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    This study introduces the lunar-inspired artificial bee colony (LuABC) algorithm to solve the single machine total weighted tardiness problem (SMTWTP). The LuABC algorithm efficiently finds optimal solutions for large-scale SMTWTP instances.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Artificial Intelligence

    Background:

    • The single machine total weighted tardiness problem (SMTWTP) is a fundamental combinatorial optimization challenge.
    • Existing swarm intelligence (SI) techniques struggle with large-scale SMTWTP instances, posing a significant computational challenge.
    • The Artificial Bee Colony (ABC) algorithm is an effective SI-based method for real-world optimization.

    Purpose of the Study:

    • To develop an enhanced swarm intelligence strategy for solving the SMTWTP.
    • To improve the exploitation capabilities of the ABC algorithm for SMTWTP.
    • To present an effective amended ABC-based strategy for efficient SMTWTP resolution.

    Main Methods:

    • A novel local search (LS) approach, termed the lunar-inspired LS (LLS), was developed.
    • The LLS approach was hybridized with the ABC algorithm, creating the lunar-inspired ABC (LuABC) algorithm.
    • The LuABC algorithm was applied to 25 large-scale SMTWTP instances with 1000 jobs each.

    Main Results:

    • The proposed LuABC algorithm demonstrated superior performance in solving SMTWTP.
    • Optimal solutions were achieved for all 25 large-scale SMTWTP instances.
    • The algorithm successfully solved these complex instances within a reasonable timeframe.

    Conclusions:

    • The LuABC algorithm offers an effective and efficient solution for the single machine total weighted tardiness problem.
    • Hybridizing ABC with the LLS approach significantly enhances its exploitation capacity.
    • The developed LuABC algorithm provides a viable method for tackling large-scale SMTWTP instances.