Tijen Ertay

A fuzzy optimization model for QFD planning process using analytic network approach

In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and product development system achieving higher customer satisfaction. Quality function deployment (QFD) is used as a powerful tool for improving product design and quality, and procuring a customer-driven quality system. In this paper, an integrated framework based on fuzzy-QFD and a fuzzy optimization model is proposed to determine the product technical requirements (PTRs) to be considered in designing a product. The coefficients of the objective function are obtained from a fuzzy analytic network process (ANP) approach. Fuzzy analytic hierarchy process (AHP) is also used in the proposed framework. An application in a Turkish Company producing PVC window and door systems is presented to illustrate the proposed framework. � 2004 Elsevier B.V. All rights reserved.

Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems

Facility layout design (FLD) has a very important effect on the performance of a manufacturing system. The concept of FLD is usually considered as a multiobjective problem. For this reason, a layout generation and its evaluation are often challenging and time consuming due to their inherent multiple objectives in nature and their data collection process. In addition, an effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g., flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, and material handling vehicle utilization in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating FLD. The criteria that are to be minimized are viewed as inputs whereas the criteria to be maximized are considered as outputs. A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data by using exact and vague data by means of fuzzy set theory. Analytic hierarchy process (AHP) is then applied to collect qualitative data related to quality and flexibility. The DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set of a case study, which consists of 19 FLDs provided of the plastic profile production system.

DATA ENVELOPMENT ANALYSIS BASED DECISION MODEL FOR OPTIMAL OPERATOR ALLOCATION IN CMS

This paper presents a decision making approach based on data envelopment analysis (DEA) for determining the most efficient number of operators and the efficient measurement of labor assignment in cellular manufacturing system (CMS). The DEA approach is performed by employing the average lead time, the average operator utilization as the output variables and using the number of operators, transfer batch size, demand level as the input variables. Both inputs and outputs are procured by means of simulation of CMS. The objective is to determine the labor assignment in CMS environment.

Determining the importance weights for the design requirements in the house of quality using the fuzzy analytic network approach: The Fuzzy Analytic Network Approach

Quality function deployment (QFD) has been used to translate customer needs (CNs) and wants into technical design requirements (DRs) in order to increase customer satisfaction. QFD uses the house of quality (HOQ), which is a matrix providing a conceptual map for the design process, as a construct for understanding CNs and establishing priorities of DRs to satisfy them. This article uses the analytic network process (ANP), the general form of the analytic hierarchy process (AHP), to prioritize DRs by taking into account the degree of the interdependence between the CNs and DRs and the inner dependence among them. In addition, because human judgment on the importance of requirements is always imprecise and vague, this work concentrates on a fuzzy ANP approach in which triangular fuzzy numbers are used to improve the quality of the responsiveness to CNs and DRs. A numerical example is presented to show the proposed methodology.

An approach based on ANFIS input selection and modeling for supplier selection problem

Supplier selection is a key task for firms, enabling them to achieve the objectives of a supply chain. Selecting a supplier is based on multiple conflicting factors, such as quality and cost, which are represented by a multi-criteria description of the problem. In this article, a new approach based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented to overcome the supplier selection problem. First, criteria that are determined for the problem are reduced by applying ANFIS input selection method. Then, the ANFIS structure is built using data related to selected criteria and the output of the problem. The proposed method is illustrated by a case study in a textile firm. Finally, results obtained from the ANFIS approach we developed are compared with the results of the multiple regression method, demonstrating that the ANFIS method performed well.

Evaluation of renewable energy alternatives using MACBETH and fuzzy AHP multicriteria methods: the case of Turkey

Energy is a critical foundation for economic growth and social progress. It is estimated that 70% of the world energy consumption could be provided from renewable resources by the year 2050. Renewable energy is the inevitable choice for sustainable economic growth, for the harmonious coexistence of human and environment as well as for the sustainable development. The aim of this paper is to evaluate the renewable energy alternatives as a key way for resolving the Turkeys energy-related challenges because of the fact that Turkeys energy consumption has risen dramatically over the past three decades as a consequence of economic and social development. In order to realize this aim, we comparatively use MACBETH and AHP-based multicriteria methods for the evaluation of renewable energy alternatives under fuzziness. We use 4 main attributes and 15 sub-attributes in the evaluation. The potential renewable energy alternatives in Turkey are determined as Solar, Wind, Hydropower, and Geothermal.

