Abstract
Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.
Highlights
With extensive application of the Internet, big data, and cloud computing in industry, cloud manufacturing (CMfg), a new service-oriented business model, was proposed in 2010 [1,2]
For the multi-objective service selection and scheduling (SSS) problem with linguistic preference in cloud manufacturing (CMfg), a novel two-phase interactive optimization method is proposed in this paper
The order of the desirable satisfying degrees is introduced and used to express the vague relative importance among the objectives showed by linguistic information
Summary
With extensive application of the Internet, big data, and cloud computing in industry, cloud manufacturing (CMfg), a new service-oriented business model, was proposed in 2010 [1,2]. In the last ten years, it has received more and more attention from industrial enterprises and from researchers [3,4,5,6]. In order to realize its objective, distributed manufacturing resources are aggregated by a common cloud platform and encapsulated into different kinds of manufacturing services [12,13]. These virtual services will be provided to clients or users in the pay-as-you-go mode. Given the various needs of clients, flexible selection and scheduling of these services become a significant challenge
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