Chiuh-Cheng Chyu

Optimization of robust design for multiple quality characteristics

The Taguchi method has recently been widely applied to variability reduction for increased quality and lower cost in many different industries. The traditional Taguchi method was focused on optimizing a single quality characteristic. A real problem in a product or process possesses multiple quality characteristics. The optimization methods of multiple quality characteristics design have thus become crucial issues for industry. Several articles have presented approaches to optimizing the parameter design with multiple quality characteristics. Few have focused primarily on optimizing the correlated multiple quality characteristics problem. This research presents an approach to optimizing the correlated multiple quality characteristics with asymmetric loss function by a mathematical programming model. The goal is minimizing the total average quality loss for experiments. This proposed procedure is illustrated with data from nine previously published articles. A numerical analysis of the model is provided and the results are compared with those of prior approaches.

Optimization of correlated multiple quality characteristics robust design using principal component analysis

The use of the Taguchi method for improving the design and quality of products and processes has become widespread among different industries. The traditional Taguchi method focused on one characteristic to optimize a combination of parameter conditions. In practice, most products have more than one quality characteristic. The methods of multiple quality characteristics design have become very important for industries. Several studies have presented approaches addressing multiple quality characteristics. Few published articles have focused primarily on optimizing correlated multiple quality characteristics. This research presents an approach to optimizing correlated multiple quality characteristics by using proportion of quality loss reduction and principal component analysis. The results reveal the advantages of this approach in that the optimal parameter design using proportion of quality loss reduction is the same as that using the Taguchi traditional method for one quality characteristic; the chosen optimal design is robust for optimizing correlated multiple quality characteristics.

Application of a Hybrid Ant Colony Optimization for the Multilevel Thresholding in Image Processing

Our study proposes a hybrid optimization scheme based on an ant colony optimization algorithm with the Otsu method to render the optimal thresholding technique more applicable and effective. The properties of discriminate analysis in Otsu’s method are to analyze the separability among the gray levels in the image. The ACO-Otsu algorithm, a non-parametric and unsupervised method, is the first-known application of ACO to automatic threshold selection for image segmentation. The experimental results show that the ACO-Otsu efficiently speed up the Otsu’s method to a great extent at multi-level thresholding, and that such method can provide better effectiveness at population size of 20 for all given image types at multi-level thresholding in this study.

A memetic algorithm for maximizing net present value in resource-constrained project scheduling problem

In this study, we develop a model that considers monetary issues in resource-constrained environments, and involves scheduling project activities to maximize net present value. This problem is recognized as the ldquoresource-constrained project scheduling problem with discounted cash flows (RCPSPDCF),rdquo. which is strongly NP-hard. All resources considered are both types of renewable and nonrenewable; the duration of each activity depends on the amount of resources allocated to its execution. Efforts are made by considering a two-stage method applying mode selection rules at the first stage and the memetic algorithm at the second stage. Results are shown in a comparative study which demonstrates the effectiveness of using memetic algorithm in maximizing project net present value; as well as, a combination of mode selection rules which provide a high probability of giving the best solution.

A Hybrid Fuzzy Analytic Network Process Approach to the New Product Development Selection Problem

New product development selection is a complex decision-making process. To uphold their competence in competitive business environments, enterprises are required to continuously introduce novel products into markets. This paper presents a fuzzy analytic network process (FANP) for solving the product development selection problem. The fuzzy set theory is adopted to represent ambiguities and vagueness involved in each expert’s judgment. In the proposed model, the fuzzy Kano method and fuzzy DEMATEL are employed to filter criteria and establish interactions among the criteria, whereas the SAM is applied to aggregate experts’ opinions. Unlike the commonly used top-down relation-structuring approach, the proposed FANP first identifies the interdependence among the criteria and then the identified relationships are mapped to the clusters. This approach is more realistic, since the inner and outer relationships between criteria are simultaneously considered to establish the relationships among clusters. The proposed model is illustrated through a real life example, with a comparative analysis using modified TOPSIS and gray relation analysis in the synthesizing phase. The concluded results were approved by the case company. The proposed methodology not only is useful in the case study, but also can be generally applied in other similar decision situations.

