Solving warehouse location problem by Lagrange programming neural network

The LPPH-CSP is a neural network for solving the constraint satisfaction problem (CSP), which is a combinatorial problem to find a solution which satisfies all given constraints. The trajectory of the LPPH-CSP is not trapped by any point which is not the solution of the CSP. Though the already proposed other methods for solving the CSP must update all variables sequentially, the LPPH-CSP can update all variables simultaneously. We think this is an advantage of the LPPH-CSP for VLSI implementation. In this paper, we add new types of constrains to the CSP, and extend the LPPH-CSP for these types of constraints. We apply this new LPPH-CSP for solving the warehouse location problem (WLP), which is a kind of the CSP with an objective function.