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    Area of Science:

    • Operations Research
    • Computer Science
    • Artificial Intelligence

    Background:

    • Online-to-offline (O2O) restaurant services face complex set meal design (SMD) challenges.
    • Optimizing SMD requires balancing customer preferences, operational constraints, and profit.

    Purpose of the Study:

    • To develop a comprehensive mathematical formulation for the O2O-SMD problem.
    • To propose an efficient algorithm for solving the O2O-SMD problem.

    Main Methods:

    • A unified mathematical formulation integrating dish variety, pricing, nutrition, and profitability.
    • A tensor-based ant colony optimization (TACO) algorithm with parallel processing capabilities.
    • Integration of a local search strategy to enhance solution refinement and convergence speed.

    Main Results:

    • TACO demonstrated superior performance compared to existing algorithms.
    • The algorithm achieved significant improvements in solution quality, scalability, and computational efficiency.
    • Evaluation on real-world data confirmed the algorithm's effectiveness.

    Conclusions:

    • The proposed TACO algorithm offers an effective and practical solution for O2O-SMD problems.
    • TACO's tensor-based approach enables efficient parallel optimization.
    • The method provides a valuable tool for restaurants optimizing their set meal offerings.