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。

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

Abstract： Enhancing the efficiency of public services is essential to residents in mountainous areas. It is also important to promote sus- tainable 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 distribu- tion 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 dis- tance, 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 ser- vices 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. Keywords： mountainous areas; public services; residents＇ satisfaction; entropy-weighted analytic hierarchy process （EWAHP）; technique for order preference by similarity to ideal solution （TOPSIS）; Tobit regression; southwestern Sichuan Province

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.

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.

全渠道策略中线下有限库存服务决策优化研究

为了给顾客提供更好的购物体验,众多零售商引入了线上下单-线下提货(BOPS)购物渠道,从而使有限的线下库存面对来自多个渠道的需求.引入BOPS渠道是否对零售商有益,以及对线下有限库存服务决策优化尚缺乏研究.本研究考虑一个零售商和两类顾客,一类顾客为仅通过实体商店购买商品的实体店顾客,另一类为策略性地在各种渠道中进行选择的全渠道顾客.现有全渠道研究多假设零售商的线下库存优先满足实体店顾客需求,本研究将线下有限库存优先服务全渠道顾客需求策略(OPS)、优先服务实体店顾客需求策略(SPS)、按比例服务两类顾客需求策略(EPS)纳入考虑,与传统文献对比,发现引入BOPS并不完全对零售商有益,零售商实体店密度(决定顾客的商店访问成本)和配送中心的密度(决定顾客的线上等待成本)会对零售商收益造成影响.若顾客的线上等待成本与商店访问成本均较低,且商店访问成本更低时,零售商采用EPS和SPS服务策略会提高零售商利润,同时提高实体店顾客和全渠道顾客的消费者剩余.

基于概率逻辑推理的高阶互补云API推荐方法

云时代,云API作为服务交付、数据交换和能力复制的最佳载体,已成长为当今面向服务软件开发和企业数字化转型不可或缺的核心要素.然而动态开放网络中持续增长的云API在给开发者提供了更多选择的同时,也将其淹没在海量的云API选择之中,设计有效的云API推荐方法就此成为API经济健康发展中迫切要解决的现实问题.但是,现有研究主要利用搜索关键词、服务质量和调用偏好进行建模,生成质量高功能单一的云API推荐列表,没有考虑服务化软件实际开发中开发者对多元化高阶互补云API的客观需要.高阶互补云API推荐旨在为多个查询云API生成多元互补云API列表,要求推荐结果与查询云API均互补,以满足开发者的联合需求.针对此问题,本文提出基于概率逻辑推理的高阶互补云API推荐方法(Probabilistic Logic Reasoning for High-order Complementary Cloud API Recom⁃mendation,PLR4HCCR).首先,通过云API生态真实数据分析论证云API互补推荐需求的必要性和互补关系建模中替补噪声的客观存在,为云API互补推荐问题研究提供动机和数据支持.其次,采用Beta概率嵌入对云API及其之间的关系约束进行编码,以刻画云API间互补关系的不确定性和支持互补逻辑推理.接着,设计由投影、取反和交并三个基本逻辑算子构建的互补关系逻辑推理网络,使查询集中的每个云API获得非对称互补关系感知和替补噪声消解约束下的互补云API表示.然后,引入注意力机制为查询云API的互补云API分配不同权重,增强高阶互补云API基向量的表征能力.在此基础上,采用KL散度度量高阶互补云API基向量与候选云API之间的距离,并根据KL散度排序生成高阶互补性可感知下的云API推荐结果.最后,我们利用两个真实云API数据集在不同阶互补推荐场景下进行实验,实验表明,与传统启发式推荐方法和深度学习推荐方法相比,PLR4HCCR在互补关系感知推理和替补噪声消解方面均具有较大的优势,继而使其在低阶、高阶和混合阶互补云API推荐中均展示出更优的推荐效果和更强的泛化能力.进一步,超参数敏感性实验、实例分析和用户调查验证了方法的有效性、实用性和可行性,这使结合高阶互补关系的云API推荐方法PLR4HCCR不仅更有可能生成开发者满意的结果,而且可有效提升云API服务提供者的收益.

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。

公共服务的空间叙事:西藏易地扶贫搬迁社区的秩序重构

易地扶贫搬迁作为空间重构下的扶贫实践,是一种以空间换发展的可行性举措。易地扶贫搬迁群体由原来分散居住在农牧区村落到聚集生活在易地扶贫搬迁现代化社区,其生活方式、生产方式和社会关系都发生了巨大转变,且移民搬迁社区的公共服务呈现出一定的“空间阻隔”。对西藏易地扶贫搬迁S社区进行实地调查,运用社会空间理论从“空间的表征—空间的实践—表征性空间”三元辩证法出发,对易地扶贫搬迁群体生产空间、生活空间和心理空间进行分析,发现易地扶贫搬迁S社区处于过渡期的“阈限”阶段,且存在生活空间压缩分化、生产空间生计转型和关系空间更新重组的特点。要把政府对空间规划与移民对空间的使用通过制度化的方式进行重新架构和诠释,如通过加大政府与社区的互动力度、探索政府与市场的互补方式、实施社区与市场互助合作等举措,以期重构易地扶贫搬迁S社区秩序,为S社区易地扶贫搬迁群体提供可及的公共服务。

某院静脉用药调配中心抗肿瘤药物不合理医嘱评价结果分析

目的探讨某院静脉用药调配中心抗肿瘤药物不合理医嘱的原因,根据不合理的原因制定个性化的对策,规范患者合理、安全用药。方法收集某院2023年静脉用药调配中心抗肿瘤药物不合理医嘱的记录共258条,统计这些不合理医嘱的药物种类、科室分布,以及记录不合理医嘱的原因,并采取针对性的措施,减少不合理医嘱。结果统计分析显示,某院2023年静脉用药调配中心抗肿瘤药物不合理医嘱共258张,其中放疗科、肿瘤科、胸外科的不合理医嘱比率占前三位,分别为32.17%、21.71%、20.16%;不合理类型中溶媒选择不合理的占比最高,为43.41%,其次分别为溶媒用量、浓度不合理,用药剂量不合理,用药顺序不合理,配伍不当,其他,占比分别为29.07%、15.50%、5.04%、4.26%、2.71%。112张抗肿瘤药物溶媒选择不合理医嘱中,注射用依托泊苷溶媒选择不合理最多,占比为33.04%。75张抗肿瘤药物溶媒用量、浓度不合理医嘱中,注射用吉西他滨溶媒用量、浓度不合理最多,占比为24.00%。40张抗肿瘤药物用药剂量不合理医嘱中,复方苦参注射液超量使用最多,占比为40.00%。结论评价静脉用药调配中心抗肿瘤药物不合理医嘱发生具体原因,加强合理用药管理,采取针对性的措施,可保障抗肿瘤药物合理应用。

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。

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。

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。

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。

基于行动学习法的医院药师执业能力培养实践与思考

目的探讨基于行动学习法的培养模式对医院药师执业能力的提升效能。方法选取2022年9月至2023年12月在攀枝花市中心医院培训的药师30名,随机分为教改组(16人)和常规组(14人)。教改组采取基于行动学习法的常规带教,常规组采取常规带教法,比较两组药师在理论知识、实践操作、药学服务、应急处置与综合素质的差异。结果教改组药师在处方点评、临药用药分析、药学服务、应急处置、综合素质等方面优异于常规组,差异有统计学意义(P<0.05)。结论基于行动学习法的教学模式可以有效提升药师的高阶思维能力,帮助药师更好地运用医学知识和技能服务患者和医师。

OOPN-SOA架构下全球供应链业务优化研究

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