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<p>Considering its significant role in the first contact customers have with the product, designing a distinctive product packaging that effectively conveys the intended meaning of a product is crucial, but also challenging and complex for companies. Hence, the research aims to identify and rank critical front-of-package cues for packaging design, and to select the best packaging design, in the form of a gift box, among artificial intelligence-generated alternatives. A reinforcement learning-based hybrid decision-making method is developed and adopted to facilitate ranking and selection processes. The proposed methodology, with a spatial and geometric approach, uniquely applies the concepts of molecular geometry to define degrees of membership, non-membership, and hesitancy within the fuzzy set framework, offering a new perspective in fuzzy decision-making. The methodology’s foremost superiority is combining the fuzzy decision-making approach with molecular geometry knowledge. Thus, the methodology’s use of molecular fuzzy numbers significantly contributes to state-of-the-art engineering. To evaluate the applicability and robustness of the methodological framework, a gift box design for the sustainable products of a textile company is created and implemented as a real case study. The results reveal that brand identity and aesthetic appeal are the most prioritized design criteria in the package design process. Based on the overall evaluation, the third alternative is the best artificial intelligence-generated gift box design for the respective company. This research highlights the key aspects of packaging elements necessary for effective packaging design and the application of artificial intelligence in the gift box design process, providing practical implications for professionals responsible for packaging design. Additionally, this research contributes to the weighting and ranking problems of artificial intelligence-generated gift box design assessment by using molecular fuzzy sets and the reinforcement learning technique, thereby providing a more accurate approach.</p>

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Artificial intelligence-generated sustainable gift box design evaluation via reinforcement learning-driven hybrid molecular fuzzy modelling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Hasan Dinçer,
  • Serhat Yüksel,
  • İlkin Yaran Ögel,
  • Fatih Ecer

摘要

Considering its significant role in the first contact customers have with the product, designing a distinctive product packaging that effectively conveys the intended meaning of a product is crucial, but also challenging and complex for companies. Hence, the research aims to identify and rank critical front-of-package cues for packaging design, and to select the best packaging design, in the form of a gift box, among artificial intelligence-generated alternatives. A reinforcement learning-based hybrid decision-making method is developed and adopted to facilitate ranking and selection processes. The proposed methodology, with a spatial and geometric approach, uniquely applies the concepts of molecular geometry to define degrees of membership, non-membership, and hesitancy within the fuzzy set framework, offering a new perspective in fuzzy decision-making. The methodology’s foremost superiority is combining the fuzzy decision-making approach with molecular geometry knowledge. Thus, the methodology’s use of molecular fuzzy numbers significantly contributes to state-of-the-art engineering. To evaluate the applicability and robustness of the methodological framework, a gift box design for the sustainable products of a textile company is created and implemented as a real case study. The results reveal that brand identity and aesthetic appeal are the most prioritized design criteria in the package design process. Based on the overall evaluation, the third alternative is the best artificial intelligence-generated gift box design for the respective company. This research highlights the key aspects of packaging elements necessary for effective packaging design and the application of artificial intelligence in the gift box design process, providing practical implications for professionals responsible for packaging design. Additionally, this research contributes to the weighting and ranking problems of artificial intelligence-generated gift box design assessment by using molecular fuzzy sets and the reinforcement learning technique, thereby providing a more accurate approach.