Sharif Digital Repository

The taping mode Atomic Force Microscopic (T-AFM) can be properly described by a sinusoidal excitation of its base and nonlinear potential interaction with sample. Thus the cantilever may cause chaotic behavior which decreases the performance of the sample topography. In this paper a nonlinear delayed feedback control.is proposed to control.chaos in a single mode approximation of a T-AFM system. Assuming model parameters uncertainties, the first order Unstable Periodic Orbits (UPOs) of the system is stabilized using the sliding nonlinear delayed feedback control. The effectiveness of the presented methods is numerically verified and the results show the high performance of the control.er  

In this paper, an optimal control framework is formed to control rotor-Active Magnetic Bearing (AMB) systems. The multi-input-multi-output non-affine model of AMBs is well established in the literature and represents a challenging problem for control design, where the design requirement is to keep the rotor at the bearing centre in the presence of external disturbances. To satisfy the constraints on the states and the control inputs of the AMB nonlinear dynamics, a nonlinear optimal controller is formed to minimize tracking error between the current and desired position of the rotor. To solve the resulted nonlinear constrained optimal control problem, the Gauss Pseudospectral Collocation... 

Enhancing the quality of the product has always been one considerable concern of production process management, and this subject gave way to implementing so many methods including robust design. In this paper, robust design utilizes response surface methodology (RSM) considering the mean and variance of the response variable regarding system design, parameter design, and tolerance design. In this paper, customer requirements and robust design are regarded simultaneously to achieve enriched quality. Subsequently, with a non-linear programming, a novel method for integrating RSM and quality function deployment has been proposed to achieve robustness in design. The customer requirements are... 

This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition... 

Lot streaming means breaking a lot into sublots, where sublots may be transferred to a number of machines for the operations. Here, the multi-job lot streaming problem in a multistage hybrid flow shop having identical parallel machines at stages with work-in-process (WIP) jobs, work shifts constraint, and sequence-dependent setup times is studied. The aim is to minimize the sum of weighted completion times of jobs in each shift in order to furnish a better machine utilization for the following shifts. Our model in meeting the job demands appropriates job scheduling onmachines for processing, the sequence of operations on allocated machines, the size of the sublots in the work shifts, the... 

In this research, a manufacturing facility with independent workstations to remanufacture nonconforming products is investigated. Each workstation is first modeled as an M/M/m queuing system with m being a decision variable. Then, a tri-objective integer nonlinear programming models is developed to formulate the problem. The first objective tries to minimize the waiting times of products, while the second one tries to maximize the minimum reliability of machines at the workstations. Since minimization of the waiting times results in using a large number of machines with higher idle times, the third objective is considered to minimize the mean idle time of the machines. The aim is to... 

In this paper, the problem of optimal tracking control for a container ship is addressed. The multi-input-multi-output nonlinear model of the S175 container ship is well established in the literature and represents a challenging problem for control design, where the design requirement is to follow a commanded maneuver at a desired speed. To satisfy the constraints on the states and the control inputs of the vessel nonlinear dynamics and minimize the heading error, a nonlinear optimal controller is formed. To solve the resulted nonlinear constrained optimal control problem, the Gauss Pseudospectral Method (GPM) is used to transcribe the optimal control problem into a Nonlinear Programming... 

We propose a robust optimization framework to deal with uncertain component reliabilities in redundancy allocation problems in series-parallel systems. The proposed models are based on linearized versions of standard mixed integer nonlinear programming (MINLP) formulations of these problems. We extend the linearized models to address uncertainty by assuming that the component reliabilities belong to a budgeted uncertainty set, and develop robust counterpart models. A key challenge is that, because the models involve nonlinear functions of the uncertain data, classical robust optimization approaches cannot apply directly to construct their robust optimization counterparts. We exploit problem... 

In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of... 

The vendor-managed inventory (VMI) is a common policy in supply chain management (SCM) to reduce bullwhip effects. Although different applications of VMI have been proposed in the literature, the multi-vendor multi-retailer single-warehouse (MV-MR-SW) case has not been investigated yet. This paper develops a constrained MV-MR-SW supply chain, in which both the space and the annual number of orders of the central warehouse are limited. The goal is to find the order quantities along with the number of shipments received by retailers and vendors such that the total inventory cost of the chain is minimized. Since the problem is formulated into an integer nonlinear programming model, the... 

This article proposes a multi-objective goal programming based approach with two objectives of maximizing the reliability and minimizing the placement cost of Phasor Measurement Units (PMUs) for full observability in power systems. The weighted sum goal programming formulation incorporates the reliability of individual PMUs and finds a placement to resolve the conflicting objectives of minimum number of PMUs cost-wise and maximum level of system-wide reliability. This multi-objective problem is formulated as a nonlinear goal programming model in which weights are associated with the objectives. The model is solved for several weight scenarios for the IEEE 14, 30, 57 and 118 bus test systems.... 

This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed...