scholarly journals Optimized Configuration of Manufacturing Resources for Middle and Lower Batch Customization Enterprises in Cloud Manufacturing Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yinyun Yu ◽  
Wei Xu
Keyword(s):  
Customer Satisfaction ◽  
Manufacturing Systems ◽  
Programming Model ◽  
Model Simulation ◽  
Cloud Manufacturing ◽  
Economic Benefits ◽  
Optimal Configuration ◽  
Manufacturing Environment ◽  
Kano Model ◽  
Manufacturing Resources

The optimal configuration of manufacturing resources in the cloud manufacturing environment has always been the focus of research on various advanced manufacturing systems. Aiming at the problem of manufacturing resources optimization configuration for middle and lower batch customization enterprises in cloud manufacturing environment, this paper gives a bi-level programming model for manufacturing resources optimization configuration in cloud manufacturing environment which fully considers customer satisfaction and enterprise customization economic benefits. The method firstly identifies the relationship between customer demands and customer satisfaction through questionnaires and quantifies the Kano model effectively. Then, it uses Quality Function Deployment (QFD) to transform customer demand characteristics into engineering characteristics and integrates the qualitative and quantitative results of the Kano model. Next, the method establishes enterprise economic benefits function according to the factors of order quantity and input cost. Furthermore, a comprehensive nonlinear bi-level programming model is established based on cost, time, and quality constraints. The model is solved by intelligent algorithm. Finally, the validity and feasibility of the model are verified by model simulation of actual orders of an enterprise. This method effectively realizes the optimal configuration of manufacturing resources in the cloud manufacturing environment, while maximizing the interests of both suppliers and demanders.

Computational Trust in Cloud Manufacturing

2013 ◽  
Vol 774-776 ◽  
pp. 1908-1913 ◽  
Author(s):  
Lu Gao ◽  
Quan Liu ◽  
Ping Lou
Keyword(s):  
Manufacturing Systems ◽  
Service Providers ◽  
Cloud Manufacturing ◽  
Trust Model ◽  
Virtual Manufacturing ◽  
Manufacturing Environment ◽  
Collaborative Relationships ◽  
Computational Trust ◽  
Service Oriented ◽  
Manufacturing Resources

Cloud manufacturing is a new kind of advanced service-oriented network manufacturing paradigm. There are two kinds of nodes in this network manufacturing environment: manufacturing service nodes (service providers) encapsulated by manufacturing resources and task nodes (service customers). One of the bases of building up the collaborative relationships between customers and providers in cloud manufacturing environment is their reciprocal trust. However, vicious, mendacious, and inveracious information makes it quite difficult for customers to find reliable and high-quality providers to form virtual manufacturing systems for efficiently responding to market demands in cloud manufacturing environment, viz. service consumers often have insufficient information on service providers. The trustworthy network manufacturing environment is a prerequisite to implementation of cloud manufacturing. In this paper the notion of human trust is extended to the cloud manufacturing. A computational trust model which combines the direct computational reliability and the computational reputation is presented, and the simulating result confirms it valid.

scholarly journals Developing a Quick Response Product Configuration System under Industry 4.0 Based on Customer Requirement Modelling and Optimization Method

2019 ◽  
Vol 9 (23) ◽  
pp. 5004 ◽  
Author(s):  
Lee ◽  
Chen ◽  
Lin ◽  
Li ◽  
Zhao
Keyword(s):  
Customer Satisfaction ◽  
Industry 4.0 ◽  
Quality Function Deployment ◽  
Programming Model ◽  
Optimization Method ◽  
Product Configuration ◽  
Quick Response ◽  
Kano Model ◽  
Configuration System ◽  
Technical Specifications

In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital “make-to-order” (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system.

scholarly journals A Decision Method to Maximize Service Quality under Budget Constraints: The Kano Study of a Chinese Machinery Manufacturer

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qingliang Meng ◽  
Xiaochao Wei ◽  
Wen Meng
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Value Chain ◽  
Programming Model ◽  
Budget Constraints ◽  
Kano Model ◽  
Construction Machinery ◽  
Machinery Industry ◽  
Decision Method ◽  
Nonlinear Programming Model

Manufacturers are increasingly facing keen competition and improving customers’ position in the value chain. Many of them have made efforts to promote service quality and enhance customer satisfaction. Research is lacking in considering the trade-off between service costs and customer satisfaction when tackling service quality issues in the machinery industry. A decision method is proposed for maximizing service quality in machinery industry under budget constraints, from the perspective of enterprise capacity and customer satisfaction. Due to the strength of the Kano model in acknowledging the nonlinear impacts of quality elements on customer satisfaction, we formalize the relationship between customer satisfaction and sufficiency of service quality elements quantitatively. And then we develop a novel nonlinear programming model to maximize service quality under budget constraints. In particular, we implement our model at Xuzhou Construction Machinery Group Co., Ltd., one of the largest Chinese construction machinery companies, to validate the efficacy of the method.

OPTIMAL ANALYSIS OF FARM AGRICULTURAL MACHINERY EQUIPMENT BASED ON MATHEMATICAL MODELLING

2020 ◽  
pp. 333-340
Keyword(s):  
Mathematical Modelling ◽  
Production Efficiency ◽  
Programming Model ◽  
Constraint Equation ◽  
Economic Benefits ◽  
Agricultural Machinery ◽  
Optimization Analysis ◽  
Agricultural Modernization ◽  
Actual Equipment ◽  
Optimal Allocations

With the development of science and technology, the degree of agricultural mechanization is getting higher and higher. Agricultural machinery is an important support for the development of agricultural modernization. Optimizing the allocation of agricultural machinery is conducive to improving agricultural production efficiency and economic benefits. In this paper, mathematical modelling method is mainly used in the analysis and optimization of agricultural machinery configuration. By determining the objective function and constraint equation, combined with the actual situation of Xinjiang Production and Construction Corps, the linear programming model and workload model of agricultural machinery and equipment optimization are established. Finally, the actual number of agricultural machinery and equipment and the number of optimal allocations of Xinjiang Production and Construction Corps farm were compared. The effectiveness of the optimization model is verified by comparing the optimized agricultural machinery equipment with the actual equipment. The results show that the optimized equipment model has good optimization effect. On the basis of reducing the number of agricultural machinery and equipment, the matching rate of agricultural machinery is improved, and the operation cost of agricultural machinery is effectively reduced. It is hoped that this study can provide certain reference and reference for the optimization analysis of agricultural machinery and equipment based on mathematical modelling.

Sign in / Sign up

Export Citation Format

Share Document