Jiafu Tang

A new approach to quality function deployment planning with financial consideration

Quality function deployment (QFD) is becoming a widely used customer-oriented approach and tool in product design. Taking into account the financial factors and uncertainties in the product design process, this paper deals with a fuzzy formulation combined with a genetic-based interactive approach to QFD planning. By introducing new concepts of planned degree, actual achieved degree, actual primary costs required and actual planned costs, two types of fuzzy optimisation models are discussed in this paper. These models consider not only the overall customer satisfaction, but also the enterprise satisfaction with the costs committed to the product. With the interactive approach, the best balance between enterprise satisfaction and overall customer satisfaction can be obtained, and the preferred solutions under different business criteria can be achieved through human-computer interaction.

Fuzzy regression-based mathematical programming model for quality function deployment

Quality function deployment (QFD) is becoming a widely used customer-driven approach and tool in product design. The inherent fuzziness in QFD modelling makes fuzzy regression more appealing than classical statistical tools. A new fuzzy regression-based mathematical programming approach for QFD product planning is presented. First, fuzzy regression theories with symmetric and non-symmetric triangular fuzzy coefficients are discussed to identify the relational functions between engineering characteristics and customer requirements and among engineering characteristics. By embedding the relational functions obtained by fuzzy regression, a mathematical programming model is developed to determine targets of engineering characteristics, taking into consideration the fuzziness, financial factors and customer expectations among the competitors in product development process. The proposed modelling approach can help design team assess relational functions in QFD effectively and reconcile tradeoffs among the various degree of customer satisfaction and determine a set of the level of attainment of engineering characteristics for the new/improved product towards a higher customer expectation within design budget. The comparison results under symmetric and non-symmetric cases and the simulation analysis are made when the approach is applied to a quality improvement problem for an emulsification dynamite packing machine.

Estimating the functional relationships for quality function deployment under uncertainties

Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well as those among ECs into the problem formulation. Owing to the typical vagueness or imprecision of functional relationships in a product, product planning is becoming more difficult, particularly in a fuzzy environment. In this paper, an asymmetric fuzzy linear regression approach is proposed to estimate the functional relationships for product planning based on QFD. Firstly, by integrating the least-squares regression into fuzzy linear regression, a pair of hybrid linear programming models with asymmetric triangular fuzzy coefficients are developed to estimate the functional relationships for product planning under uncertainties. Secondly, using the basic concept of fuzzy regression, asymmetric triangular fuzzy coefficients are extended to asymmetric trapezoidal fuzzy coefficients, and another pair of hybrid linear programming models with asymmetric trapezoidal fuzzy coefficients is proposed. The main advantage of these hybrid-programming models is to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. Next, the illustrated example shows that trapezoidal fuzzy number coefficients have more flexibility to handle a wider variety of systematic uncertainties and ambiguities that cannot be modeled efficiently using triangular number fuzzy coefficients. Both asymmetric triangular and trapezoidal fuzzy number coefficients can be applicable to a much wider variety of design problems where uncertain, qualitative, and fuzzy relationships are involved than when symmetric triangular fuzzy numbers are used. Finally, future research direction is also discussed.

Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements into the technical attributes of a product. Deriving the rating order of technical attributes from input variables is a crucial step in applying QFD. When the relative weights of customer requirements and the relationship measures between customer requirements and technical attributes are expressed as fuzzy numbers, calculating the importance of each technical attribute falls into the category of fuzzy weighted average, in which the derived membership function of the fuzzy importance of each technical attribute is not explicitly known. Thus, most ranking methods are not suitable under these circumstances. A method is proposed in this paper using fuzzy weighted average method in the fuzzy expected value operator in order to rank technical attributes in fuzzy QFD. An example of a flexible manufacturing system design is cited to demonstrate the application of the proposed approach.

Product design resources optimization using a non-linear fuzzy quality function deployment model

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/ mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-defined or incomplete understanding of the relationship between a given set of customer requirements (CR) and TA, the complexity of interdependence among TA, etc. A non-linear fuzzy model is proposed here to offer a more practical and effective means of incorporating the resource factors in QFD planning. The impacts of the correlation among TA are also considered. In the model, the resources for achieving the design target for a certain TA are expressed in a non-linear formulation of its relationship, correlation as well as interdependence with other customer requirements or TA. The concepts of the achieved attainments and planned attainments for TA, and the corresponding primary costs, planned costs and actual costs are introduced. Solutions to the non-linear fuzzy model can be obtained using a parametric optimization method or a hybrid genetic algorithm. A case study is also given to illustrate how the proposed fuzzy model and the optimization routine can be applied to help decision-makers in a company deploy their design resources towards gaining better overall customer satisfaction.

Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is renowned for translating customer requirements into the technical attributes of a product. To deal with the imprecise elements in the development process, fuzzy set theory is incorporated into QFD methodology. A novel fuzzy expected value operator approach is proposed in this paper to model the QFD process in a fuzzy environment, and two fuzzy expected value models are established to determine the target values of engineering characteristics in handling different practical design scenarios. Analogous to stochastic programming, the underlying philosophy in the proposed approach is based on selecting the decision with maximum expected returns. Furthermore, the proposed approach considers not only the inherent fuzziness in the relationships between customer requirements and engineering characteristics, but also the correlation among engineering characteristics. These two kinds of fuzzy relationships are aggregated to give the fuzzy importance of individual engineering characteristics. Finally, an example of a quality improvement problem of a motor car design is given to demonstrate the application and performance of the proposed modelling approach.

Capacitated dynamic lot sizing problems in closed-loop supply chain

In this paper, we address the capacitated dynamic lot sizing problem arising in closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed or be remanufactured to be sold as new ones again; hence the market demands can be satisfied by either newly produced products or remanufactured ones. The capacities of production, disposal and remanufacturing are limited, and backlogging is not allowed. A general model of this problem is formulated, and several useful properties of the problem are characterized when cost functions are concave. Moreover, this problem is analyzed and solved to optimality using dynamic programming algorithms under different scenarios. It is shown that the problem with only disposal or remanufacturing can be converted into a traditional capacitated lot sizing problem and be solved by a polynomial algorithm if the capacities are constant. A pseudo-polynomial algorithm is proposed for the problem with both capacitated disposal and remanufacturing. The problem with capacitated production and remanufacturing and the problem with uncapacitated production and capacitated remanufacturing are also analyzed and solved. Through numerical experiments we show that the proposed algorithms perform well when solving problems of practical sizes. From the experimental results also indicates that it is worthwhile to expand the remanufacturing capacity only when returned products exist in a relatively long planning horizon, and production capacities have little effect on the remanufacturing plan when the demand is mainly satisfied by the production.

Fuzzy formulation for multi-product aggregate production planning

Abstract Considering that the demand requirements are fuzzy demand in each period during the planning horizon, this paper focuses on a novel approach to modelling multi-product aggregate production planning (APP) problems with fuzzy demands and fuzzy capacities. The objective of the problem considered is to minimize the total costs of quadratic production costs and linear inventory holding costs. By means of formulation of fuzzy demand, fuzzy addition and fuzzy equation, the productioninventory balance equation in single stage and dynamic balance equation are formulated as soft equations in terms of a degree of truth, and interpreted as the levels of satisfaction with production and inventory plan in meeting market demands. As a result, the multi-product aggregate production planning problem with fuzzy demands and fuzzy capacities can be modelled into a fuzzy quadratic programming with fuzzy objective and fuzzy constraints. The fuzzy solution approach to the model is also proposed in this paper. It can offer the decision-maker (DM) more choices to construct an aggregate production plan so as to ensure the feasibility of family disaggregation plan, especially in fuzzy environment.

Optimization of software components selection for component-based software system development

During the last two decades, there has been a growing interest in component-based software system (CBSS) development both in academia and in industry. In CBSS development, it is common to identify software modules first. Once they are determined, we need to select appropriate software components for each software module. However, very few research works so far have addressed the theoretical aspect especially in the optimization of software component selection for CBSS development. Previous studies of CBSS development seldom considered the influence of software components on coupling and cohesion of software modules. In this paper, the formulation of an optimization model of software components selection for CBSS development is described. The model has two objectives: maximizing the functional performance of the CBSS and maximizing the cohesion and minimizing the coupling of software modules. A genetic algorithm (GA) is introduced to solve the optimization model for determining the optimal selection of software components for CBSS development. An example of developing a financial system for small- and medium-size enterprises is used to illustrate the proposed methodology.

Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight

The multilevel lot-sizing (MLLS) problem is a key production planning problem in materials requirements planning (MRP) system. The MLLS problem deals with determining the production lot-sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost, the inventory carrying cost, the back ordering cost and etc. This paper proposed a particle swarm optimization (PSO) algorithm for solving the uncapacitated MLLS problem with assembly structure. All the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number or a positive one. The feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm (GA).