全局优化的方法,提出基于对象Petrinet建模方法,给出建模步骤和模型活性检验算法。根据OPN工具的输入输出特性,设计基于SOA的服务进化策略实施步骤。案例检验采用宁晋地区电缆产业数据,该方法提出改善国际消费者金融服务和国内生产者金融服务两个建议,证明了方法的有效性。

图书馆微服务满意度的影响因素分析研究——基于Ordered Probit模型

图书馆微服务是指借助微信、微博、移动app等互联网工具产生的一种新型服务,论文通过Ordered Probit模型分析图书馆微服务的影响因素,探讨图书馆如何充分利用微服务的各项功能,提升读者的使用体验和满意度。

信息获取渠道对农机社会化服务的影响研究——基于非农就业视角

基于江汉平原372户水稻种植户调查数据,运用有序Probit模型实证考察信息获取渠道对农户农机社会化服务采纳程度的影响,并从非农就业视角探讨信息获取渠道对农户农机社会化服务采纳程度的影响机制。研究结果表明:媒介渠道、组织渠道和人际渠道对农户农机社会化服务采纳程度具有促进作用,经过稳健性检验结果依旧显著;媒介渠道、组织渠道和人际渠道对农户农机社会化服务采纳程度的促进作用通过非农就业实现。进一步异质性分析发现,信息获取渠道对农业收入占比低的农户农机社会化服务采纳程度具有正向影响,而对农业收入占比高的农户农机社会化服务采纳程度具有负向影响;信息获取渠道对受教育程度低的农户农机社会化服务采纳程度影响较小,而对受教育程度高的农户农机社会化服务采纳程度影响显著。因此,建议拓宽农户信息获取渠道,鼓励农户多渠道就业,并针对不同农户实行差异化扶持政策。

人口老龄化背景下老年人精神养老服务体系构建——以合肥市为例

人口老龄化背景下,老年人精神养老是对传统养老服务内涵的全新拓展,能够切实保障老年人的养老权益,是我国养老事业的发展方向与目标。受限于意识、资源与政策方面的支持力度,我国老年人精神养老服务存在一定的问题。以合肥市为例,这些问题主要包括老年人精神自养意识及能力不足,社区及邻里的作用未能充分发挥,政府的精神养老主导地位未体现等。为支持老年人的日常生活,满足老年人的精神养老需求,基于差序格局理论,提出通过积极提高老年人社会生活参与度,重点关注老年人的社会情感需求,加快完善精神养老相关法律法规,为老年人提供科学且完善的精神养老服务体系等建议,以期弥合老年人精神需求与精神养老服务的差距。

我国农村订单定向医学生履约意愿及影响因素研究

农村订单定向医学生免费培养项目(简称订单定向项目)是国家为加强农村基层卫生人才培养而实施的重要项目。我国基层医疗卫生人才总量不足、质量不高问题较为突出,实施好订单定向项目至关重要。农村订单定向医学生(简称定向医学生)履约意愿的提高是保证订单定向项目持续开展的关键。研究针对我国定向医学生履约意愿现状及影响因素研究进行综述。分析结果提示,国内针对定向医学生履约意愿开展了较多研究,不同地区定向医学生履约意愿存在明显差异,同一地区开展的不同研究结果差异也较大,国内学者主要从社会人口学因素和认知因素两个方面对定向医学生履约意愿的影响因素进行了探讨。后续应开展全国统一性调查,加强院校培养模式和政策因素与履约意愿的相关性探讨,并开展更多履约激励策略的研究,以达到提高定向医学生履约意愿的目的。

