Computational experiment-aided prescriptive decision-making for complex supply chains: A case of multi-generation smartphone marketing

•This study proposes a computational experiment-aided prescriptive decision-making framework.•The framework predicts multivariate emergences and exploits emergence diversity.•The proposed framework enables optimal prescriptions with improved solution stability.•A case study shows the framework can e...

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Vydáno v:Expert systems with applications Ročník 228; s. 120451
Hlavní autoři: Long, Qingqi, Chen, Yingni, Wang, Yongheng, Xu, Le, Zhang, Shuzhu, Peng, Juanjuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.10.2023
Témata:
Computational experiment
Entropy-TOPSIS
Evolutionary multi-objective algorithm
Multivariate evolution
Prescriptive decision-making
Supply chain
Supply chain
Computational experiment
Evolutionary multi-objective algorithm
Entropy-TOPSIS
Prescriptive decision-making
Multivariate evolution
ISSN:0957-4174, 1873-6793
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Shrnutí:•This study proposes a computational experiment-aided prescriptive decision-making framework.•The framework predicts multivariate emergences and exploits emergence diversity.•The proposed framework enables optimal prescriptions with improved solution stability.•A case study shows the framework can exploit multivariate performances and obtain prescriptions. A supply chain is a complex multivariate evolutionary system that challenges traditional decision-making paradigms and calls for new decision-making frameworks which can comprehensively support multivariate potential emergence prediction and further extend the decision-making from prediction to prescription. To fill these methodological gaps, a computational experiment-aided prescriptive decision-making framework was proposed for the analysis of complex supply chain evolution. The proposed framework enables the exploration of multivariate emergences for solution optimization in a closed-loop interactive iteration between computational experiments and evolutionary multi-objective algorithms. This framework enhances the solution stability behind multivariate emergences and can support Pareto-optimal solution selection based on a multi-criteria decision-analysis approach. A case study of multi-generation smartphone marketing was then conducted to validate the proposed framework and illustrate its applicability. Results indicate that the proposed framework can (i) bridge the complex nonfunctional relationships between impact factors and objectives for marketing prediction; and (ii) exploit the multivariate marketing performances within these relationships to make optimal prescriptions after the closed-loop interactive iterations. This framework enables managers to obtain Pareto-optimal solution prescriptions with improved solution stability surpassing those of traditional prediction.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120451