Work

Service Composition Modeling and Optimization Method for QoS Evaluation of Intelligent Manufacturing Supply Chain under Ripple Effect

Year: 2025

Type: article

Abstract: Aiming at the problem of QoS（Quality of Service）evaluation inaccuracy and ripple effect transmission delay caused by insufficient dynamic regulation of service time attributes in intelligent manufacturing supply chain, this study proposes a resilient Manufacturing Service Composition And Optimization Selection method (IMSCOS) based on QoS time attribute lean evaluation. Firstly, the limitations of the preset parameter fixation and linear response mechanism of the traditional resilience management model are broken through. By deconstructing three-dimensional indicators such as service cycle elasticity coefficient, time window dynamic fault tolerance threshold and task coupling delay sensitivity, a service composition resilience evaluation model with spatiotemporal correlation characteristics is constructed. On this basis, a multi-objective optimization algorithm integrating time constraint relaxation mechanism and chaotic adaptive perturbation strategy is designed to achieve a dynamic balance between Pareto front convergence efficiency and solution space distribution diversity. Experimental results show that compared with traditional optimization methods, the IMSCOS method improves key indicators such as manufacturing cycle volatility and service reliability index by 28.6% and 19.4% respectively, and shortens the service composition reconstruction response time by 32.8%, effectively improving the resilience buffering capacity of the supply chain system to time attribute disturbances. This study provides a solution with both theoretical value and engineering application prospects for building a spatiotemporal collaborative service composition decision system for intelligent manufacturing supply chain.

Source: International Journal of Frontiers in Engineering Technology

DOI: 10.25236/ijfet.2025.070104

Author: <p>Xiaomo Yu</p>

Institution: Nanning Normal University

Language: English

Topic: Supply Chain Resilience and Risk Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: National Natural Science Foundation of China, Guangxi University