In production systems, process planning and scheduling are the two core functions that must be implemented before starting a production. The process planning generates a suitable process plan for each part that indicates the sequences of machining operations of the part, and the suitable machines and tools for that operations. This plan depends on the available manufacturing resources and their operation time. The scheduling, then, specifies the timetable of each operation from the process plans’ information. A significant conflict during the scheduling process is that some determined resources are not valid or overloaded because the process planning intentionally specifies the machines that make the least manufacturing cost. Therefore, it is time-consuming to re-schedule or search for a feasible solution that agree to the constraints of both process planning and scheduling. Integrated process planning and scheduling is a trend of modern manufacturing systems that would improve production in more flexible ways and overcome the disadvantages of conventional procedures of process planning and scheduling. It is mostly solved the integrated approach by using various metaheuristics algorithms. Hopfield networks, in literature, show effective results in scheduling and can be implemented in hardware level that has the computation powers and speed over the digital computations. Otherwise, the analog computation has no iteration and obtain a real-time solution. However, the Hopfield network is a local minimization method with slow convergence by its gradient descent algorithm. Some modifications of Hopfield network should lead to a fast convergence and global optimization problem. This thesis presents two original and novel Hopfield neural networks architectures. One of the proposed Hopfield neural networks modifies the standard Hopfield Network by ii properly incorporating the Broyden-Fletcher-Goldfarb-Shanno (BFGS) unconstrained optimization algorithm. The second proposed Hopfield network is based on properly incorporating the Nazareth unconstrained optimization algorithm in the standard Hopfield network. One important issue to be investigated is the convergence speed of these two novel Artificial Networks with respect to the convergence of the standard Artificial Hopfield Network. The proposed model uses analog integrators to follow the traditional algorithms of BFGS and Nazareth algorithms. The algorithms can be modeled by using simple analog circuits, such as integrators, multipliers, and adders. The proposed algorithms show a significant increase in the convergence speed of modified Hopfield networks compared to the standard one. Also noise and adaptive learning parameter technique are integrated to the modified Hopfield networks for global optimization. To test and compare the effectiveness of the proposed Networks, they are implemented on the solution of an integrated process planning and scheduling (IPPS) problem, which is a nondeterministic polynomial time problem (NP-hard). Moreover, here, it is treated under a novel formulation and to represent a modern manufacturing approach.