我国农村订单定向医学生免费培养项目相关政策分析

农村基层卫生工作是我国卫生工作的重点。为在农村地区吸引和留住卫生人才,提高农村基层卫生服务能力,近年来,国家和各省市自治区针对农村订单定向医学生免费培养项目(简称订单定向项目)出台了多项政策。对我国订单定向项目相关政策内容进行梳理,并对订单定向项目的实施情况以及存在问题进行分析,进而提出政策建议。研究结果显示,订单定向项目相关政策逐步完善,有力缓解了基层卫生人才数量不足、专业能力有待提升的问题,同时,为贫困人口接受高质量教育做出了重要贡献。然而,当前我国订单定向项目缺少对基层从医志向和素质的考核机制,缺乏明确基层医疗卫生机构建设标准,相关激励政策不够完善,缺乏基层全科医生培养模式要求,后续相关政策应进一步完善。

2022年安徽中医药大学第一附属医院静脉用药配置中心不合理医嘱分析

目的分析安徽中医药大学第一附属医院2022年度静脉用药配置中心(PIVAS)医嘱用药不合理的原因及干预现状,为药师合理审方提供参考,促进临床合理用药。方法收集医院PIVAS2022年住院患者医嘱用药不合理的干预记录,对不合理医嘱的审核与干预、病区分布、医嘱类型等情况进行分析,总结不合理医嘱发生原因,提出可行性对策。结果汇总发现不合理用药医嘱172份,不合格率为0.024%。不合理医嘱成功干预168组,干预成功率为97.67%。不合理医嘱数排名靠前的病区分别是脑病一区、脑病二区、心血管内科二病区、风湿内科二病区等病区,共占比79.65%,为不合理用药的主要病区。不合理医嘱占比较高的类型为溶媒用量不适宜和溶媒选择不适宜,占不合理医嘱总数的73.26%,为主要类型。结论PIVAS药师应增强合理用药意识,及时发现并纠正不合理医嘱,协助临床医师合理的药物治疗。

处方前置审核系统在静脉药物集中调配中心医嘱审核中的应用价值

目的分析处方前置审核系统在静脉药物集中调配中心(以下简称静配中心)医嘱审核中的应用价值。方法医院从2020年7月开始应用处方前置审核系统,比较其应用前(2020年1月—2020年6月,对照组)和应用后(2021年1月—2021年6月,观察组)医嘱审核情况,并随机选取这2个时间段静配中心医务工作人员各20名,调查其满意度。结果观察组不合理医嘱数占比为0.20%,明显低于对照组的0.73%(P<0.05);观察组遴选溶媒不适宜、输液浓度不适宜、医嘱录入错误、给药途径不适宜、给药剂量不适宜和配伍禁忌发生率明显低于对照组(P<0.05);观察组处方审核率(100.00%)明显高于对照组的90.65%(P<0.05);观察组人员满意度明显优于对照组(P<0.05)。结论处方前置审核系统的应用可有效减少静配中心不合理医嘱事件的发生,提高处方审核率,改善医嘱审核质量,提高人员满意度。

静脉用药调配中心药师干预不合理医嘱效果分析

目的为规范静脉用药医嘱和提高临床静脉用药合理水平提供参考。方法统计医院静脉用药调配中心(PIVAS)2021年10月至2022年9月的不合理医嘱,分析全院和各科室不合理医嘱的总体概况、分布情况及其发生原因,并对PIVAS药师干预效果进行评价。结果2021年10月至2022年9月,共开具PIVAS医嘱79520条,不合理医嘱552条(占0.69%)。不合理医嘱以肿瘤科、骨一科、胸外科最多,发生率分别为60.87%,14.31%,10.14%。不合理医嘱类型主要为用法用量不合理、溶剂不合理、给药浓度不合理、使用频率不合理,发生率分别为29.71%,21.92%,21.74%,19.20%。不合理医嘱发生原因主要与前期沟通不充分,临床理解不到位,药品说明书不熟悉等有关。通过PIVAS药师的审方和干预,加强与医师的沟通和培训,不合理医嘱数量逐步减少,不合理医嘱发生率基本控制在1%以内。结论PIVAS药师对不合理医嘱进行干预,及时发现并纠正不合理医嘱,可有效降低静脉输液药品因临床医嘱开具不规范、不合理而引发的用药风险,为提高静脉用药的安全性提供药学支持,确保临床静脉用药的合理性、安全性和有效性。