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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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    This study introduces a novel bi-level grouping optimization (BIGO) model to solve the complex storage location assignment problem with grouping constraints. The BIGO model effectively optimizes item grouping and location assignment, outperforming random search methods.

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    Area of Science:

    • Operations Research
    • Supply Chain Management
    • Optimization

    Background:

    • The storage location assignment problem with grouping constraint (SLAP-GC) presents significant challenges due to complex grouping restrictions.
    • Simultaneously solving the item grouping and group-to-location assignment subproblems is computationally demanding.

    Purpose of the Study:

    • To propose a novel bi-level grouping optimization (BIGO) model to address the SLAP-GC.
    • To develop an effective optimization framework that handles the inherent complexities of grouping constraints in storage logistics.

    Main Methods:

    • Developed a bi-level optimization model (BIGO) separating item grouping (upper level) and group assignment (lower level).
    • Designed sophisticated fitness evaluation and search operators for both optimization levels to ensure solution feasibility and efficient search.
    • Implemented a multistart random search and a tabu search algorithm based on the BIGO model.

    Main Results:

    • The BIGO model demonstrates efficacy in solving the SLAP-GC.
    • Experimental results on a real-world dataset validate the model's performance.
    • The tabu search algorithm shows a significant advantage over the random search method.

    Conclusions:

    • The proposed BIGO model provides an effective approach for the storage location assignment problem with grouping constraints.
    • The developed optimization strategies enhance search efficiency and solution quality.
    • Tabu search is a superior method for solving this problem compared to random search.