Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency

Wang Lei,Liu Xiang,Steve Evans,Zhao Hui,Xia Xu-Hui,Zhang Zelin

doi:10.1109/access.2021.3089896

Abstract

Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods to be changed: the products in GGRMS should be divided into multiple parts for maximizing residual value of different parts. However, this increases the difficulty of resource matching for service activities. To improve the efficiency of resource matching, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set, and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, we, using a retired manual gearbox an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is suitable for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of RMS.

Highlights

Nowadays, a large amount of various machinery products in China have been starting to entered into the peak of retirement [1]
Service providers offer remanufacturing services for specific needs, which can be divided into two types: 1) Remanufacturing providers, which include productive service activities such as evaluation, cleaning, testing, processing, and assembly of retired mechanical products, are the core of Generalized Growth-oriented Remanufacturing Services (GGRMS); 2) Service resource providers are mainly responsible for providing relevant equipment, technical personnel and series of technical support, according to the Generalized Growth Scheme (GGS) of the Remanufacturing Service (RMS) integration platform, and under its supervision, as an outsourcing company to transfer service resources to remanufacturing providers
To improve the efficiency of service resources matching under the GGRMS mode, this paper considers coupling relevance of service activities, and merges redundant service activities between different GGSs before matching service resource

Summary

INTRODUCTION

A large amount of various machinery products in China have been starting to entered into the peak of retirement [1]. Wang L. et al.: Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency (* 2021). Service providers offer remanufacturing services for specific needs, which can be divided into two types: 1) Remanufacturing providers, which include productive service activities such as evaluation, cleaning, testing, processing, and assembly of retired mechanical products, are the core of GGRMS; 2) Service resource providers are mainly responsible for providing relevant equipment, technical personnel and series of technical support, according to the GGS of the RMS integration platform, and under its supervision, as an outsourcing company to transfer service resources to remanufacturing providers. This paper proposed an RMSRA optimization method based on the GGRMS model, aiming to improve the matching efficiency of service resource and eventually maximizing the residual value of complex mechanical single products, and provided the guidelines for the resource allocation process under the RMS integrated platform. (3) Different from the traditional remanufacturing resource allocation process, the flexible factor of resource allocation is added, which improves the interests of both the service integrators and the service providers under the GGRMS model

Assembly

MODEL SOLVING

CASE STUDY

CONCLUSION AND FUTURE WORK