An optimization-based approach to fault diagnosis for nonlinear stochastic dynamic models is developed. An optimal diagnosis problem is formulated according to a receding-horizon strategy. This approach leads to a functional optimization problem (also called ‘infinite optimization problem’), whose admissible solutions belong to a function space. As in such a context, the tools from mathematical...

In this paper, we propose a new model for designing integrated forward/reverse logistics based on pricing policy in direct and indirect sales channel. The proposed model includes producers, disposal center, distributers and final customers. We assumed that the location of final customers is fixed. First, a deterministic mixed integer linear programming model is developed for integrated logistic...

in this study, we considered an inflationary inventory control model under non-deterministic conditions. we assumed the inflation rate as a normal distribution, with any arbitrary probability density function (pdf). the objective function was to minimize the total discount cost of the inventory system. we used two methods to solve this problem. one was the classic numerical approach which turne...

We introduce and analyze stochastic optimization methods where the input to each gradient updateis perturbed by bounded noise. We show that this framework forms the basis of a unified approachto analyze asynchronous implementations of stochastic optimization algorithms. In this framework,asynchronous stochastic optimization algorithms can be thought of as serial methods operatin...

this work presents a hybrid method for motif discovery in dna sequences. the proposed method called spso-lk, borrows the concept of chebyshev polynomials and uses the stochastic local search to improve the performance of the basic pso algorithm as a motif finder. the chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed b...

Recent advances in coupling novel optimization methods to large-scale computing problems have opened the door to tackling a diverse set of physically realistic engineering design problems. A large computational overhead is associated with computing the cost function for most practical problems involving complex physical phenomena. Such problems are also plagued with uncertainties in a diverse s...

The traditional numerical analysis considers optimization algorithms which guarantee some accuracy for all functions to be optimized. This includes the exact algorithms. Limiting the maximal error requires a computational effort that in many cases increases exponentially with the size of the problem (Horst and Pardalos, 1995, Handbook of Global Optimization, Kluwer). That limits practical appli...

Quasi-Newton methods for numerical optimization exploit quadratic Taylor polynomial models of the objective function. Trust regions are widely used to ensure the global convergence of these methods. Analogously, response surface methods for stochastic optimization exploit linear and quadratic regression models of the objective function. Ridge analysis is widely used to safeguard the optimizatio...

Departing from the conventional cache hit optimization in cache-enabled wireless networks, we consider an alternative optimization approach for the probabilistic caching placement in stochastic wireless D2D caching networks taking into account the reliability of D2D transmissions. Using tools from stochastic geometry, we provide a closed-form approximation of cache-aided throughput, which measu...