Juite Wang

FUZZY OUTRANKING APPROACH TO PRIORITIZE DESIGN REQUIREMENTS IN QUALITY FUNCTION DEPLOYMENT

Identification of important design requirements for product development is critical because it leads to successful products with shorter development time. The quality function deployment (QFD) is a tool to help the product development team systematically determine the design requirements for developing a product with higher customer satisfaction. However, it is more difficult to assess the performance of a design with accurate quantitative values, due to the imprecise and incomplete information available at the early design stage. A decision model is needed to assist team members in selecting the critical design requirements for product development. This paper considers the QFD planning as a multi-criteria decision problem and proposes a new fuzzy outranking approach to prioritize design requirements recognized in QFD. The inputs required for QFD are represented with linguistic terms that are characterized by fuzzy sets. The fuzzy outranking relation is used to model the imprecise preference relations bet...

FUZZY DECISION MODELING FOR SUPPLY CHAIN MANAGEMENT

Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships between various entities exist in the SC. Moreover, the reducing product life cycle and the heightened expectations of customers have also made the SC even harder to manage, especially for innovative products. Although the innovative products can enable a company to achieve higher profit margins, they make demands for them unpredictable, because no historical data is available. This paper develops a fuzzy decision methodology that provides an alternative framework to handle SC uncertainties and to determine SC inventory strategies, while there is lack of certainty in data or even lack of available historical data. Fuzzy set theory is used to model SC uncertainty. A fuzzy SC model based on possibility theory is developed to evaluate SC performances. Based on the proposed fuzzy SC model, a genetic algorithm approach is developed to determine the order-up-to levels of stock-keeping units in the SC to minimize the SC inventory cost subject to the restriction of fulfilling the target fill rate of the finished product. The proposed model allows decision makers to express their risk attitudes and to analyze the trade-off between customer service level and inventory investment in the SC and better SC inventory strategies can be made. A simulation approach is used to validate the concept developed.

Efficient organizing of design activities

Concurrent design should result in reduction of the duration of a design project, cost reduction, and better quality of the final design; however, it may increase the complexity of the design process and make it more difficult to manage. In this paper, an algorithm is developed for organizing design activities in order to effectively produce an acceptable design. The relationship among design activities is represented with an incidence matrix and the corresponding directed graph. The design process is simplified by identifying and analysing design activities that are coupled. The algorithm presented in the paper generates a sequence of desigmactivities such that the number of cycles is minimized, i.e. the product development time is reduced, The concepts presented are illustrated with examples.

A fuzzy multicriteria group decision making approach to select configuration items for software development

Selection of configuration items in software configuration management is important to determine the software quality and reduce the development time and cost. The objective of this research is to develop a multicriteria group decision making model based on fuzzy set theory to improve the configuration items selection process. Since most information available in this stage is not numerical, fuzzy set theory is used to represent the evaluation ratings of candidate items. The developed model ranks candidate items into partial or complete orders that can assist decision makers in selecting more proper set of configuration items. The consensus measures are also developed to determine the group acceptability of the obtained ranking orders. In addition, sensitivity analysis can be performed to examine the solution robustness. An example of flight simulator development project is used to illustrate the concept developed.

A fuzzy robust scheduling approach for product development projects

Abstract Efficient scheduling of a product development project is difficult, since a development project is usually unique in nature and high level of design imprecision exists at the early stages of product development. Moreover, risk-averse project managers are often more interested in estimating the risk of a schedule being late over all potential realizations. The objective of this research is to develop a robust scheduling methodology based on fuzzy set theory for uncertain product development projects. The imprecise temporal parameters involved in the project are represented by fuzzy sets. A measure of schedule robustness based on qualitative possibility theory is proposed to guide the search process to determine the robust schedule; i.e., the schedule with the best worst-case performance. A genetic algorithm approach is developed for solving the problem with acceptable performance. An example of electronic product development project is used to illustrate the concept developed.

Ranking engineering design concepts using a fuzzy outranking preference model

Abstract The conceptual design evaluation is important, since the poor selection of a design concept can rarely be compensated at later design stages. Due to subjective and incomplete design information collected at the early design stage, it is difficult to select the “best” design concepts from a number of alternatives. To tackle this problem, an outranking preference model based on the possibility theory is developed in this paper. The fuzzy outranking relation is developed to model the imprecise preference relation between each pair of design concepts. A design concept outranked others if and only if there is sufficient evidence to support that the concept is superior or at least equal to the others. According to the fuzzy outranking relation identified between each pair of design concepts, three types of indices are developed to determine the non-dominated design concepts for continuous improvement or further development at later design stages. Moreover, the sensitivity analysis is used to examine the robustness of the result. The fuzzy outranking preference model developed is more suitable to be used for concept selection in the imprecise and uncertain design environment.

A possibilistic decision model for new product supply chain design

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.

Dependency analysis in constraint negotiation

Problems in engineering design are constraint-oriented and often involve multiple perspectives. Designers must consider not only functional requirements of a product but also its life-cycle perspectives. Each perspective has its own set of constraints which may contain conflicting or unsatisfied requirements. A human designer can not be aware of all constraints and design alternatives all the time. The objective of this paper is to develop a methodology to assist designers in negotiation of constraints. A network model is proposed to represent relationships among design variables. An algorithm is developed to derive dependencies between design variables and goals. Based on the dependencies obtained, design modifications are generated for resolving conflicts. In the second part of the paper, a fuzzy-logic-based approach is used to model imprecise dependencies between variables in the case when no sufficient quantitative information is available. The approach proposed can be used to increase the amount of information provided to the designers for making decisions. >

A fuzzy project scheduling approach to minimize schedule risk for product development

The efficient management of product development projects is important to reduce the required development time and cost. However, each project is unique in nature and the duration of activities involed in a project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this research is to develop a fuzzy scheduling methodology to deal with this problem. Possibility theory is used to model the uncertain and flexible temporal information. The concept of schedule risk is proposed to evaluate the schedule performance. A fuzzy beam search algorithm is developed to determine a schedule with the minimum schedule risk and the start time of each activity is selected to maximize the minimum satisfaction degrees of all temporal constraints. In addition, the properties of schedule risk are also discussed. We show that the proposed methodology can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment. An example with an electronic product development project is used to illustrate the developed approach.

An overlapping process model to assess schedule risk for new product development

Faster time-to-market for new products is important for hi-tech corporations to gain premium pricing and higher sales volume. An activity overlapping strategy is a frequently used technique in practice for quicker product launch. However, complex interaction patterns between components and activities increase the chance of unanticipated iterations that may lead to late time-to-market. This paper presents an overlapping process model to analyze the impact of process structure on the lead-time of a complex development project. Information evolution and change sensitivity, which are two major factors for activity overlapping, are considered in the proposed model and a simulation algorithm is developed to analyze the impacts of process structure on the development lead-time. The example of a battle tank simulator project is used to illustrate the proposed approach. The computational experiment shows that the proposed approach allows project managers to design a better process structure to minimize the risk of being late-to-market.