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The optimal design of hybrid power generation systems (HPGSs) can significantly improve the technical and economic performance of power supply. However, the discrete-time simulation with logical disjunctions involved in HPGS design usually leads to nonsmooth optimization model, to which well-established techniques for smooth nonlinear optimization cannot be directly applied. This paper casts the HPGS design optimization problem as a multidisciplinary design optimization problem with complementarity constraints, a formulation that introduces a complementarity formulation of the nonsmooth logical disjunction, as well as a time horizon decomposition framework, to ensure a fast local solution. A numerical study of a stand-alone hybrid photovoltaic/wind power generation system is presented to demonstrate the effectiveness of the proposed approach. [DOI: 10.1115/1.4002292]

Product take-back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. 'Selling a service' (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back but current methods assume a fixed leasing period. What is needed is a method for fine tuning the time span of customers' life cycles in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market and also across different lifecycles within each segment. Post-optimal studies help determine feasibility of component redesign in addition to parts consolidation. The proposed method is illustrated through an example involving personal computers. [DOI: 10.1115/1.4002142]

Product design is one of the most important sectors influencing global sustainability, as almost all the products consumed by people are outputs of the product development process. In particular, early design decisions can have a very significant impact on sustainability. These decisions not only relate to material and manufacturing choices but have a far-reaching effect on the product's entire life cycle, including transportation, distribution, and end-of-life logistics. However, key challenges have to be overcome to enable eco-design methods to be applicable in early design stages. Lack of information models, semantic interoperability, methods to influence eco-design thinking in early stages, measurement science and uncertainty models in eco-decisions, and ability to balance business decisions and eco-design methodology are serious impediments to realizing sustainable products and services. Therefore, integrating downstream life cycle data into eco-design tools is essential to achieving true sustainable product development. Our review gives an overview of related research and positions early eco-design tools and decision support as a key strategy for the future. By merging sustainable thinking into traditional design methods, this review provides a framework for ongoing research, as well as encourages research collaborations among the various communities interested in sustainable product realization. [DOI: 10.1115/1.4002308] Keywords: sustainable design, eco-design, product design, manufacturing, supply chain

Product recovery has become a field of rapidly growing interest for product manufacturers as a promising solution for product stewardship as well as for economic viability. Because product recovery is highly dependent on the way a product is designed, it should be considered in the design stage so that the product is designed to have high recovery. potential. To make a product easy to recover, manufacturers first need to understand the links between product design and recovery profit and be able to evaluate which design is better than others and why. This study proposes a framework for analyzing how design differences affect product recovery and what architectural characteristics are desirable from the end-of-life perspective. For better design evaluation, an optimization-based model is developed, which considers product design and recovery, network design simultaneously. For illustration, a comparative study with cell phone examples is presented. Three cell phone handset designs that share the same design concept but have different architectural characteristics are created, and the recovery potential of each design variant is evaluated under three different recovery scenarios. The results show that the framework can highlight preferred design alternatives and their design implications for the economic viability of end-of-life recovery. [DOI: 10.1115/1.4001411]

Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC)--a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving this class of problem. In this paper, we propose a new decomposition algorithm for the MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one solution. [DOI: 10.1115/1.4001206]

Environmental protection legislation, consumer interest in 'green' products, a trend toward corporate responsibility and recognition of the potential profitability of salvaging operations, has resulted in increased interest in product take back. However, the cost effectiveness of product take-back operations is hampered by many factors, including the high cost of disassembly and a widely varying feedstock of dissimilar products. Two types of decisions, must be made, how to carry out the disassembly process in the most efficient. manner to 'mine' the value-added that is still embedded in the product, and then how to best utilize that value-added once it is recovered. This paper presents a method for making those decisions. The concept of a transition matrix is integrated with mixed integer linear programming to determine the extent to which products should be disassembled and simultaneously determine the optimal end-of-life (EOL) strategy for each resultant component or subassembly: The main contribution of this paper is the simultaneous consideration of selective disassembly multiple products, and the value added that remains in each component or subassembly. Shared disassembly operations and capacity limits are considered. An example using two cell phone products illustrates application of the model. The obtained results demonstrate the most economical level of disassembly for each cell phone and the best EOL options for each resultant module. In addition, the cell phone example shows that sharing disassembly operations between different products makes disassembly more cost effective compared with the case in which each product is disassembled separately. [DOI: 10.1115/1.4001207]

