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Improve cloud manufacturing supply chain note‐enterprises optimize combination of the Cuckoo search
Author(s) -
Liang Hejun,
Sun Lily
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4764
Subject(s) - cuckoo search , particle swarm optimization , cloud computing , supply chain , cloud manufacturing , computer science , task (project management) , genetic algorithm , mathematical optimization , matlab , pareto principle , multi objective optimization , cuckoo , algorithm , engineering , business , mathematics , machine learning , systems engineering , zoology , marketing , biology , operating system
Summary To realize full sharing of mass enterprises in cloud manufacturing environment and free and fair cooperation among the supply chain note‐enterprises, this paper presents a method of combining multi‐Objective Cuckoo Search (MOCS) with Pareto to make optimization of task completion time and cost on supply chain in order to prove the viability and effectiveness of the method, doing the muti‐objective optimization model simulation experiment by MATLAB. The experimental results show that the algorithm performs better than present commonly used multi‐objective optimization genetic algorithm, particle swarm optimization, and artificial fish school algorithm, have better search capability and larger search range, escape from local optimal solution, and achieve the global minimum; the multi‐Objective Cuckoo Search can optimize the combination of supply chain enterprises on cloud manufacturing platform effectively.