This study exploits Hopfield neural networks (HNN) with branch and bound (B&B) tree and tabu strategy for solving bilevel linear programming and bilevel linear decentralized programming (BLP) problems, which are the special cases of multilevel linear programming (MLP) problems. MLP is a useful model to manage a decentralized planning process of hierarchical organizations in the real world. The model includes both levels having conflicting goals and separated controlled decisions which are difficult to obtain an optimal solution. The newly developed HNN approaches are efficient tools to manipulate the optimization model with parallel processing, and are especially suitable for large size problems. Therefore, two types of hybrid HNN approaches, with a B&B tree or Tabu strategy, are developed to attack BLP problems. In addition, some discussions on parameter settings, transfer functions, learning rates, and etc. are also discussed for ease of using HNN approaches. Finally, some typical examples are demonstrated and an application of supply chain planning is also investigated.

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