A methodology for knowledge discovery to support product family design

This paper introduces a methodology for knowledge discovery related to product family design that integrates an ontology with data mining techniques. In the proposed methodology, the ontology represents attributes for the components of products in functional hierarchies. Fuzzy clustering is employed for data mining to first partition product functions into subsets for identifying modules in a given product family and then identify the similarity level of components in a module. Module categorization is introduced to support association rule mining for knowledge discovery related to platform design. We apply the proposed methodology to first develop and then utilize design knowledge for a family of power tools. Based on the developed design knowledge, a new platform is suggested to improve commonality in the power tool family.     

Factors affecting international product design

In this paper we examine the impact of changes of such factors as tariffs/import cost, exchange rate, and unit savings derived from economies of scale, on the product design of four international strategies which are characterised by two dimensions. The first dimension describes whether the company offers a standardised or a customised product. The second indicates whether the company centralises its production to a single facility in one country or decentralises its production to facilities located in each country. To address the above issue, we present a model that has elements from marketing and manufacturing.For the case where the product has one attribute, we show that when tariffs/import cost decrease, an international enterprise should respond by enhancing the features of its products. Similarly, the product features should be enhanced when the exchange rate increases or the unit savings derived from economies of scale increases. Numerical examples indicate that an international enterprise should change its production configuration from decentralised to centralised, in environments of high tariffs/import cost. Furthermore, an international enterprise should change its product policies from customised to standardised when the savings derived from economies of scale are high, and the exchange rate increases.     

Models for concurrent product and process design

We propose procedures to address product design and manufacturing process configurations concurrently in environments characterized by large degrees of product proliferation. Exploiting the intrinsic flexibility of product and process design, we present two approaches that synchronize production flows through the manufacturing system. These approaches integrate product and manufacturing system design decisions with operational concerns and provide powerful means for managing production in environments characterized by a proliferation of products. Experimental results show that the proposed methods can substantially reduce manufacturing lead times, work in process (WIP), and overall system complexity.     

Agent-based optimization for product family design

This paper presents a two-step approach to determine the optimal platform level for a selected set of product families and their variants. The first step employs a multi-objective optimization using an agent-based framework to determine the Pareto-design solutions for a given set of modules. The second step performs a post optimization analysis that includes application of the quality loss function (QLF) to determine the optimal platform level. The post optimization analysis yields the optimal platform level for a related set of product families and their variants. We demonstrate the working of the proposed method by using an example problem.     

Overlapping design and build cycles in product development

Product development time is a major component of the total time it takes to bring a product to market. Overlapping is the process of starting a downstream operation (such as manufacturing) before completing the upstream operation (such as design). There is an inherent risk in overlapping as a build-assembly may not be compatible with a subsequent assembly, requiring reconciliation work on the overlapped tasks. The ensuing trade-off between this risk, and the project time saved, is shown to possess some unique characteristics among different modes of overlapping. We study the optimization model of the trade-off in general, and analyze some special cases to establish interesting insights on overlapping. These include: advantage of overlapping in different scenarios, special properties of optimal overlap, and impact of parameter values on overlapping decisions. We show that several properties of a single overlap can be extended to multiple overlaps, and establish a limit to overlapping based on administrative cost of implementing overlap and shapes of cost functions. The critical role of the administrative cost (of coordinating overlaps) is also borne out by the model.     

Matching product architecture with supply chain design

Product architecture is typically established in the early stages of the product development (PD) cycle. Depending on the type of architecture selected, product design, manufacturing processes, and ultimately supply chain configuration are all significantly affected. Therefore, it is important to integrate product architecture decisions with manufacturing and supply chain decisions during the early stage of the product development. In this paper, we present a multi-objective optimization framework for matching product architecture strategy to supply chain design. In contrast to the existing operations management literature, we incorporate the compatibility between the supply chain partners into our model to ensure the long term viability of the supply chain. Since much of the supplier related information may be very subjective in nature during the early stages of PD, we use fuzzy logic to compute the compatibility index of a supplier. The optimization model is formulated as a weighted goal programming (GP) model with two objectives: minimization of total supply chain costs, and maximization of total supply chain compatibility index. The GP model is solved by using genetic algorithm. We present case examples for two different products to demonstrate the model’s efficacy, and present several managerial implications that evolved from this study.     

Reverse logistics network design for product recovery and remanufacturing

Due to environmental concerns, reverse logistics now is becoming an important strategy to increase customer satisfaction. This research develops a generic mixed integer nonlinear programming model (MINLP) for reverse logistics network design. This is a multi-echelon reverse logistics model. It maximizes total profit by handling products returned for repair, remanufacturing, recycling, reuse, or incineration/landfill. A hybrid genetic algorithm (GA) is proposed to solve the problem. The designed model is validated and tested by using a real-life example of recycling bulk waste in Taoyuan City, Taiwan. Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model. Post-optimality analysis and comparison show that the proposed model performs better than current reverse logistic operations and the proposed hybrid GA demonstrates the efficiency of solving the complex reverse logistics problem.     

A web-based methodology for product design evaluation and optimisation

The authors have developed a methodology that takes advantages of the World Wide Web to analyse and develop optimal new product designs. This paper describes the methodology and illustrates its application to a case study involving the design of an actual Web site where music CDs are sold. The proposed methodology has the following features: (a) it is based on a design inspired by conjoint analysis; (b) it involves unobtrusive electronic measurement of the actual behavior of Web users who remain undisturbed by experimental factors; and (c) it utilises an integer programming approach to seek optimal Web site configurations. The methodology uses limited dependent variable methods to develop response models that provide the basis for the development of objective functions for an optimisation model. The optimisation model can consider either single or multiple objective functions by using a Pareto optimum approach.     

