A TQCS-based service selection and scheduling strategy in cloud manufacturing

Abstract

Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

Scheduling in cloud manufacturing: state-of-the-art and research challenges

“…In another work, they proposed a scheduling framework for cloud manufacturing with the specific consideration of resource service availability. Cao et al (2016) addressed the service selection and scheduling issue in cloud manufacturing. Although being entitled 'scheduling', the problem under consideration is actually a single-task-oriented service composition issue taking explicitly into account service occupancy over time.…”

The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing

“…. , λ 5 PE 1 = (23, 14,16,13,17), and 18,070, respectively. Now, for values α = 0.1 and 0.2, we construct enterprise networks in CM from day 2-30, as explained in Section 3.4, by means of the suggested algorithm, and a B and B algorithm for comparison purpose.…”

“…For example, Laili et al [12] analyzed the complex features of cloud services in cloud computing, and based on these features, they suggested a ranking chaos algorithm for service composition selection, and optimal computing resource allocation altogether in the private cloud. In Cao et al [13], the authors adopted a fuzzy decision-making theory to establish a manufacturing scheduling model in the MC considering four criteria: time, quality, cost, and service. Mai et al [14] proposed a framework for 3D printing services in the MC to handle several problems of the MC, such as evaluation, service matching, scheduling, etc.…”

“…For example, the models developed in Cao et al [13] and Wei et al [16] yields a manufacturing schedule, and the model developed in Li et al [15] allocates tasks only before the manufacturing process begins. In other words, their models cannot be applied to real-time situations.…”

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

“…Recently, inspired by the application of SOT to serviceoriented computing/manufacturing systems [38][39][40], applying SOT to CMfg systems has been of wide theoretical and application-based interests, where the issue of QoS-aware service composition for CMfg has become an active field of research [41][42][43]. As the vital factors to reflect the performance of QoS-aware service composition, overall QoS and success rate placed a constant emphasis in CMfg systems.…”