A hybrid approach combining an extended BBO algorithm with an intuitionistic fuzzy entropy weight method for QoS-aware manufacturing service supply chain optimization

With the increasing complexity of manufacturing tasks, selecting an optimal manufacturing service supply chain has become an important challenge, especially in fuzzy manufacturing environments. In this study, we first propose a new fuzzy quality of service (QoS)-aware multi-objective mathematical model for evaluating the global QoS value of a manufacturing service supply chain including four basic composite structures. Then, we present a hybrid approach that combines the biogeography-based optimization (BBO) algorithm with the intuitionistic fuzzy entropy weight (IFEW) method, to effectively solve the manufacturing service supply chain optimization (MSSCO) problem. Furthermore, the IFEW method is adapted to obtain a more accurate preference weight for each QoS attribute, by further considering the degrees of influence of different decision makers. In addition, the BBO algorithm is extended to effectively obtain a manufacturing service supply chain (MSSC) with an optimal fuzzy QoS value by improving its standard migration and mutation operators, and introducing a new operator called the invasion operator. Finally, we perform three sets of simulation experiments to illustrate the practicality, effectiveness, and efficiency of our proposed method, based on comparisons with the standard BBO algorithm and two other population-based optimization algorithms, namely the genetic algorithm and differential evolution.