決定最佳設施規劃對於提高生產力具有非常大的影響。然而，設施規劃問題（FLP）基於其目標和配置已發展得更為複雜。不等面積的設施規劃問題（UA-FLP），由於其多樣的場地配置、不固定的走道距離和計算複雜性而具有挑戰性。除了 UA-FLP 模型的複雜性之外，現實世界中的 FLP 還利用多目標模型來表示材料移動方面以外的設施規劃。因此，本研究提出了一種萬用演算法，通過考慮三個目標函數（流量/距離測量、鄰接等級和危害移動）來解決多目標 UA-FLP。

本研究所提出的演算法，其使用矩陣切片機制，利用數組元素表示離散之佈局網格。每個部門被表示為較小的子陣列，以放置在較大的陣列中作為其廠區。這種離散方法採用由部門放置序列組成的染色體，該序列將用於多目標萬用演算法。提出的進化演算法是利用非支配排序遺傳演算法-II（NSGA-II），最優解包含一組對每個目標函數沒有任何優先的候選解。兩種演算法都增加了重複刪除機制，以保持解的多樣性並避免過早收斂。

根據計算結果，所提出的演算法能夠解決小、中及大型的多目標 UA-FLP。 NSGA-II 較 MOPSO 提供了更好的解決方法，特別是在兩點交配機制下，其適應值優於其他演算法。通過支配其他演算法的解決方法，具有兩點交配的 NSGA-II 也產生了更好的性能評估。

Determining an optimal facility placement in a production layout plays a crucial role in terms of improving productivity. However, the scope of facility layout problem (FLP) have developed a complex configuration based on its objective and placement. Facility layout problem with unequal area (UA-FLP) poses its own challenge due to its diverse department dimension, uncertain bay and computational complexity. In addition to the complex nature of UA-FLP model, real-world FLP encourages many-objective model to represent facility layout purposes other than material movement aspect. Therefore, this study proposes a metaheuristic algorithm to solve many-objective UA-FLP by considering three objective functions (flow/distance measurement, adjacency rating, and hazard movement).

The proposed algorithm uses a matrix slicing mechanism, by utilizing array elements as a discrete representation of layout grid. Each department is represented as smaller sub-array to be placed at larger array grid as its production floor. This discrete approach employs a chromosome that consist of department placement sequence that will be used in the multi-objective metaheuristics. The proposed evolutionary algorithms are non-dominated sorting genetic algorithm-II (NSGA-II) with the optimal solution contains a set of solution candidates that does not have any priorities on each objective function. Duplicate removal mechanism is added in the algorithm in order to preserve solution diversity and prevent early convergence.

Based on the computational result, the proposed algorithm is able to solve multi- objective UA-FLP on small, medium, and large data instances. The NSGA-II algorithm provide a better solution compared to MOPSO, especially with two-point crossover mechanism because the fitness value dominates the other algorithm. By dominating the other algorithm’s solution, the NSGA-II with two-point crossover
also yields a better performance evaluation

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