Using linguistic preferences and grey relational analysis to evaluate the environmental knowledge management capacity

This study proposes a combined fuzzy grey relational analysis method based to deal with study objective. This study objective is aimed to present a perception approach to deal with supplier evaluation of environmental knowledge management capacities (EKMC) with uncertainty and lack of information. The ranking of best supplier might be a key strategic direction of other suppliers prior to EKMC. The solving procedure is as follows, (i) the weights of criteria and alternatives are described both in qualitative and quantitative information using fuzzy set theory; (ii) using a grey relational analysis to result the ranking order for all alternatives; (iii) an empirical study of supplier ranking problem in EKMC are used to resolve with this proposed approach and the result indicates that optimal supplier is with higher protection of the environmental knowledge from inappropriate or illegal use or theft (C7) and from the best alternative supplier to study its criteria ranking.     

Integrated model of hot spring service quality perceptions under uncertainty

Integrating service quality perceptions from customer expectation and employee performance groups in subjective and objective functions are the main concern of service business today. However, to evaluate service quality criteria in linguistic preferences are difficult to present as exact numerical values. Hence, this study proposes a fuzzy extension of Decision Making Trial and Evaluation Laboratory (DEMATEL) in perceptions to address intertwined criteria by applying fuzzy set theory to evaluate the subjective and objective functions and applies extension of DEMATEL to evaluate the model for strategic goals. The empirical result shows that two groups of perceptions can be combined into a visual model to further develop the strategic concerns.     

A new QoS ontology and its QoS-based ranking algorithm for Web services

Web service composition is a promising solution for building distributed applications on the Internet in which Web service discovery is a key step. With a number of Web services having similar functionality, it is necessary to rank those services to select the best Web services for a request. QoS information which can reflect user’s expectation and experience of using a service is often used as a distinguish factor in a service ranking algorithm. Different service providers and participants may use different QoS concepts for describing service quality information. Therefore, it leads to the issue of semantic interoperability of QoS. In this paper, we propose a novel approach for designing and developing a QoS ontology and its QoS-based ranking algorithm for evaluating Web services. The QoS ontology can support not only describing QoS information in great detail but also facilitating various service participants expressing their QoS offers and demands at different levels of expectation. The QoS-based ranking algorithm adopted Analytic Hierarchy Process (AHP), a multiple criteria decision making technique, as an underlying mechanism for developing a flexible and dynamic ranking algorithm. The proposed QoS ontology and ranking algorithm can be used in various applications in order to facilitate automatic and dynamic discovery and selection of Web services.     

A best-matching protocol for order fulfillment in re-configurable supply networks

Order fulfillment is a process which encompasses all the activities from the inquiry of goods by the customer to the final delivery of goods to the customer. The most important activity of the order fulfillment process is the selection of the order fulfilling agent in the supply network. The selection of the agent involves multiple criteria based on quantitative and qualitative metrics and requires several self-interested agents and organizations to dynamically form and configure supply chain. This article describes a methodology for selection of an order fulfillment agent in a collaborative, geographically distributed network by developing a Best Matching Protocol (BMP). The BMP developed, enables better matching of fulfillment agents with customers in a given supply network, by determining which agent best satisfies the pre-defined quality and cost requirements of the customer. The protocol enables collaboration between the agents of the Supply Network (SN) and provides a scalable solution for the increasing size of the SN.     

基于多项Logit模型网上订单的最佳配送时间实证分析

随着互联网的不断普及和飞速发展,上网购买商品正成为人们日常消费的主要方式之一,提供网上购物服务的商家也日益增多.但不同的商家所提供的商品的价格、服务质量不尽相同,因此,商品的价格、订单配送时间的长短等都是影响顾客与商家的买卖是否成交的重要因素.本文就是站在商家的立场上,从顾客对商家所提供的商品价格、服务满意度出发,建立一个顾客选择商家的数学模型——多项Logit模型,然后再以商家的期望收益最大为目标,确定最佳的顾客订单指定配送时间.     