A mathematical model and a heuristic approach for design of the hybrid manufacturing systems to facilitate one-piece flow

One-piece flow is a design rule that entails production in manufacturing cells on a ‘make one, check one, and move-on one’ basis (Black, J.T., 2007. Design rules for implementing Toyota Production System. International Journal of Production Research, 45 (16), 3639–3664), which reduces manufacturing lead time significantly. This paper proposes a sequential methodology comprised of a mathematical model and a heuristic approach (HA) for the design of a hybrid cellular manufacturing system (HMS), to facilitate one-piece flow practice. The mathematical model is employed in the cases of small- and medium-sized problems, and it attempts to minimise the total number of exceptional operations, while considering machine capacities and alternative machines. The machine-part matrix achieved by the mathematical model is input into the flow line design stage of the HA, where backflow within the cells is eliminated. However, for industrial problems, the proposed HA is utilised. After the formation of the cells by clustering, the HA attempts to eliminate exceptional operations of a given cellular configuration together with a functional structure by employing alternative machines, based on the decision rules developed. Later, unidirectional flow within the cells is achieved and the capacity and budget constraints are satisfied. A medium-sized problem is solved by using both of the approaches, namely, the model integrated with the flow-line design stage of the HA and the complete HA. The results are discussed and the limitations are explained.

Evaluation of design requirements using fuzzy outranking methods

To increase customer satisfaction, quality function deployment is used to translate customer needs into technical design requirements (DRs). Determination of DRs for product development is very important because these requirements are the vital keys to successful products. The methods used to evaluate DRs in the literature can be categorized into multicriteria evaluation methods such as scoring methods, the analytic hierarchy method, analytic network process, and so forth. There are few papers using fuzzy multi‐attribute outranking methods to evaluate DRs. This article aims to compare the results of three different fuzzy outranking methods to evaluate the DRs in the PVC windows industry. A sensitivity analysis is also made by using the software, FOuR.

AN INTEGRATED FUZZY APPROACH FOR DETERMINING ENGINEERING CHARACTERISTICS IN CONCRETE INDUSTRY

This paper deals with the modeling of conceptual knowledge to capture the major customer requirements effectively and to transform these requirements systematically into the relevant design requirements. Quality Function Deployment (QFD) is a well-known planning and problem-solving tool for translating customer needs (CNs) into the engineering characteristics (ECs) and can be employed for this modeling. In this study, an integrated methodology is presented to rank ECs for implementing QFD in a fuzzy environment. The proposed methodology uses fuzzy weighted average method as a fuzzy group decision making approach to fuse multiple preference rankings for determining the weights of the customer needs. It adopts a fuzzy Analytic Network Process (ANP) approach which enables the consideration of inner dependencies in a cluster as well as the interdependencies between the clusters to determine the importance of ECs. The proposed approach is illustrated through a case study in ready-mixed concrete industry.

System parameter selection with information axiom for the new product introduction to the hybrid manufacturing systems under dual-resource constraint

New part introduction is an important topic because customer expectations expanded and a large variety of parts must be produced by the production systems. Therefore, effect of new parts to the system performance at the operational level should be analysed in order to verify that the new parts can be processed within the cells using the current capacity of machines and workers or to identify if there will be any inter-cell flow due to new parts. In the current study, a methodology that incorporates both simulation methodology and Information Axiom of Axiomatic Design (AD) to identify the best system parameter levels is developed. The structural properties of a manufacturing system will be investigated with simulation technique to observe system performance metrics. Then based on observations, information content of each scenario is calculated and the best scenario with the most suitable parameter levels is selected. To indicate applicability of the methodology, a real hybrid manufacturing system where new parts are introduced is modelled for different levels of system parameters including different setup levels, numbers of workers and lot size, using the simulation technique and different scenarios are populated. The performance of the system for each scenario is observed using the simulation models. Then, based on the Information Axiom of Axiomatic Design total information content is calculated for each scenario, the best scenario that represents the most suitable system parameters is determined. Integration of simulation and Information Axiom of AD makes a distinction from other new product introduction studies.