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

被引:144
作者
Cao, Yang [1 ]
Wang, Shilong [1 ]
Kang, Ling [1 ]
Gao, Yuan [2 ]
机构
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[2] Univ Waterloo, Dept Math, Waterloo, ON N2L 3G1, Canada
关键词
Cloud manufacturing (CMfg); Service selection and scheduling; TQCS; Relative superiority degree; Analytic hierarchy process (AHP); Ant colony optimization (ACO); ANT COLONY OPTIMIZATION; GENETIC ALGORITHM; RESOURCE; QOS; DECISION; SUPPORT; MODEL; CONSTRUCTION; SYSTEM; CELLS;
D O I
10.1007/s00170-015-7350-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:235 / 251
页数:17
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