Using BPNN and DEMATEL to modify importance–performance analysis model – A study of the computer industry

The importance–performance analysis (IPA) model has been widely used as the primary tool for customer satisfaction management. IPA is a 2-D matrix analysis based on the importance and performance of the organization from the customer perception of quality. The firm’s customer satisfaction management strategy is formulated according to the IPA analysis results. However, both conventional and modified IPA models have important hidden assumptions: (1) assumptions regarding the importance of quality characteristics and performance; (2) the assumption that performance and satisfaction have a linear relationship; (3) that quality characteristics are mutually independent variables, with no causal relationship. Under these assumptions, if the quality characteristics cannot meet the above-mentioned assumptions, the conventional and modified IPA models will not accurately analyze the importance and priority ranking for improvement, leading to wrongful decision making. This study puts forth a new decision making and analysis methodology that will, on one hand, exploit the back-propagation neural network (BPNN) to establish quality characteristics and the hidden important integral satisfaction assumptions. The decision making trial and evaluation laboratory (DEMATEL) is used to calculate the causal relationship and extent of mutual influence among the qualities to adjust the importance of the quality characteristics and identify the core Order-Winners and Qualifiers problems. The proposed method modifies the quality importance and improves the IPA model ranking and also resolves difficult practical problems with fewer resources. This study illustrates using Taiwan industrial computer, working in conjunction with IPA models established with BPNN and DEMATEL to observe its application and effect.     

A criteria weighting approach by combining fuzzy quality function deployment with relative preference relation

In past, fuzzy multi-criteria decision-making (FMCDM) models desired to find an optimal alternative from numerous feasible alternatives under fuzzy environment. However, researches seldom focused on determination of criteria weights, although they were also important components for FMCDM. In fact, criteria weights can be computed through extending quality function deployment (QFD) under fuzzy environment, i.e. fuzzy quality function deployment (FQFD). By FQFD, customer demanded qualities expressing the opinions of customers and service development capabilities presenting the opinions of experts can be integrated into criteria weights for FMCDM. However, deriving criteria weights in FQFD may be complex and different to multiply two fuzzy numbers in real world. To resolve the tie, we will combine FQFD with relative preference relation on FMCDM problems. With the relative preference relation on fuzzy numbers, it is not necessary multiplying two fuzzy numbers to derive criteria weights in FQFD. Alternatively, adjusted criteria weights will substitute for original criteria weights through relative preference relation. Obviously, adjusted criteria weights are clearly determined and then utilized in FMCDM models.     

SANet: a service-agent network for call-center scheduling

We consider a network of service-providing agents, where different agents have different capabilities, availability, and cost to solve problems. These characteristics are particularly important in practice for semi-automated call centers which provide quality customer service in real time. We have developed SANet, a service agent network for call center automation, to serve as an experimental testbed for our research. SANet can select appropriate agents to provide better solutions for customer problems according to the changing capabilities and availability of service agents in the network. It can also add or delete appropriate agents to balance problem-solving quality, efficiency, and cost according to the number and types of incoming customer problems. On this network, each service agent can be a human service agent, an automated software service agent, or a combination of the two. This paper describes the architecture, a problem scheduling algorithm and an agent assignment algorithm on the SANet. We highlight an application in which we apply SANet to a call-center scheduling problem for a cable-TV company. Finally, we show the efficiency and adaptability of our system via experimental results and discuss related works.     

FCM-based customer expectation-driven service dispatch system

Maintaining long-term customer loyalty has been an important issue in the service industry. Although customer satisfaction can be enhanced with better service quality, delivering appropriate services to customers poses challenges to service providers, particularly in real-time and resource-limited dynamic service contexts. However, customer expectation management has been regarded as an effective way for helping service providers achieve high customer satisfaction in the real world that is nevertheless less real-time and dynamic. This study designs a FCM-based customer expectation-driven service dispatch system to empower providers with the capability to deal effectively with the problem of delivering right services to right customers in right contexts. Our evaluation results show that service providers can make appropriate decisions on service dispatch for customers by effectively managing customer expectations and arranging their contextual limited resources and time via the proposed service dispatch system. Meanwhile, customers can receive suitable service and obtain high satisfaction when appropriate services are provided. Accordingly, a high-performance ecosystem can be established by both service providers and customers who co-create value in the dynamic service contexts.     

A PSO-based intelligent service dispatching mechanism for customer expectation management

In the era of experience economy, service providers have to provide customers with high quality service experience in order to attract more customers and achieve higher customer satisfaction. Managing customer expectation is a critical approach for service providers to consider. Although customer expectation has been discussed across different research disciplines, to our knowledge, there is still no systematical and feasible way to apply customer expectation management into real environments. This study attempts to establish an intelligent service dispatching mechanism by using particle swarm optimization for customer expectation management. This mechanism can help service providers design and deliver satisfactory service experience to customers. In order to evaluate the effectiveness and robustness of this mechanism, this study employs micro- and macro-simulation experiments to confer and analyze its performance. The simulation results show service providers can gain benefit and raise customer satisfaction by managing customer expectation during service experience delivery. Meanwhile, customers can also receive memorable experiences and have positive responses to service providers and other customers. Consequently, a high performance ecosystem within service providers and customers can be formed.     

Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS

In this study, a new interval type-2 fuzzy multiple-attribute decision making model is developed by integrating Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Decision Making Trial and Evaluation Laboratory (DEMATEL). The proposed model utilizes hierarchical decomposition approach for reducing inherent complexity of the decision making problems. Additionally, interdependencies among problem attributes are taken into consideration by using interval type-2 fuzzy DEMATEL method. Finally, ranking orders of the alternatives are obtained by hierarchical interval type-2 fuzzy TOPSIS method. As there are several forms of interactions among criteria in real life settings, decision makers should be provided with the expert and intelligent systems which can overcome the preferential independence assumption. The proposed method is able to model causal dependencies by using interval type-2 trapezoidal fuzzy sets. The proposed method is implemented in a Strengths, Weaknesses, Opportunities, and Threats (SWOT)-based strategy selection problem.     

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast.The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.     

Assessing Web-enabled call center technologies

E-commerce's explosive growth has seen corporate Web sites mature from electronic versions of glossy brochures to full-service storefronts. To support this new environment, companies are providing real-time customer service to their Web customers. Despite this tremendous e-commerce growth, many people remain reluctant to complete a Web transaction without first talking to a live agent. Corporations are linking their Web sites to call centers where pools of trained agents can assist Web shoppers that need help in real time. To provide Web visitors with instant customer service, retailers are adding a talk-to-agent button on Web pages. When a visitor presses the button, the Web site will present the caller with several options for actuary talking to an agent. These options include e-mail, text chat, agent callback, and Internet telephony. There are two main technology alternatives that a call center can implement for Web integration. The first technology enables a traditional call center that has circuit-switched-based systems to support talk-to-agent alternatives. The second technology is the implementation of an all-IP call center infrastructure. Mitretek's Call Center Lab has assessed several products     

Extension of Fuzzy ELECTRE based on VIKOR method

Many decision problems in real-world deal with conflicting criteria, uncertainty and imprecise information. Some also allow a group of decision makers (DMs) to make their opinions independently. Multi-criteria decision making (MCDM) is a well known decision method that can make the quality of group multiple criteria decisions better by creating a more explicit, rational and efficient process. A group of MCDM models known as “outranking methods” have been used to rank a set of alternatives. ELECTRE I is an outranking method which is simple, but provides partial ranking. So we consider VIKOR and try to mitigate this problem with regard to relations between VIKOR and ELECTRE. The objective of this paper is to extend ELECTRE I method based on VIKOR to rank a set of alternatives versus a set of criteria to show the decision maker’s preferences.     

基于95598大数据的电力客户满意度分析

客户满意度反映了客户期望值与客户体验的匹配程度,是评价客户服务质量的一个重要指标。在电力客户满意度研究中,最重要的是获取真实的客户满意度评价结果。本文利用电力客户服务所积累的真实业务数据,提出了基于分层结构的客户满意度得分判定方法,该方法具有严谨的三层结构设计,与实际数据紧联系,逻辑逐层递进,互为补充,从而实现了从模糊的满意度评价到直观的满意度得分的顺利转化。随后通过实际应用案例研究,将电力客户满意度评价与具体的业务过程相联系,提出研究技术路线,对抽取的业务数据进行满意度得分转化,并利用R语言挖掘客户满意度较低的深层原因。所提方法突出了电力客户服务测评的综合性,能客观地从用户体验的角度反映服务质量,具有现实应用价值。     

Expectation-based coopetition approach to service experience design

Service experience design is a key issue in the service industry. Satisfactory service experiences provide customers with good memories and increase customer satisfaction, thus increasing customer loyalty and service provider profitability. However, providing customers with quality service experiences requires considering numerous factors, and thus is a complex and difficult issue both in academia and in real service contexts. Customer satisfaction results from the difference between customer perceptions and customer expectations. Hence, customer expectation management is important in delivering quality service experiences. Additionally, a competitive relationship exists among different service providers as well as between service providers and customers. The service experience delivery process can be regarded as a value co-creation process for service providers and customers who can implement either a competitive or cooperative strategy based on their goals and needs. This study accordingly presents an expectation based coopetition approach by using a real exhibition data of AutoTronics 2009 for simulations. The evaluation results show that this expectation-based coopetition approach can help service providers design and deliver quality service experiences and co-create value with customers, yielding a high performance ecosystem.     

