Integrating a prospect theory-based consensus-reaching process into large-scale quality function deployment and its application in the evaluation of contingency plan

2021 ◽  
pp. 1-20
Author(s):  
Yan Zhu ◽  
Chuanhao Fan ◽  
Jing Xiao ◽  
Shenghua Liu
Keyword(s):  
Prospect Theory ◽  
Quality Function Deployment ◽  
Large Scale ◽  
Management Tool ◽  
Quality Function ◽  
Contingency Plan ◽  
Large Numbers ◽  
Consensus Reaching Process ◽  
Evaluation Information ◽  
Consensus Reaching

Quality function deployment (QFD) is a quality management tool that aims to improve customer satisfaction by transforming customer requirements into technical characteristics. It is a crucial procedure to obtain the prioritization of technical characteristics for the products or services in QFD. Traditional QFDs are often implemented by a small number of QFD members. However, with the increasing complexity of product and service design, QFD requires the participation of more QFD members from dispersed departments or institutions. Additionally, the evaluation information given by QFD members may widely differ due to their different knowledge and background. Furthermore, the psychological behaviours of QFD members also greatly influence the final prioritization of technical characteristics. Hence, this paper proposes a novel QFD framework to prioritize technical characteristics using a consensus-reaching process and prospect theory when large numbers of QFD members are involved. In the large-scale QFD framework, prospect theory is generally utilized to depict the psychological behaviours of QFD members. Then, QFD members are divided into several clusters. Eventually, a consensus-reaching process is established to assist QFD members in reaching a consensus. To verify the practicability of the presented framework, this paper applies it to the evaluation of contingency plan to determine the critical measures.

A large scale consensus reaching process managing group hesitation

2018 ◽  
Vol 159 ◽  
pp. 86-97 ◽  
Author(s):  
Rosa M. Rodríguez ◽  
Álvaro Labella ◽  
Guy De Tré ◽  
Luis Martínez
Keyword(s):  
Large Scale ◽  
Consensus Reaching Process ◽  
Consensus Reaching

The Nuts and Bolts of Implementing Large-Scale Longitudinal Studies

1992 ◽  
Vol 7 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Magda Stouthamer-Loeber ◽  
Welmoet van Kammen ◽  
Rolf Loeber
Keyword(s):  
Staff Training ◽  
Large Scale ◽  
Longitudinal Research ◽  
Data Entry ◽  
Management Tool ◽  
Academic Training ◽  
Scientific Journals ◽  
Training And Supervision ◽  
Large Numbers ◽  
Set Up

Studies that assess large numbers of subjects for longitudinal research, for epidemiological purposes, or for the evaluation of prevention and intervention efforts, are very costly and should be undertaken with the greatest care to ensure their success. The success of a study, apart from its scientific merit, depends largely on the ability of the researcher to plan and set up a smoothly running operation. However, the skills required for such a task are often not acquired in academic training, nor do scientific journals abound with information on the practical aspects of running a large study. This paper summarizes the experience gained in executing a longitudinal study and covers aspects of planning, hiring of staff, training and supervision of interviewers, data collection and data entry and management. The importance of the use of the computer as a management tool is stressed.

Adaptive consensus reaching process with hybrid strategies for large-scale group decision making

2020 ◽  
Vol 282 (3) ◽  
pp. 957-971 ◽  
Author(s):  
Ming Tang ◽  
Huchang Liao ◽  
Jiuping Xu ◽  
Dalia Streimikiene ◽  
Xiaosong Zheng
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Large Scale ◽  
Group Decision ◽  
Consensus Reaching Process ◽  
Consensus Reaching

scholarly journals The Extension of Quality Function Deployment Based on 2-Tuple Linguistic Representation Model for Product Design under Multigranularity Linguistic Environment

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Ming Li
Keyword(s):  
Product Design ◽  
Quality Function Deployment ◽  
Decision Makers ◽  
Linguistic Information ◽  
Quality Function ◽  
Discrimination Ability ◽  
Linguistic Representation ◽  
Analysis Process ◽  
Representation Model ◽  
Evaluation Information

Quality function deployment (QFD) is a customer-driven approach for product design and development. A QFD analysis process includes a series of subprocesses, such as determination of the importance of customer requirements (CRs), the correlation among engineering characteristics (ECs), and the relationship between CRs and ECs. Usually more than group of one decision makers are involved in the subprocesses to make the decision. In most decision making problems, they often provide their evaluation information in the linguistic form. Moreover, because of different knowledge, background, and discrimination ability, decision makers may express their linguistic preferences in multigranularity linguistic information. Therefore, an effective approach to deal with the multi-granularity linguistic information in QFD analysis process is highly needed. In this study, the QFD methodology is extended with 2-tuple linguistic representation model under multi-granularity linguistic environment. The extended QFD methodology can cope with multi-granularity linguistic evaluation information and avoid the loss of information. The applicability of the proposed approach is demonstrated with a numerical example.

scholarly journals Two-Stage Consensus Reaching Process for Two-Sided Matching Based on the Cloud Model in Large Sharing Platform

2021 ◽  
Author(s):  
Huagang Tong ◽  
Jianjun Zhu ◽  
Xiao Tan
Keyword(s):  
Large Scale ◽  
Cloud Model ◽  
Sharing Economy ◽  
Uncertain Information ◽  
Two Stage ◽  
Industrial Internet ◽  
Internet Platform ◽  
Consensus Reaching Process ◽  
Resources Sharing ◽  
Consensus Reaching

Abstract The sharing economy plays an important role in economic development, and the sharing platform is the core point in the sharing economy. However, the large-scale sharing platform results in low-efficiencies and weak matching. To address the problem, we design a two-stage consensus reaching process for resources sharing in platform. Firstly, considering the time-consuming process of generating satisfaction, we design a new method to generate satisfaction based on large-scale mixed historical data, the cloud model is used to unify the mixed uncertain information. Then, we design a two-stage consensus reaching process to realize the stable matching. For the first stage, we maximize the total consensus of the two-sided individuals. For the second stage, to realize stable sharing, we focus on the individuals’ requirement of consensus, the platform’s strategies, such as discount and scheduling, are used to adjust their consensus. Finally, considering the hierarchy of the two-stage consensus reaching process, we establish bi-level programming to embody the features. We design an improved algorithm to deal with bi-level programming. Also, an industrial internet platform is used as an example to verify the method and algorithm.

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