Masataka Yoshimura

Decision-making support system for human resource allocation in product development projects

Recent dismal economic conditions and a ruthlessly competitive environment have forced many companies to restructure, or reorganize their priorities. For such companies, the concentration of various resources upon their particular corporate strong points has become a central strategy. Consequently, there has been a rapid increase in the importance of (1) selecting profitable projects from a wealth of possible alternatives and (2) optimizing the allocation of current resources among the selected projects. This paper proposes an optimization system for project selection that not only yields the most beneficial project set, but also the optimum allocation of human resources for the selected projects. The optimization system consists of two algorithms, namely (1) a project selection algorithm for choosing the set of projects that maximizes the total estimated profit, and (2) a human resource allocation algorithm for optimally placing human resources among the selected projects, having considered the satisfaction level provided by each employees skills, personal motivation and career goals.

A multiple cross-sectional shape optimization method for automotive body frames

Automotive body frames, which profoundly affect automotive performance such as crashworthiness, are generally formed using pressed metal sheets, and the assembled cross-sectional shapes govern the frame characteristics. This paper proposes a cross-sectional shape generating method for achieving the cross-sectional properties assigned by design engineers. The cross-sectional shape-generating problem for pressed metal sheets is formulated as a multiobjective optimization problem that involves a marriage of continuous variables, such as shape dimensions, and discrete design variables, such as types of material and their thicknesses. Genetic algorithms are applied to solve the optimization problem.

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Abstract Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

Integrated optimization of machine product design and process design

In order to attain the true integration of computer-aided design and computer-aided manufacturing not only is a smooth flow of information required, but also decision making for both product design and process design must be synthesized. In this paper an integrated design process is proposed in which decisions concerning both product design and process design are simultaneously made. According to the proposed design procedures, an integrated optimization problem is formulated. This optimization is expressed as a multiobjective optimization problem which produces many Pareto optimum solution sets corresponding to combinations of materials used for parts. The algorithm for solving the problem is also presented. The proposed method is applied to designing a cylindrical co-ordinate robot, thereby demonstrating the effectiveness of conducting a simultaneous process through product design and process design.

System Design Optimization for Product Manufacturing

The current ultra-competitive manufacturing arena demands that a multitude of factors be considered during product design and manufacturing, in addition to core concerns such as product performances, product qualities, and manufacturing costs. Safety aspects, environmental impact, recycling issues, and the satisfaction of product consumers and users must all be addressed for a product design to be successful. Optimum system technologies that can concurrently consider a broad range of factors pertaining to product manufacturing, and streamline decision-making for product design and manufacturing are required. This article describes a total system design optimization method that assists decision-making across the entire spectrum of product manufacturing processes, from the conceptual design stage to final product realization. System design optimizations for advanced product manufacturing are discussed, starting from historical changes in manufacturing methodology paradigms and product manufacturing criteria, and the roles of concurrent engineering and collaboration concepts are then clarified as technological concepts that form the basis for innovative product manufacturing from the standpoint of system optimization. Next, aspects of peoples involvement in product manufacturing are classified and technologies that support product manufacturing are summarized from the standpoint of providing sophisticated decision-making assistance to design and manufacturing personnel. Finally, based on the foregoing discussions, new methods for optimal decision-making technologies are proposed, with the aim of facilitating effective decision-making during the task of designing sophisticated products for manufacture.

Enhanced multiobjective particle swarm optimization in combination with adaptive weighted gradient-based searching

This article proposes a new multiobjective optimization method for structural problems based on multiobjective particle swarm optimization (MOPSO). A gradient-based optimization method is combined with MOPSO to alleviate constraint-handling difficulties. In this method, a group of particles is divided into two groups—a dominated solution group and a non-dominated solution group. The gradient-based method, utilizing a weighting coefficient method, is applied to the latter to conduct local searching that yields superior non-dominated solutions. In order to enhance the efficiency of exploration in a multiple objective function space, the weighting coefficients are adaptively assigned considering the distribution of non-dominated solutions. A linear optimization problem is solved to determine the optimal weighting coefficients for each non-dominated solution at each iteration. Finally, numerical and structural optimization problems are solved by the proposed method to verify the optimization efficiency.