Recently, online user-generated data have been used as an efficient resource for customer analysis. In the product design area, various methods for analyzing customer preference for product features have been suggested. However, most of them focused on feature categories rather than product components which are crucial in practical applications. To address that limitation, this paper proposes a new methodology for extracting sub-features from online data. First, the method detects phrases in the data and filtered them using product manual documents. The filtered phrases are embedded into vectors, and then they are divided into several groups by two clustering methods. The resulting clusters are labeled by analyzing items in each cluster. Finally, cue phrases for sub-features are obtained by selecting clusters with labels representing product features. The proposed methodology was tested on smartphone review data. The result provides feature clusters containing sub-feature phrases with high accuracy. The obtained cue phrases will be used in analyzing customer preferences for sub-features and this can help product designers determine the optimal component configuration in embodiment design. [ABSTRACT FROM AUTHOR]

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit. [DOI: 10.1115/1.2838336]

Analytical target cascading is a hierarchical multilevel multidisciplinary design methodology. In analytical target cascading, top-level design targets (i.e., specifications) are propagated to lower-level design problems in a consistent and efficient manner. In this paper, a modified Lagrangian dual formulation and coordination for analytical target cascading are developed to enhance a formulation and coordination proposed earlier in the literature. The proposed approach guarantees all the properties established earlier and additionally offers new significant advantages. As established previously for the convex case, the proposed analytical target cascading coordination converges to a global optimal solution with corresponding optimal Lagrange multipliers in the dual space. The Lagrange multipliers can be viewed as the weights for deviations in analytical target cascading formulations. Thus the proposed coordination algorithm finds the optimal solution and the optimal weights for the deviation terms simultaneously. The enhancement allows for target cascading between levels, for the use of augmented Lagrangian to improve convergence of the coordination algorithm, and for prevention of unboundedness. A guideline to set the step size for subgradient optimization when solving the Lagrangian dual problem is also proposed.

Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation. [DOI: 10.1115/1.2205870] Keywords: hierarchical multilevel optimization, analytical target cascading, uncertainty, probabilistic approach, design targets and consistency, coordination strategy, moments

In this article the process of rigorously setting supersystem targets in an enterprise context is explored as a model-based approach termed 'analytical target setting.' Engineering design decisions have more value and lasting impact if they are made in the context of the enterprise that produces the designed product. Setting targets that the designer must meet is often done at a high level within the enterprise, however, with inadequate consideration of the engineering design embodiment and associated cost. For complex artifacts produced by compartmentalized hierarchical enterprises, the challenge of linking the target setting rationale with the product instantiation is particularly demanding. The previously developed analytical target cascading process addresses the problem of translating top level design targets into design targets for all systems in a multilevel hierarchically structured product, so that local targets are consistent with each other and top targets can be met as closely as possible. The effectiveness of linking analytical target setting and target cascading is demonstrated in a hybrid electric automotive truck vehicle example. The manufacturer introduces a new product (hybrid electric truck) in the market under uncertainty in fuel prices during the life cycle of the vehicle. The example demonstrates a clear interaction between the enterprise decision making and the engineering product development. [DOI: 10.1115/1.2125972]

Enterprise-level business decisions are linked with engineering product decisions by integrating enterprise utility optimization and engineering design optimization under a hierarchical, multilevel, decision-based design framework. The enterprise problem sets attribute targets, that is, specifications, for engineering product development, which then optimizes product performance within the feasible design space to match the targets with minimum deviations. When the feasible domain imposed by engineering product development is disconnected in the space of attribute targets, an engineering design with the minimum deviation from the targets may not correspond to the design with the maximum utility value, even though the design is a converged solution from the multilevel optimization. To address this issue, a new algorithm is developed, which systematically explores the target space to lead the engineering product development to a feasible and optimal design in the enterprise context. Analytical examples and an automotive suspension design case study are presented to demonstrate the effectiveness of the proposed methodology.