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<p>Order-of-addition experiments aim to study how the sequence in which multiple components are added influences the final response of a process or product. Such experiments are particularly relevant in industrial and scientific applications where the order of mixing or combining ingredients affects the outcome. Designing these experiments efficiently is crucial for accurately identifying the optimal or suitable order of addition. Two major modelling frameworks, namely the pairwise-ordering model and the component-position model, have been proposed in the literature for analyzing such experiments. However, available methods for constructing corresponding designs remain limited. This paper develops systematic algorithms for obtaining designs under both modelling approaches and provides catalogues of designs for use by experimenters. All algorithms have been implemented in a dedicated R package, thereby offering a comprehensive computational resource to facilitate the planning and analysis of order-of-addition experiments.</p>

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On Construction of Efficient Designs for Order-of-Addition Experiments

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Sukanta Dash,
  • Baidya Nath Mandal,
  • Rajender Parsad,
  • Anil Kumar

摘要

Order-of-addition experiments aim to study how the sequence in which multiple components are added influences the final response of a process or product. Such experiments are particularly relevant in industrial and scientific applications where the order of mixing or combining ingredients affects the outcome. Designing these experiments efficiently is crucial for accurately identifying the optimal or suitable order of addition. Two major modelling frameworks, namely the pairwise-ordering model and the component-position model, have been proposed in the literature for analyzing such experiments. However, available methods for constructing corresponding designs remain limited. This paper develops systematic algorithms for obtaining designs under both modelling approaches and provides catalogues of designs for use by experimenters. All algorithms have been implemented in a dedicated R package, thereby offering a comprehensive computational resource to facilitate the planning and analysis of order-of-addition experiments.