服务产品设计方案组合的决策方法

服务产品设计方案的选择或组合问题是服务企业运作管理中最重要的内容之一.研究了服务产品设计过程中方案组合的决策方法.考虑不同服务要素下的备选方案之间的方向性相容关系,以服务产品满足顾客期望的程度最大化和服务产品的总体相容度最大化为目标,建立了服务产品设计中方案组合的多目标优化模型.根据模型的特点,设计出相应的多目标进化算法对模型进行求解,获得服务产品设计中的最优方案组合.最后,通过一个移动传媒服务产品的实际例子,验证提出方法的可行性和有效性.     

基于游客感知服务质量的多目标旅游产品优化设计

旅游产品的优化设计问题是当今旅游企业运营管理中最重要的内容之一。从游客感知服务质量的角度针对旅游服务的六大要素以及实现要素的备选方案进行研究。通过引入期望服务质量和实际感知服务质量概念,提出了游客满意度最大、优化设计成本最小和旅游景区生态环境最优的单目标旅游产品优化设计模型,并且分析目标之间的权重,最终确定多目标的旅游产品优化设计模型。最后,通过一个亲子游产品优化设计的实际案例,对多目标模型的有效性进行了验证,为旅游服务企业在旅游产品设计方面提供更加可行的决策。     

改进试验设计法优化电动汽车按时间租赁利润

针对电动汽车租赁服务系统,结合顾客到达率瓶颈、顾客依据感知价值选择服务的特征及电动汽车特性,构建了按时间收费利润模型。以利润最大化为目标,采用改进试验设计法求解模型。先采用传统试验设计法求出最佳利润及因素取值组合,确定利润函数在最佳因素取值组合处的单调性,进而更精确地判断可取得最优利润的因素水平取值区间,根据精确化后的取值区间并保持各因素水平数不变,采用试验设计法再次优化利润。实例分析比较了试验设计法改进前后所得利润优化结果,显示改进后方法得出的利润显著更优,验证了模型的有效性和实用性。     

基于GIOWA算子的备件服务绩效评价方法

当前对服务绩效的研究更多的是对服务质量的衡量,但缺少从顾客感知的角度对服务业务的评价研究。同时,由于不同产品备件服务的属性重要性随着服务类型等因素的不同而有较大的差距,很难赋予服务属性固定的权重值。因此,为了全面分析备件服务绩效的影响因素,参考SERVQUAL模型建立了备件服务绩效评价体系。为了从顾客感知的角度对服务业务进行评价,采用GIOWA算子的方法以有效地融合评价体系中的多组模糊和不确定的客户评价信息。实例结果表明,该方法取得了对模糊语言评价的良好集结效果,为备件服务绩效评价提供了一种定性和定量相结合的评价方法。     

基于公理设计和相容决策支持问题法的稳健优化设计方法

 针对多目标稳健优化问题,建立了多目标稳健优化的损失函数,利用灵敏度分析方法确定各设计变量对各设计目标的影响程度,确定主要的设计参数,便于调整和控制设计参数的公差.根据信息公理与损失函数的一致性关系,建立以最小化各目标的总损失函数为目标函数.并在相容决策支持问题法框架基础上,提出一种基于公理设计和相容决策支持问题法的多目标稳健优化设计模型.实例分析表明,提出的方法是可行的.     

基于顾客满意度的产品概念设计评价研究

目的为实现产品设计过程的科学决策,对子过程产品概念设计进行顾客满意度量化评价,为决策者选择概念设计方案提供参考,在一定程度上解决产品概念设计评价活动的主观性和随意性。方法确定研究方案,搜集产品概念设计阶段方案的评价指标,运用层次分析法建立评价指标体系;构建对概念设计阶段方案的顾客满意度评价模型;运用模糊层次分析法进行指标权重即指标重要度的计算;运用模糊数学思想进行指标满意度的调查与计算,在此基础上进行概念设计方案的满意度量化评价,得到满意的决策方案。结论以台灯概念设计基于顾客满意度的评价,验证了方法的可行性,有助于为决策活动提供量化评价依据,从而更有效地进行后续设计。     

合同市场和现货市场中的零售商最优采购决策组合

考虑从组合市场中采购商品的零售商的最优决策问题,对两类具有不同优化目标和约束条件的零售商采购过程建模:1)第一类零售商希望在满足采购风险小于其最大风险承受能力的条件下使得采购成本期望最小;2)第二类零售商希望在满足最大采购成本期望值约束的条件下使得面临的采购风险最小.模型中现货市场采购价格、顾客需求为随机变量,通过计算,得到最优采购决策的解析解,并分析说明最优解和相关参数的关系.     

