A new multi-objective discrete robust optimization algorithm for engineering design

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality.     

An efficient optimization method for mechanical shells considering cut-outs

In this contribution an optimization method for shell structures is presented. This method was developed in order to perform a simultaneous optimization of the shape and position of the mid surface and a topology optimization to introduce cut-outs. A topology optimization method for continuum structures is combined with a manufacturing constraint for deep drawable sheet metals. It is shown, how more than a million design variables can be handled efficiently using a mathematical optimization algorithm for the design update and the finite element method for the structural simulation. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

REVIEW OF DYNAMIC OPTIMIZATION METHODS IN RENEWABLE NATURAL RESOURCE MANAGEMENT

In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. Optimization methods are characterized by (1) the mathematical model used to describe a natural resource system, (2) a set of feasible strategies available to the resource manager, and (3) an objective functional by which to measure benefits and costs of strategies. A formal statement of the control problem is used to describe six approaches to optimal utilization of renewable natural resources: variational mathematics, specifically Pontryagin's Maximum Principle; dynamic programming; linear programming; nonlinear programming; simulation-optimization; and classical procedures. Solution methodologies are illustrated for each of these approaches, and examples from the ecological and natural resource literature are described for various subject matter areas. Applications are highlighted in terms of model structures, objective functionals, and system constraints. To the extent possible, optimal management patterns are characterized. Finally, the applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.     

Robust topology optimization for multi-material structures under interval uncertainty

In this paper, we propose an efficient method to design robust multi-material structures under interval loading uncertainty. The objective of this study is to minimize the structural compliance of linear elastic structures. First, the loading uncertainty can be decomposed into two unit forces in the horizontal and vertical directions based on the orthogonal decomposition, which separates the uncertainty into the calculation coefficients of structural compliance that are not related to the finite element analysis. In this manner, the time-consuming procedure, namely, the nested double-loop optimization, can be avoided. Second, the uncertainty problem can be transformed into an augmented deterministic problem by means of uniform sampling, which exploits the coefficients related to interval variables. Finally, an efficient sensitivity analysis method is explicitly developed. Thus, the robust topology optimization (RTO) problem considering interval uncertainty can be solved by combining orthogonal decomposition with uniform sampling (ODUS). In order to eliminate the influence of numerical units when comparing the optimal results to deterministic and RTO solutions, the relative uncertainty related to interval objective function is employed to characterize the structural robustness. Several multi-material structure optimization cases are provided to demonstrate the feasibility and efficiency of the proposed method, where the magnitude uncertainty, directional uncertainty, and combined uncertainty are investigated.     

A semismooth sequential quadratic programming method for lifted mathematical programs with vanishing constraints

Mathematical programs with vanishing constraints are a difficult class of optimization problems with important applications to optimal topology design problems of mechanical structures. Recently, they have attracted increasingly more attention of experts. The basic difficulty in the analysis and numerical solution of such problems is that their constraints are usually nonregular at the solution. In this paper, a new approach to the numerical solution of these problems is proposed. It is based on their reduction to the so-called lifted mathematical programs with conventional equality and inequality constraints. Special versions of the sequential quadratic programming method are proposed for solving lifted problems. Preliminary numerical results indicate the competitiveness of this approach.     

Constrained optimization using multiple objective programming

In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically designed to incorporate such complex preference structures, they gain more and more importance in application areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share important properties, and that many classical solution approaches have correspondences in the respective models. The analysis naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence results are proven for convex and nonconvex problems.     

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

In this study, we propose an improved fruit fly optimization algorithm (FOA) based on linear diminishing step and logistic chaos mapping (named DSLC-FOA) for solving benchmark function unconstrained optimization problems and constrained structural engineering design optimization problems. Based on comparisons with genetic algorithm, particle swarm optimization, FOA, LGMS -FOA, and chaotic FOA methods, we demonstrated that DSLC-FOA performed better at searching for the optimal solutions of four typical benchmark functions. The approximate optimal results were obtained using DSLC-FOA for three structural engineering design optimization problems as examples of applications. The numerical results demonstrated that the proposed DSLC-FOA algorithm is superior to the basic FOA and other metaheuristic or deterministic methods.     

