Optimal Design Of Complex Systems Research Articles

Digital Transformation Program of Production and Logistics Systems: Methodological Aspects of Creating

The paper is devoted to the problem of managing the processes of digital transformation of production and logistics systems. The objective of the article is to describe the methodology for creating a comprehensive program for digital transformation of systems. In contrast to existing studies, the article presents solutions to methodological aspects of creating a transformation program based on optimal design of complex systems. The order of program development and the logical sequence of decisions are determined. The necessity of applying system and continuous engineering as well as Agile design methodology in the formation of programs and projects of digital transformation of the production and logistics system is revealed. The main results of the study were methodological approaches to creating a digital transformation program. The paper describes the procedure for developing a comprehensive digital transformation program. The stages of development and the content of work on the analysis, strategic and calendar planning of activities of the integrated program of digital transformation of the production and logistics system are shown. The scope of the proposed solutions: digital transformation management, production development management, design of production and logistics systems.

Multidisciplinary Design of Electric Vehicles Based on Hierarchical Multi-Objective Optimization

A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach.

Multi-objective reliability redundancy allocation problem considering two types of common cause failures

Common cause failures actuate simultaneous failure in the components of system and can incredibly reduce the reliability of the system. So the evaluation of common cause failure rates and its effect on system reliability is very much significant. When common cause failures are considered, failure rate of the components are not independent. The influence of common cause failure (CCF) to system reliability has motivated to consider common cause failures in reliability redundancy allocation problem. So, a novel multi-objective reliability redundancy allocation problem is formulated here with the objectives of maximizing system reliability and minimizing system cost in the presence of common cause failures. In this article, two types of common causes are highlighted, namely fatal and non-fatal CCFs, in addition to random failures. These types of failures lead to simultaneous failure of multiple components because of a common cause. An attempt has been made for the first time to solve the multi-objective optimization problem using two meta-heuristic algorithms which gives the Pareto optimal solution. Since the rapid advancements in Science and Technology encouraged the researchers for the optimal design of complex systems considering different environmental situations, two numerical evidences based on complex systems has been considered here. The results indicate the reduction of optimal redundancy with two types of CCF when compared to random failure alone.

Multi-objective design optimization of functionally graded material for the femoral component of a total knee replacement

The optimal design of complex systems in engineering requires pursuing rigorous mathematical modeling of the system’s behavior as a function of a set of design variables to achieve goal-oriented design. Despite the success of current knee implants, the limited life span remains the main concern of this complex system. The mismatch between the properties of engineered biomaterials and those of biological materials leads to insufficient bonding with bone, stress shielding effects and wear problems (i.e. aseptic loosening). The use of a functionally graded material (FGM) for the femoral component of knee implants is attractive because the properties can be designed to vary in a certain pattern to meet the desired requirements at different regions in the knee joint system, thereby decreasing loosening problem. However, matching the properties does not necessarily guarantee the best functionality of the knee implant and there is a need for developing the optimal design of an FGM femoral component that is longer lasting. In this study, therefore, a multi-objective design optimization of a FGM femoral component is carried out using finite element analysis (FEA) and response surface methodology (RSM). The results of using optimized FGM are then compared with the use of standard Co–Cr alloy in a femoral component knee implant to demonstrate relative performance.

Multiresponse Metamodeling in Simulation-Based Design Applications

The optimal design of complex systems in engineering requires the availability of mathematical models of system’s behavior as a function of a set of design variables; such models allow the designer to search for the best solution to the design problem. However, system models (e.g., computational fluid dynamics (CFD) analysis, physical prototypes) are usually time-consuming and expensive to evaluate, and thus unsuited for systematic use during design. Approximate models of system behavior based on limited data, also known as metamodels, allow significant savings by reducing the resources devoted to modeling during the design process. In this work in engineering design based on multiple performance criteria, we propose the use of multi-response Bayesian surrogate models (MR-BSM) to model several aspects of system behavior jointly, instead of modeling each individually. To this end, we formulated a family of multiresponse correlation functions, suitable for prediction of several response variables that are observed simultaneously from the same computer simulation. Using a set of test functions with varying degrees of correlation, we compared the performance of MR-BSM against metamodels built individually for each response. Our results indicate that MR-BSM outperforms individual metamodels in 53% to 75% of the test cases, though the relative performance depends on the sample size, sampling scheme and the actual correlation among the observed response values. In addition, the relative performance of MR-BSM versus individual metamodels was contingent upon the ability to select an appropriate covariance/correlation function for each application, a task for which a modified version of Akaike’s Information Criterion was observed to be inadequate.

Extensible multi-agent system for optimal design of complex systems using analytical target cascading

Analytical target cascading (ATC) has emerged as an integrated approach and methodology for optimal design of complex hierarchical systems with good convergence. It facilitates collaborative design problem solving and enables distributed computing to extend both the capability and capacity. ATC is very powerful yet requiring comprehensive understanding and efforts in setting up specific projects. This paper presents a generic and extensible information infrastructure called atcPortal in the form of a web portal where the ATC analyst defines the ATC problem using a special-purpose XML-based language called atcXML, conducts the ATC analysis, and obtains the analytical results. atcPortal not only reduces the overheads for analysts to set up the projects for practical applications, but also allows the researchers to experiment and extend the ATC methods under different system structures and/or different ATC strategies.

Optimization and integration of ground vehicle systems

This article deals with the optimal design of ground vehicles and their subsystems, with particular reference to ‘active’ safety and comfort. A review of state-of-the-art optimization methods for solving vehicle system design problems, including the integration of electronic controls, is given, thus further encouraging the use of such methods as standard tools for automotive engineers. Particular attention is devoted to the class of methods pertaining to complex system design optimization, as well as approaches for the optimal design of complex systems under uncertainty. Some examples of design optimizations are given in the fields of vehicle system dynamics, powertrain/internal combustion engine design, active safety and ride comfort, vehicle system design and lightweight structures, advanced automotive electronics, and smart vehicles.