Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. Most of the scheduling problems are NP-Hard. Therefore, heuristics and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions and when computational complexity can be controlled. For this purpose, we have developed a method based on Hopfield Neural Network model. This approach permits to solve in an iterative way a scheduling problem, finding a solution through the minimization of an energy function. An interesting property of this approach is its capacity to trade-off the quality for computation time. Indeed, the convergence speed of the minimization process can be tuned by adapting several parameters that influence the quality of the results. By tuning these parameters, we can built a library of a set of run-time executions (contracts) of the Hopfield minimization process with different characteristics (quality, efficiency). We present in this paper two applications exploiting the advantage of having available anytime contract algorithms. The first application illustrates how the availability of a library of contract algorithms make it possible to build a solution to one machine scheduling problems within a delay that follows a stochastic distribution. The second application deals with unrelated parallel machines scheduling of non preemptive tasks.