Mathematical programs with vanishing constraints: optimality conditions and constraint qualifications

We consider a difficult class of optimization problems that we call a mathematical program with vanishing constraints. Problems of this kind arise in various applications including optimal topology design problems of mechanical structures. We show that some standard constraint qualifications like LICQ and MFCQ usually do not hold at a local minimum of our program, whereas the Abadie constraint qualification is sometimes satisfied. We also introduce a suitable modification of the standard Abadie constraint qualification as well as a corresponding optimality condition, and show that this modified constraint qualification holds under fairly mild assumptions. We also discuss the relation between our class of optimization problems with vanishing constraints and a mathematical program with equilibrium constraints.     

Efficiency of the Tresca yield criterion in modeling of annular plates with rigid constraint

Correct prediction of the load carrying capacity and initiation of failure of compound annular plates (with or without inner solid disk) embedded into a rigid container are crucial for achieving their optimal structural design and reliable service conditions. In this paper an analytical study devoted to mathematically and physically rigorous stress/strain analysis of such structures has been performed. The decohesive carrying capacity criterion based on the radial strains is applied and discussed. The material of the plate is considered to be elastic/perfectly-plastic, and the inner insert is modeled as purely elastic. Two principle engineering criteria for material yielding, namely, the Tresca and the Mises, are employed, discussed and compared. The range of validity of each criterion is analyzed, and the choice of the suitable criterion for particular engineering applications is suggested. The advantages of the Tresca yield criterion both in mathematical modeling and engineering design are outlined.     

Optimization of axisymmetric membrane shells

Problems of the joint optimization of the shape and distribution along the meridian of the thickness of membrane shells of revolution under the action of axisymmetric loads are considered, taking account of the constraints concerning the strength of the shell and the volume of its cavity. General formulations of problems of the optimal design of shells of revolution are given and the optimal shape of a shell and the corresponding thickness distribution are investigated. Results of the exact solution of problems of the optimal design of closed shells of revolution when there is an internal pressure are presented. The simultaneous introduction of two control functions, describing the shape of the shell and the distribution of its thickness, not only ensures a substantial reduction in the mass of a shell but also leads to significant mathematical simplifications, which enable the solution of the optimization problem being considered to be obtained in an analytical form.     

Time traps in supply chains: Is optimal still good enough?

Operations Researchers support Supply Chain Management and Supply Chain Planning by developing adequate mathematical optimization models and providing suitable solution procedures. In this paper we discuss what adequate could mean. Therefore, we may ask several questions concerning “optimality” in Supply Chain Planning under causal and temporal uncertainty: What is an optimal solution? When is it optimal? For how long is it optimal? How should the design of a supply chain be changed when conditions and requirements ask for new structures? In particular, we discuss new approaches to Supply Chain Planning in order to give an optimal transformation from an initial solution over multiple periods to a desired one rather than just specifying an optimal snapshot solution. Time and uncertainty are the factors triggering the whole discussion. In particular, several flaws often found when dealing with these factors result in so-called “time traps”. We look at the impact of recent technological developments like the Internet of Things or Industry 4.0 on operational supply chain planning and control, and we show how online optimization can help to cope with real-time challenges. Moreover, we re-coin the concept of risk in the realm of Supply Chain Planning. Here the question is how to measure supply chain specific risks and how to incorporate them “adequately” into mathematical models.     

Extrema of matrix functionals

The problem of determining conditional extrema of functionals with matrix arguments is considered. We derive the necessary and sufficient mathematical conditions for the existence of extrema of functionals satisfying constraints of the form of matrix equalities on the arguments. The construction of extrema is based on functions and matrices of indeterminate Lagrange multipliers. As applications we consider an example of determining the optimal strength coefficient matrix in a dynamical system with an adaptive Carleman filter and an example, from the theory of statistical decisions, of minimizing the volume of the dispersion error ellipsoid. Our approach has wide applications not only in optimization problems from automatic control theory but also in mathematical statistics and the theory of material strength and plasticity.Translated from Dinamicheskie Sistemy, No. 5, pp. 103–106, 1986.     

Diseño óptimo robusto utilizando modelos Kriging: aplicación al diseño óptimo robusto de estructuras articuladas

Conventional methods addressing the robust design optimization problem of structures usually require high computational requirements due to the nesting of uncertainty quantification within the optimization process. In order to address such a problem, this work proposes a methodology, based on Kriging models, to efficiently assess the uncertainty quantification in the optimization process. The Kriging model approximates the structural performance both in the design domain and in the stochastic domain, which allows to decouple the uncertainty quantification process and the optimization process. In addition, an infill criterion based on the variance of the Kriging prediction is included to update the Kriging model towards the global Pareto front. Three numerical examples show the applicability and the accuracy of the proposed methodology. The results show that the proposed method is appropriate to solve the robust design optimization problem with reasonable accuracy and a considerably lower number of function calls than required by conventional methods.     

Multicriterion reliability-oriented optimization of structural systems by stochastic programming

Papers deals with multicriterion reliability-oriented optimization of truss structures by stochastic programming. Deterministic approach to structural optimization appears to be insufficient when loads acting upon a structure and material properties of the structure elements have a random nature. The aim of this paper is to show importance of random modelling of the structure and influence of random parameters on an optimal solution. Usually, quality of engineering structure design is considered in terms of displacements, total cost and reliability. Therefore, optimization problem has been formulated and then solved in order to show interaction between displacement and a total cost objective function. The examples of 4-bar and 25-bar truss structures illustrate our considerations. The results of optimization are presented in the form of diagrams.     

Formulation and Execution of Structural Topology Optimization for Practical Design Solutions

Topology optimization has gained prime significance due to increasing demands of lightweight components. In this paper, a general mathematical formulation of topology optimization is presented with some imperative manufacturing constraints for maximizing the stiffness of a structure with mixed boundary conditions. A methodology is implemented to determine the optimal configuration of operative structural components by executing TOSCA in batch-process mode with ANSYS software. CAD viable design is attained by smoothing the topological optimized surfaces. The geometry at the maximum stressed areas is also optimized. Analysis of the customized reduced weight configuration reveals that it comprises the harmonized stress distribution and improved structural performance.     

Stochastic Material and Topology Design

In order to find a robust optimal topology or material design with respect to stochastic variations of the model parameters of a mechanical structure, the basic optimization problem under stochastic uncertainty must be replaced by an appropriate deterministic substitute problem. Starting from the equilibrium equation and the yield/strength conditions, the problem can be formulated as a stochastic (linear) program “with recourse”. Hence, by discretization the design space by finite elements, linearizing the yield conditions, in case of discrete probability distributions the resulting deterministic substitute problems are linear programs with a dual decomposition data structure.     

Optimal plastic design for partially pressigned strength distribution

The optimal plastic design of structures having a partially predefined strength distribution is considered. Sufficient conditions for optimality as well as upper and lower bounds on minimum structural volume are established and examples involving a continuous beam and a grillage are given. It is shown that most existing theories for optimal plastic design and limit analysis can be derived from the optimality criteria presented.     

Boundary optimal control of MHD flows

This paper deals with some optimal control problems associated with the equations of steady-state, incompressible magnetohydrodynamics. These problems have direct applications to nuclear reactor technology, magnetic propulsion devices, and design of electromagnetic pumps. These problems are first put into an appropriate mathematical formulation. Then the existence of optimal solutions is proved. The use of Lagrange multiplier techniques is justified and an optimality system of equations is derived. The theory is applied to an example.The work of L. S. Hou was supported in part by the Natural Science and Engineering Research Council of Canada under Grant Number OGP-0137436 and by a Simon Fraser University President's Research grant.     

无线通信中的最优资源分配―复杂性分析与算法设计

最优资源分配问题是无线通信系统设计中的基本问题之一.最优地分配功率、传输波形和频谱等资源能够极大地提高整个通信系统的传输性能.目前,相对于通信技术在现实生活中的蓬勃发展,通信系统优化的数学理论和方法显得相对滞后,在某些方面已经成为影响其发展和应用的关键因素.无线通信中的最优资源分配问题常常可建模为带有特殊结构的非凸非线性约束优化问题.一方面,这些优化问题常常具有高度的非线性性,一般情况下难于求解;另一方面,它们又有自身的特殊结构,如隐含的凸性和可分结构等.本文着重考虑多用户干扰信道中物理层资源最优分配问题的复杂性刻画,以及如何利用问题的特殊结构设计有效且满足分布式应用等实际要求的计算方法.