Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition

TL;DR

This paper presents a novel framework for optimizing service collaboration and composition in cloud manufacturing to enhance efficiency and reduce costs in customized production, using a problem decomposition genetic algorithm.

Contribution

It introduces a new mathematical model and a genetic algorithm for optimal service composition considering collaboration, with demonstrated effectiveness in a smart clothing customization case.

Findings

Reduced production time in simulations

Lowered costs compared to baseline methods

Fewer services selected for optimal solutions

Abstract

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services.