供应链绩效评价体系构造及对灰关联法的改进

首先区分不同主体,面向顾客选择指标,基于流程构建一个供应链绩效评价的指标体系;然后,提出一种改进的灰关联法。在多指标、多对象的评价中,这种方法只需要增加劣关联度的计算,就可以同时确定体系中各指标的权重和各个对象的综合绩效,大大减少了评价的工作量;最后利用提出的体系和方法进行案例研究。     

DVD在线租赁问题研究

本文为在线租赁DVD服务的网站设计一种分配方案。该方案保证顾客的满意度最大且DVD购买量最少。对于问题一我们首先根据会员租赁DVD的频率及其概率，算出会员租DVD频率的期望值，从而求出各种DVD满足条件的最少需求量。问题二是一个单目标优化问题。我们以顾客满意度为目标函数，建立0-1规划模型，利用Lingo软件求得最大满意度。经过检验，用此方法得到的结果是比较合理的。     

基于多目标模糊优选模型的工程施工方案优选方法

建设工程在施工过程中普遍具有影响目标众多、不确定性较强的特点。为了寻找出更加客观、更加适宜的施工方案,本文以河南省商丘市某热力管道工程为例,综合考虑工期、成本、质量、资源均衡度四个目标对工程施工的影响,引入粗糙数判断矩阵法确定权重,并将取得的权重代入模糊优选模型,经计算得出各个备选施工方案的相对隶属度。结果表明,在多目标模糊优选模型建立的评判标准下,相对隶属度最大的施工方案为最优选择。多目标模糊优选模型综合考虑了施工过程中对项目工程影响较大的几个目标,各目标权重的选取更加科学、可靠,可以为施工单位选取施工方案提供较为有效的决策依据,具有一定借鉴意义。     

基于RIR-MOO的定制产品优化设计方法研究

大规模定制是未来产品制造的发展方向,以低成本快速响应客户的个性化需求是定制产品设计的关键。以往对定制产品设计的研究缺少从个性化需求到产品参数的流程化设计指导方法,无法从定制产品个性化的客户需求出发,快速地关联设计目标并指导产品参数的设计。为此,提出了基于RIR-MOO （relative importance ratings and multi-objective optimization,相对重要性等级-多目标优化）的定制产品优化设计方法,从客户需求出发,将关键需求映射到相关的设计目标,再通过多目标优化（multi-objective optimization,MOO）方法快速求解产品参数,实现定制产品的快速响应和优化设计。首先,针对模糊、动态变化的客户需求,利用加权区间粗糙集分析方法对客户需求进行客观分析,排除争议性大的模糊需求,同时计算不同客户需求的相对权重,得到其相对重要性等级（relative importance ratings,RIR）;然后,提出一种基于转换矩阵的客户需求与设计目标关联的方法,根据关键需求与设计目标的关联度以及关键需求的RIR,对关键需求与设计目标进行匹配,实现关键需求到设计目标的转换;最后,基于MOO方法对设计目标对应的产品参数进行优化设计,实现约束条件下定制产品的多目标优化。以汽车发动机活塞机构优化设计为例,对基于RIR-MOO的优化设计方法的可行性进行验证。结果表明,所提出的方法可以快速响应定制产品的客户需求并指导其优化设计,可为定制化产品制造企业提供指导。     

考虑电量消耗的车辆调度优化研究

基于电动汽车电量消耗特性,考虑电动车里程、载重、顾客服务时间窗等约束,建立以配送总成本最小为目标的电动车调度优化问题模型;利用自然数编码的遗传算法,求解出电动车的配送路线以及车辆的充电计划,再结合枚举法,在配送中心运营时间内以10 min为时间间隔,计算出配送车辆惩罚成本最小时的最优发车时刻。最后结合算例,验证该模型和方法的有效性、正确性。     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

On the risk-averse optimization of service level in a supply chain under disruption risks

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.     

