ABSTRACT Cloud manufacturing (CMfg) is a new service-oriented production paradigm from the wide application of cloud computing for the manufacturing industry. The aim of this manufacturing mode is to provide resource-sharing and on-demand manufacturing mode to improve operation efficiency. Resource allocation is considered as a crucial technology to implement CMfg. Traditional resource allocation approaches in CMfg mainly focus on the optimal resource selection process, but the energy consumption for manufacturing resources is rarely considered. In response, this paper proposes a hybrid energy-aware resource allocation approach to help requestors acquire energy-efficient and satisfied manufacturing services. The problem description on energy-aware resource allocation in CMfg is first summarized. Then a local selection strategy based on fuzzy similarity degree is put forth to obtain appropriate candidate services. A multi-objective mathematical model for energy-aware service composition is established and the nondominated sorting genetic algorithm (NSGA-II) is adopted to conduct the combinatorial optimization process. Furthermore, a technique for order preference by similarity to an ideal solution is used to determine the optimal composite services. Finally, a case study is illustrated to validate the effectiveness of the proposed approach.
Zheng REF proposed a hybrid energy-aware resource allocation approach to help users acquire energy-efficient and satisfying manufacturing services.
A Hybrid Energy-Aware Resource Allocation Approach in Cloud Manufacturing Environment
IEEE Access
IEEE Access
Computer Science