A new method of optimization on linear parabolic solar collectors using exergy analysis is presented. A comprehensive mathematical modeling of thermal and optical performance is simulated and geometrical and thermodynamic parameters were assumed as optimization variables. By applying a derived expression for exergy efficiency, exergy losses were generated and the optimum design and operating conditions were investigated. The objective function (exergy efficiency) along with constraint equations constitutes a four-degree freedom optimization problem. Using Lagrange multipliers method, the optimization procedure was applied to a typical collector and the optimum design point was extracted. The optimum values of collector inlet temperature, oil mass flow rate, concentration ratio and glass envelope diameter are calculated simultaneously by numerical solution of a highly non-linear equations system. To study the effect of changes in optimization variables on the collected exergy, the sensitivity of optimization to changes in collector parameters and operating conditions is evaluated and variation of exergy fractions at this point are studied.

Locally linear Model Tree (LOLIMOT) algorithm proposed by Nelles deals with local linear nearo-fuzzy models that is based on divides-and-conquer strategy that a complex modeling problem is divided to a number of smaller and thus simpler sub problems.So the characteristic of such a neuro-fuzzy model depends on division strategy for the original complex problem.For finding the best output the algorithm divides the problem to a number of local linear models (LLMs), then continues with finding the worst LLM and dividing it. LOLIMOT splits the local linear models into two equal halves with an axis-orthogonal decomposition strategy.In this paper a new approach based on extremeal optimization (EO) is used to optimize the structure of LOLIMOT.Simulation results show the effectiveness of the enhanced LOLIMOT to have a higher precision with optimal number of neurons.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

As an important duality result in linear optimization, the Goldman–Tucker theorem establishes strict complementarity between a pair of primal and dual linear programs. Our study extends this result into the framework of linear fractional optimization. Associated with a linear fractional program, a dual program can be defined as the dual of the equivalent linear program obtained from applying the Charnes–Cooper transformation to the given program. Based on this definition, we propose new criteria for primal and  dual optimality by showing that the primal and dual optimal sets can be equivalently modeled as the optimal sets of a pair of primal and dual linear programs. Then, we define the concept of strict complementarity and establish the existence of at least one, called strict complementary, pair of primal and dual optimal solutions such that in every pair of comple mentary variables, exactly one variable is positive and the other is zero. We geometrically interpret the strict complementarity in terms of the relative interiors of two sets that represent the primal and dual optimal setsin higher dimensions. Finally, using this interpretation, we develop two approaches for finding a strict complementary solution in linear fractional optimization. We illustrate our results with two numerical examples.

In this paper, the formulation and creation of an enterprise-wide global production planning system for the semiconductor industry are described, using linear optimization as the core planning technology. The objective is to generate production plans on a weekly or daily basis which are optimized to maximize customer service and cash flow in response to a prioritized statement of demand. When evaluating production alternatives, the system observes defined product and process structure relationships, as well as capacity and materials limitations within the various stages of manufacturing.

The problem of finding the minimum cost multi-commodity flow in an undirected and complete network is studied when the link costs are piecewise linear and convex. The arc-path model and overflow model are presented to formulate the problem. The results suggest that the new overflow model outperforms the classical arc-path model for this problem. The classical revised simplex, Frank and Wolf and a heuristic method are compared for the problem.

Dombi family of t-norms includes a parametric family of continuous strict t-norms, whose members are increasing functions of the parameter. This family of t-norms covers the whole spectrum of t-norms when the parameter is changed from zero to infinity. In this paper, we study a nonlinear optimization problem in which the constraints are defined as fuzzy relational equations (FRE) with the Dombi family of t-norms. We firstly investigate the resolution of the feasible solutions set when it is defined with max-Dombi composition and present some necessary and su cient conditions for determining the feasibility. Also, some procedures are presented for simplifying the problem. Since the feasible solutions set of FREs is non-convex, conventional nonlinear programming methods may not be directly employed to solve the problem. Based on some theoretical properties of the problem, a genetic algorithm is