Multiobjective Design Optimization of Machine-Tool Spindles

A method of multiobjective design optimization is presented in order to evaluate conflicting objectives in designing machine-tool spindles. A Pareto optimum set which shows a trade-off relationship between two objectives minimizing the total weight of the spindle and the static torsional or bending compliance, is derived using the Kuhn-Tucker necessary conditions for optimality, and through analyses of objective and constraint functions. Weighting factors between the two objectives are obtained on the Pareto optimum set. Expanded problems and numerical examples are given.

Optimal multilevel redundancy allocation in series and series-parallel systems

To achieve truly optimal system reliability, the design of a complex system must address multilevel reliability configuration concerns at the earliest possible design stage, to ensure that appropriate degrees of reliability are allocated to every unit at all levels. However, the current practice of allocating reliability at a single level leads to inferior optimal solutions, particularly in the class of multilevel redundancy allocation problems. Multilevel redundancy allocation optimization problems frequently occur in optimizing the system reliability of multilevel systems. It has been found that a modular scheme of redundancy allocation in multilevel systems not only enhances system reliability but also provides fault tolerance to the optimum design. Therefore, to increase the efficiency, reliability and maintainability of a multilevel reliability system, the design engineer has to shift away from the traditional focus on component redundancy, and deal more effectively with issues pertaining to modular redundancy. This paper proposes a method for optimizing modular redundancy allocation in two types of multilevel reliability configurations, series and series-parallel. A modular design variable is defined to handle modular redundancy in these two types of multilevel redundancy allocation problem. A customized genetic algorithm, namely, a hierarchical genetic algorithm (HGA), is applied to solve the modular redundancy allocation optimization problems, in which the design variables are coded as hierarchical genotypes. These hierarchical genotypes are represented by two nodal genotypes, ordinal and terminal. Using these two genotypes is extremely effective, since this allows representation of all possible modular configurations. The numerical examples solved in this paper demonstrate the efficacy of a customized HGA in optimizing the multilevel system reliability. Additionally, the results obtained in this paper indicate that achieving modular redundancy in series and series-parallel systems provides significant advantages when compared with component redundancy. The demonstrated methodology also indicates that future research may yield significantly better solutions to the technological challenges of designing more fault-tolerant systems that provide improved reliability and lower lifecycle cost.

Hierarchical arrangement of characteristics in product design optimization

This paper proposes a machine product design optimization method based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, that is especially responsive to the detailed features or difficulties presented by specific design problems. The optimization problems examined here are expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively provides optimum design solutions, but also facilitates deeper insight into the design optimization results, so that ideas for optimum solution breakthroughs are more easily obtained. An applied example is given to demonstrate the effectiveness of the proposed method.

Design of Compliant Thermal Actuators Using Structural Optimization Based on the Level Set Method

Compliant mechanisms are a new type of mechanism, designed to be flexible to achieve a specified motion as a mechanism. Such mechanisms can function as compliant thermal actuators in Micro-Electro Mechanical Systems (MEMS) by intentionally designing configurations that exploit thermal expansion effects in elastic material when appropriate portions of the mechanism structure are heated. This paper presents a new structural optimization method for the design of compliant thermal actuators based on the level set method and the Finite Element Method (FEM). First, an optimization problem is formulated that addresses the design of compliant thermal actuators considering the magnitude of the displacement at the output location. Next, the topological derivatives that are used when introducing holes during the optimization process are derived. Based on the optimization formulation and the level set method, a new structural optimization algorithm is constructed that employs the FEM when solving the equilibrium equations and updating the level set function. The re-initialization of the level set function is performed using a newly developed geometry-based re-initialization scheme. Finally, several design examples are provided to confirm the usefulness of the proposed structural optimization method.Copyright