A minimum setup strategy for sequencing PCBs with multi‐slot feeders

This paper considers the decision problem for a minimum setup strategy of a production system arising in the assembly of printed circuit boards of different types, using a placement machine with multi‐slot feeders. We formulate the problem as a binary linear programming model, and propose a heuristic procedure to find the solution that consists of a board‐assembly sequence, an associated component loading and unloading strategy and a feeder‐assignment plan within reasonable computational effort. Computational results from solving the simulated problem instances by using the heuristic method and the mathematical model are provided and compared. The proposed heuristic procedure can be incorporated into the PCB scheduling optimization software to decrease cycle times and increase overall assembly throughput in a high‐mix, low‐volume PCB manufacturing environment.

Reduction of printed circuit board group assembly time through the consideration of efficiency loss of placement time

In a high‐mix middle‐volume production environment for printed circuit board (PCB) assembly, the production efficiency strongly depends not only on the tactical level of how to group PCBs but also on the operational level of how to assign feeders and determine placement sequences in the group setup strategy. The present study discusses the problem of clustering PCBs into groups in such a way that total placement and setup time can be minimized. This problem is motivated by a situation that the reduction of group setup‐time and efficiency loss of placement time should be balanced in a PCB group setup optimization. This research incorporates placement time into the PCB job grouping and presents a weighting similarity measure. To solve component‐feeder assignment and placement sequences for a family of PCBs, an efficient procedure based upon an ant colony optimization (ACO) algorithm is developed. Group setup performance is evaluated and compared under a variety of grouping algorithms. Experiments are conducted to discover situations where the consideration of efficiency loss of placement time can make a significant improvement in PCB group assembly.

An Evolutionary Algorithm with Multi-Local Search for the Resource-Constrained Project Scheduling Problem

This paper introduces a hybrid evolutionary algorithm for the resource-constrained project scheduling problem (RCPSP). Given an RCPSP instance, the algorithm identifies the problem structure and selects a suitable decoding scheme. Then a multi-pass biased sampling method followed up by a multi-local search is used to generate a diverse and good quality initial population. The population then evolves through modified order-based recombination and mutation operators to perform exploration for promising solutions within the entire region. Mutation is performed only if the current population has converged or the produced offspring by recombination operator is too similar to one of his parents. Finally the algorithm performs an intensified local search on the best solution found in the evolutionary stage. Computational experiments using standard instances indicate that the proposed algorithm works well in both computational time and solution quality.

Evaluation of New Product Development Alternatives Considering Interrelationships among Decision Criteria

In electronic industry, technologies are progressing rapidly nowadays. To maintain market competition with comparative advantages, an enterprise must continuously develop various new products. This research focuses on the initial stages of the new product development (NPD), which involves generating and screening NPD alternatives. A multiple criteria decision making (MCDM) model considering interrelations among selection criteria is developed. The proposed MCDM model employs the fuzzy Delphi method to filter the performance evaluation criteria. Since the criteria are considered to be interdependent by decision-makers, the gray relation analysis (GRA) is applied to identify the interactive relationships among criteria within each aspect. Two methods are used to calculate the synthetic utility score for each alternative. The first method evaluates the alternatives using an ANP model with relation-structure derived from GRA, whereas the second method rates the alternatives using non-additive fuzzy integral. An empirical example of the medical display monitor industry is provided to show the feasibility and effectiveness of the proposed model. The two evaluation methods achieve the same ranking of the alternatives.

A competitive evolution strategy memetic algorithm for unrelated parallel machine scheduling to minimize total weighted tardiness and flow time

This research proposes a competitive evolution strategy memetic algorithm (CESMA) to solve unrelated parallel machines scheduling problems with two minimization objectives subject to job sequence- and machine-dependent setup times. A memetic operation is regarded as a genetic operation following a local search-weighted bipartite matching algorithm (WBM). The competitive evolution strategy maintains one generational population (GP) and two external archives at each generation, one preserving efficient solutions and the other preserving inefficient solutions. At each generation, two procedures, EAMA (efficient archive memetic algorithm) and IAMA (inefficient archive memetic algorithm), are applied to compete for producing the next generation offspring. The fraction p of memetic operations assigned to EAMA varies at each generation and depends on the competition results of the last generation. An experiment is conducted to compare the performance of the CESMA against two well-known evolutionary algorithms (NSGA II and SPEA2) with WBM. The effects of incorporating the WBM into these algorithms are also investigated. In the experimental study, three instances of different problem parameters were generated using a method in the literature. The experimental results show that the CESMA excels the others in terms of several proximity measures