A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality

Managing service quality is vital to retain customer satisfaction and augment revenues for any business organization. Often it is difficult to assess service quality due to lack of quantifiable measures and limited data. In this paper, we present a hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating service quality of urban transportation systems. The proposed approach consists of three steps. The first step involves development of a SERVQUAL based questionnaire to collect data for measuring transportation service quality. The participants provide linguistic assessments to rate the service quality criteria and the alternatives. In step 2, the linguistic ratings are combined through fuzzy TOPSIS to generate an overall performance score for each alternative. The alternative with the highest score is finally chosen. In step 3, sensitivity analysis is conducted to evaluate the influence of criteria weights on the decision making process.The strength of the proposed approach is its practical applicability and ability to provide solution under partial or lack of quantitative information. An application of the proposed approach for evaluation of service quality of metro in Montreal is provided.     

Fuzzy application in service quality analysis: An empirical study

Service activities have become the fundamental and dominant factors of the economic system over the past three decades and the significance and influence of service quality have been recognized through the great effect on customer satisfaction and customer loyalty. It should be noted that the assessment results obtained from multiple attributes decision-making problems of diverse intensity, such as service quality evaluation, may be misleading if the fuzziness of subjective human judgment is not taken into account. This paper develops an analysis architecture, which consists of fuzzy measurement of P–I gap, modified P–I analysis for attributes and ranking order determination for subjects, to deal with service quality measurement more effectively. The fuzzy measurement of P–I gap takes advantage of including the vagueness of evaluators’ judgment. By using the results of modified P–I analysis, effective ways for improving service quality perceptions can be focused on the attributes of high importance yet poor quality; meanwhile, resources or efforts attached to the attributes of low importance and good quality can be shifted to those of high importance yet poor quality. According to the ranking order of subjects, managers could devote more efforts to assist the subjects with inferior rankings to improve the service activities. The proposed analysis architecture can be used to investigate service quality effectively and track the trends periodically. An empirical study is conducted by using the proposed approach.     

Optimal location selection for an international distribution center by using a new hybrid method

The selection of a location for an international distribution center (IDC) is a most important decision for international logistics managers owing to the need to consider various criteria that involve a complex decision process in which multiple requirements and uncertain conditions have to be taken into consideration simultaneously. Moreover, the criteria often exist simultaneously as independent and dependent characteristics when the problems of location selection have become very complex. A new hybrid method combining the concepts of fuzzy DEMATEL and a new method of fuzzy multiple criteria decision-making (MCDM) in a fuzzy environment is proposed to solve the problems of IDC location selection. In this paper, the fuzzy DEMATEL is proposed to arrange a suitable structure between criteria, and the analytic hierarchy/network process (AHP/ANP) is used to construct weights of all criteria. The linguistic terms characterized by triangular fuzzy numbers are used to denote the evaluation values of all alternatives versus various criteria. Finally, the aggregation fuzzy assessments of different alternatives are ranked to determine the best selection. Furthermore, this paper uses an empirical case for optimal location selection for an IDC in Pacific Asia to illustrate the proposed method, and the results show that the method is an effective means for tackling fuzzy MCDM problems.     

A Model for Decision Making with Missing,Imprecise, and Uncertain Evaluations of Multiple Criteria

In real‐life multicriteria decision making (MCDM) problems, the evaluations against some criteria are often missing, inaccurate, and even uncertain, but the existing theories and models cannot handle such evaluations well. To address the issue, this paper extends the Dempster–Shafer (DS)/analytic hierarchy process (DS/AHP) approach of MCDM to handle three types of ambiguous evaluations: missing, interval‐valued, and ambiguous lottery evaluations. In our extension, the aggregation of criteria's evaluation takes the following six steps: (i) calculate the expected evaluation interval and the ambiguity degree of each group of decision alternatives regarding each criterion, (ii) from them to obtain the preference degree of each group of decision alternatives, (iii) apply the DS/AHP method to obtain the mass function distribution of each group of decision alternatives, (iv) use the Dempster's rule of combination to get the overall mass function of each group of decision alternatives with respect to all criteria, (v) according to the overall mass function to count the belief function and the plausibility function of each decision alternative, and (vi) set the overall preference ordering of decision alternatives by our regret‐avoid ambiguous principle and then find the optimal solution. Finally, we give an example of real estate investment to illustrate how our approach is employed to deal with real‐life MCDM problems.