MARKEX: An intelligent decision support system for product development decisions

A new methodology for the development of new products and an intelligent DSS, named MARKEX, which is an implementation of this methodology, are presented in this paper. The system acts as a consultant for marketers, providing visual support to enhance understanding and to overcome lack of expertise. The databases of the system are the results of consumer surveys, as well as financial information of the enterprises involved in the decision making process. The system's model base encompasses statistical analysis, preference analysis, and consumer choice models. MARKEX incorporates partial knowledge bases to support decision makers in different stages of the product development process.     

Decision support for spare parts acquisition in post product life cycle

A major task in service management is the timely and cost efficient provision of spare parts for durable products. This especially holds good, when the regular production of the product, its components and parts has been discontinued, but customer service still has to be guaranteed for quite a long time. In such post product life cycle period, three options are available to organize the spare parts acquisition, namely (i) setting up a single large order within the final lot of regular production, (ii) performing extra production runs until the end of service and (iii) using remanufacturing to gain spare parts from used products. These three options are characterized by different cost and flexibility properties. Due to the time-variability and uncertainty of demands for spare parts and also that of the returns of used products, it is a challenging task to find out the optimal combination of these three options. In this paper we show how this problem can be modeled and solved by Decision Tree and stochastic Dynamic Programming procedure. Based on the Dynamic Programming approach a heuristic method is proposed, which can be employed to come up with a simple solution procedure for real-world spare parts acquisition problems during the post product life cycle. A numerical example is presented to demonstrate the application of the solution methods described in the paper.     

An application of Lipschitzian global optimization to product design

The issue of finding feasible mixture designs is formulated and solved as a Lipschitzian global optimization problem. The solution algorithm is based on a simplicial partition strategy. Implementation aspects and extension possibilities are treated in some detail, providing also numerical examples.     

Optimal product design using a colony of virtual ants

The optimal product design problem, where the “best” mix of product features are formulated into an ideal offering, is formulated using ant colony optimization (ACO). Here, algorithms based on the behavior of social insects are applied to a consumer decision model designed to guide new product decisions and to allow planning and evaluation of product offering scenarios.     

A pruning heuristic for use with multisource product design

Products can be improved by integrating multiple viewpoints during the design process. A model has been developed that uses conjoint data from consumers and designers and optimizes a product design based on the total share-of-choices. Because the problem becomes very difficult to solve as size increases, a heuristic is developed, based on pruning techniques, to solve the problem to near-optimality in a shorter period of time as compared to complete enumeration. The performance of the heuristic is demonstrated through the use of test data and by comparison to a Genetic Algorithm (GA) based heuristic and Tabu search. Structural results for the heuristic are also provided.     

Hierarchical game joint optimization for product family-driven modular design

Product family design takes advantage of modularity to enable product variety while maintaining mass production efficiency. Focusing on a set of similar product variants, product family modularity (PFM) is achieved by reusing common components and minimizing fulfillment costs throughout the product realization process. On the other hand, traditional modular design emphasizes technical system modularity (TSM) that focuses on a single product and is geared towards product decomposition in light of technical feasibility. While it is appealing to incorporate product family considerations into the prevailing modularization theories and methods, the key challenge lies in that TSM and PFM are essentially associated with different goals and decision criteria. This leads to a dilemma that TSM and PFM are competing in decision making for identification of modules by grouping similar components. Realizing the importance of game-theoretic decision making underlying product family-driven modular design, this paper proposes to leverage TSM and PFM within a coherent framework of joint optimization. A hierarchical game joint optimization model is developed in line with bilevel programming. A two-dimension evaluation criteria taxonomy is presented for TSM and PFM criteria measure. A bilevel nested genetic algorithm is put forward for efficient solution of the non-linear hierarchical joint optimization model. A case study of robotic vacuum cleaner modular design is reported to gain insight into joint optimization of TSM and PFM. Results and analyses demonstrate that the proposed hierarchical joint optimization model is robust and can empower modular design in cohesion with product family concerns.     

Logistics network design for product recovery in fuzzy environment

The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm.     

Optimal design of structures with variable support conditions

The conditions for optimal location of supports are derived for the case of elastic design for specified mean compliance and plastic design for given safety factor of plastic collapse. The optimal design problem is next formulated including position-dependent cost of support into the design procedure. Both rigid and flexible or yielding supports are considered.     

p-adic Modular Symbols and A-adic Modular Forms

A construction of A-adic modular forms from p-adic modular symbols is described. It shows that each A linear map satisfying some certain conditions from the module of p-adic modular symbols to A corresponds to a A-adic modular form.     

A model of product line design and introduction sequence with reservation utility

Cannibalization is a major concern for a firm when designing a product line. In addition, external options from outside the firm’s product line may also play a significant role. In this paper, we investigate the impact of external options, represented by reservation utility, on product line design and introduction sequence. We find that: (a) heterogeneous reservation utility defines the relative attractiveness of segments and corresponding product line; (b) reservation utility makes it more favorable to introduce products sequentially rather than simultaneously; (c) aggregating segments is an effective way to mitigate cannibalization when it becomes too difficult to manage with different values of reservation utility across multiple segments; and (d) introducing products in a non-monotone order of quality can improve profit from simultaneous introduction when the value of reservation utility of a middle segment is particularly high.