A Unique Bifuzzy Manufacturing Service Composition Model Using an Extended Teaching-Learning-Based Optimization Algorithm

Yushu Yang, Jie Lin () and Zijuan Hu
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Yushu Yang: School of Economics and Management, Tongji University, Shanghai 200092, China
Jie Lin: School of Economics and Management, Tongji University, Shanghai 200092, China
Zijuan Hu: School of Economics and Management, Tongji University, Shanghai 200092, China

Mathematics, 2024, vol. 12, issue 18, 1-26

Abstract: In today’s competitive and rapidly evolving manufacturing environment, optimizing the composition of manufacturing services is critical for effective supply chain deployment. Since the manufacturing environment involves many two-fold uncertainties, there are limited studies that have specifically tackled these two-fold uncertainties. Based on bifuzzy theory, we put forward a unique bifuzzy manufacturing service portfolio model. Through the application of the fuzzy variable to express quality of service (QoS) value of manufacturing services, this model also accounts for the preferences of manufacturing firms by allocating various weights to different sub-tasks. Next, we address the multi-objective optimization issue through the application of extended teaching-learning-based optimization (ETLBO) algorithm. The improvements of the ETLBO algorithm include utilizing the adaptive parameters and introducing a local search strategy combined with a genetic algorithm (GA). Finally, we conduct simulation experiments to show off the efficacy and efficiency of the suggested approach in comparison to six other benchmark algorithms.

Keywords: manufacturing service composition; bifuzzy theory; bifuzzy variable; extended teaching-learning-based optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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