A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking problem, augmented system defined by derived dynamics and reference trajectory so that actor neural network can learn feedforward feedback terms at same time. During online self-learning process, learns pol...

In this paper, an integrated machine scheduling withits due date setting problem has been considered. It is assumed that the machine is subject to some kind of random unavailability. Due dates should be set in an attractive and reliable manner, implying that they should be short and possible to be met. To this end, first, long due dates are penalized in the objective function. Then, for each cu...

The definitions of optimal preventive and corrective maintenance of electric power distribution networks can be seen as a special case of a knapsack problem. This paper proposes a dynamic programming approach to deal with this problem. The approach is developed for one or more years of planning horizon. Case studies compare the optimal dynamic programming approach with an heuristic method.

In this paper, with the aim of estimating internal dynamics matrix of a gimbaled Inertial Navigation system (as a discrete Linear system), the discretetime Hamilton-Jacobi-Bellman (HJB) equation for optimal control has been extracted. Heuristic Dynamic Programming algorithm (HDP) for solving equation has been presented and then a neural network approximation for cost function and control input ...

Dynamic pricing for network revenue management has received considerable attention in research and practice. Based on data obtained from a major hotel, we use a large-scale numerical study to compare the performance of several heuristic approaches proposed in the literature. The heuristic approaches we consider include deterministic linear programming with resolving and three variants of dynami...

We describe a heuristic search algorithm for Markov decision problems, called LAO*, that is derived from the classic heuristic search algorithm AO*. LAO* shares the advantage heuristic search has over dynamic programming for simpler classes of problems: it can find optimal solutions without evaluating all problem states. The derivation of LAO* from AO* makes it easier to generalize refinements ...

Traditional optimization techniques such as Dynamic Programming(DP), Linear Programming(LP), and MixedInteger Programming(MIP) have been used to solve complex optimization problems in many diverse fields. Shortcomings of these classical techniques and the rapid development of affordable, powerful computing technology have led to the creation of new classes of heuristic-based optimizing techniqu...

In open-pit mine planning, the design of the most profitable ultimate pit limit is a prerequisite to developing a feasible mining sequence. Currently, the design of an ultimate pit is achieved through a computer program in most mining companies. The extraction of minerals in open mining methods needs a lot of capital investment, which may take several decades. Before the extraction, the p...

We present an Approximate Dynamic Programming (ADP) approach for the multidimensional knapsack problem (MKP). We approximate the value function (a) using parametric and nonparametric methods and (b) using a base-heuristic. We propose a new heuristic which adaptively rounds the solution of the linear programming relaxation. Our computational study suggests: (a) the new heuristic produces high qu...