Yung-Chia Chang

Machine scheduling with job delivery coordination

Abstract This paper considers together machine scheduling and finished product delivery. In particular, it addresses the situation in which jobs require different amounts of storage space during delivery. Three scenarios of the problem are discussed. For the problem in which jobs are processed on a single machine and delivered by a single vehicle to one customer area, we provide a proof of NP-hardness and a heuristic with worst-case analysis. The worst-case performance ratio for our heuristic is proven to be 5/3, and the bound is tight. For the problem in which jobs are processed by either one of two parallel machines and delivered by a single vehicle to one customer area, our heuristic could cause at most 100% error under the worst-case with the bound being tight. For the problem that considers jobs to be processed by a single machine and delivered by a single vehicle to two customer areas, we provide another heuristic that is 100% error bound.

Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique

Most of the current failure mode, effects, and criticality analysis (FMECA) methods use the risk priority number (RPN) value to evaluate the risk of failure. However, the traditional RPN methodology has been criticized to have several shortcomings. These shortcomings are addressed in this paper. Therefore, an efficient and simplified algorithm to evaluate the risk of failure is needed. This paper proposes a new approach, which utilizes the intuitionistic fuzzy set ranking technique for reprioritization of failures in a system FMECA. The proposed approach has two major advantages: (1) it resolves some of the shortcomings of the traditional RPN method, and (2) it provides an evaluation of the redundancy place, which can assist the designer in making correct decisions to make a safer and more reliable product design. In numerical verification, an FMECA of a silane supply system is presented as a numerical example. After comparing results from the proposed method and two other approaches, this research found that the proposed approach can reduce more duplicate RPN numbers and get a more accurate, reasonable risk ranking.

ME-OWA based DEMATEL reliability apportionment method

The maximal entropy ordered weighted averaging (ME-OWA)-based decision making trial and evaluation laboratory (DEMATEL) method for reliability allocation has been examined. The assessment results show that most conventional reliability allocation methods have five fundamental problems. The first problem is the measurement scale; while the second problem is that the system allocation factors are not equally weighted to one another, the third problem is that most reliability allocations methods often neglect many important features, such as maintainability and risk issues. The fourth problem is that they do not consider indirect relations between subsystems or components, and the fifth problem is that they do not consider predicted failure rate in the apportionment process. This study evaluated reliability allocation using a fighter aircrafts digital flight control computer (DFLCC). The proposed method offers several benefits compared with current military and commercial approaches. The computational results clearly demonstrate the advantages of the proposed approach for solving the five fundamental problems.

Innovative reliability allocation using the maximal entropy ordered weighted averaging method

Reliability allocation is one of the most important factors to consider when determining the reliability and competitiveness of a product. The feasibility-of-objectives (FOO) technique has become the current standard for assessing reliability designs for military mechanical-electrical systems, whereas the average weighting allocation method is widely used for commercial applications. However, assessment results are biased because these methods share two fundamental problems. The first problem is the measurement scale, while the second problem is that the system allocation factors are not equally weighted to one another. Both problems represent serious flaws from a technical perspective. To address these issues, we propose the use of the maximal entropy ordered weighted averaging (ME-OWA) method, which efficiently resolves the shortcomings of the FOO technique and the average weighting allocation method. As a comparative case study between the ME-OWA method and the two standards used in the military and commercially, this study evaluates reliability allocation in the context of a fighter aircraft airborne radar system. The results from this comparison show that the proposed method is both accurate and flexible.

Reliability assessment of an aircraft propulsion system using IFS and OWA tree

In conventional system reliability analysis, the failure probabilities of components of a system are treated as exact values when the failure probability of the entire system is estimated. However, it may be difficult or even impossible to precisely determine the failure probabilities of components as early as the product design phase. Therefore, an efficient and simplified algorithm to assess system reliability is needed. This article proposes a deductive top-down estimation methodology, which combines intuitionistic fuzzy set (IFS) and ordered weighted averaging (OWA) operators to evaluate system reliability. A case of an aircraft propulsion system from an aerospace company is presented to further illustrate the proposed approach. After comparing results from the proposed method and two other approaches, this research found that the proposed approach provides a more accurate and reasonable reliability assessment.

Determining the optimal re-sampling strategy for a classification model with imbalanced data using design of experiments and response surface methodologies

Imbalanced data are common in many machine learning applications. In an imbalanced data set, the number of instances in at least one class is significantly higher or lower than that in other classes. Consequently, when classification models with imbalanced data are developed, most classifiers are subjected to an unequal number of instances in each class, thus failing to construct an effective model. Balancing sample sizes for various classes using a re-sampling strategy is a conventional means of enhancing the effectiveness of a classification model for imbalanced data. Despite numerous attempts to determine the appropriate re-sampling proportion in each class by using a trial-and-error method in order to construct a classification model with imbalanced data (Barandela, Vadovinos, Sanchez, & Ferri, 2004; He, Han, & Wang, 2005; Japkowicz, 2000; McCarthy, Zabar, & Weiss, 2005), the optimal strategy for each class may be infeasible when using such a method. Therefore, this work proposes a novel analytical procedure to determine the optimal re-sampling strategy based on design of experiments (DOE) and response surface methodologies (RSM). The proposed procedure, S-RSM, can be utilized by any classifier. Also, C4.5 algorithm is adopted for illustration. The classification results are evaluated by using the area under the receiver operating characteristic curve (AUC) as a performance measure. Among the several desirable features of the AUC index include independence of the decision threshold and invariance to a priori class probabilities. Furthermore, five real world data sets demonstrate that the higher AUC score of the classification model based on the training data obtained from the S-RSM is than that obtained using oversampling approach or undersampling approach.

An ant colony optimization heuristic for an integrated production and distribution scheduling problem

Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.

Applying the concept of exponential approach to enhance the assessment capability of FMEA

Failure modes and effects analysis (FMEA) has been used to identify the critical risk events and predict a system failure to avoid or reduce the potential failure modes and their effect on operations. The risk priority number (RPN) is the classical method to evaluate the risk of failure in conventional FMEA. RPN, which ranges from 1 to 1000, is a mathematical product of three parameters—severity (

Applied column generation-based approach to solve supply chain scheduling problems

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