Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition Evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.