Product portfolio planning with customer-engineering interaction

A critical decision that faces companies across all industrial sectors is the selection of an optimal mix of product attributes to offer in the marketplace, namely product portfolio planning. The product portfolio planning problem has to date generally been considered from a marketing perspective, with the focus being on customer concerns i.e., how alternative sets of product attributes and attribute-level options interact and compete among the target customer segments. From the engineering perspective, the operational implications of product portfolio decisions have been tackled with a primary concern about the cost and complexity of interactions among multiple products in a manufacturing environment with increasing variety. Consideration of the customer and engineering interaction in product portfolio planning is becoming increasingly important, manifested by the efforts observed in many industries to improve the coordination of marketing, design and manufacturing activities across product and process platforms. This paper examines the benefits of integrating customer concerns over product offerings with more engineering implications. To leverage both the customer and engineering concerns, a maximizing shared-surplus model that considers customer preferences, choice probabilities and platform-based product costing, is proposed to address the product portfolio planning problem. A heuristic genetic algorithm procedure is applied to solve the mixed-integer combinatorial optimization problem involved in product portfolio planning. Initial findings from a case study on notebook computer portfolio planning suggests the importance of the research problem, as well as the feasibility and potential of the proposed framework.     

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.     

改进麻雀搜索算法求解多目标低碳冷链物流车辆路径问题

目的 在传统冷链物流的车辆路径问题模型基础上,考虑服务节点和车辆运输过程中产生的碳排放,并加入客户满意度,在有限资源情况下最小化路径成本和最大化客户满意度。方法 构建多目标低碳冷链物流车辆路径问题模型,将爬山算法局部搜索思想应用到麻雀搜索算法中,形成改进麻雀搜索算法,并用其对上海市某区域内的冷链物流配送路径优化问题算例进行求解。结果 通过与改进前及其他2种智能优化算法运行结果进行对比发现,改进后的麻雀搜索算法具有更快的寻优速度和更好的寻优能力,且改进后的算法对模型的碳排放效用性更高。结论 基于国家的低碳政策,设计出符合当下实情的低碳冷链物流运输模型,通过改进优化算法设计运输方案,验证了爬山算法局部搜索思想对麻雀搜索算法进行改进的有效性及所构建低碳冷链物流车辆路径模型的合理性。     

外卖配送路径多目标实时优化研究

随着外卖行业的不断发展,外卖配送的路径优化问题已引起学者们的广泛关注。但现有研究未将骑手的目标考虑在内,且未考虑动态场景下多目标如何设定权重的问题。因此,本文对外卖配送路径的多目标实时优化进行深入研究。建立多目标外卖配送路径优化模型。该模型不仅考虑订单履行时间、平台利润和骑手服务质量3个常用的目标,另外增加骑手等待时间和骑手空驶距离这两个目标,充分将外卖平台、顾客和骑手的目标综合考虑。设计动态调整权重的多目标外卖配送路径启发式算法,解决动态场景下多目标权重如何设定的问题。通过外卖配送的实时数据进行算例分析。结果表明,本文提出的算法可以有效对多目标的外卖配送问题进行实时路径优化,且订单的密集程度对骑手等待时间和订单履行时间有直接的影响。     

Improved approach to quality function deployment based on Pythagorean fuzzy sets and application to assembly robot design evaluation

Quality function deployment (QFD) is an effective method that helps companies analyze customer requirements (CRs). These CRs are then turned into product or service characteristics, which are translated to other attributes. With the QFD method, companies could design or improve the quality of products or services close to CRs. To increase the effectiveness of QFD, we propose an improved method based on Pythagorean fuzzy sets (PFSs). We apply an extended method to obtain the group consensus evaluation matrix. We then use a combined weight determining method to integrate former weights to objective weights derived from the evaluation matrix. To determine the exact score of each PFS in the evaluation matrix, we develop an improved score function. Lastly, we apply the proposed method to a case study on assembly robot design evaluation.