Ching-Chin Chern

Family-based scheduling rules of a sequence-dependent wafer fabrication system

Consider the dispatch problem for a wafer fabrication where production process is divided into hundreds of operations and takes a few months to complete. In the process, wafers have to go through similar operations several times repeatedly for different layers of circuit, called job re-entrances. General family-based scheduling rules are proved to perform better than the individual job scheduling rule in terms of machine utilization for multiserver and multiple job re-entrance manufacturing systems under the condition that with a positive possibility, a queue exists in front of steppers. Five special family-based scheduling rules are constructed, of which FCFS-F, SRPT-F, EDD-F, and LS-F are modified from previous well-known scheduling rules, while SDA-F is a rule-based algorithm, using threshold control and least slack principles. A simulation model is built to evaluate the performances of these five family-based rules by using the information collected from a wafer fab located in Hsin-Chu, Taiwan. As a result, SDA-F is shown to perform best among all five rules, followed by LS-F and FCFS-F.

A heuristic algorithm for the hospital health examination scheduling problem

Abstract This study considers the problem of health examination scheduling. Depending on their gender, age, and special requirements, health examinees select one of the health examination packages offered by a health examination center. The health examination center must schedule all the examinees, working to minimize examinee/doctor waiting time and respect time and resource constraints, while also taking other limitations, such as the sequence and continuity of the examination procedures, into consideration. The Binary integer programming (BIP) model is one popular way to solve this health examination scheduling problem. However, as the number of examinees and health examination procedures increase, solving BIP models becomes more and more difficult, if not impossible. This study proposes health examination scheduling algorithm (HESA), a heuristic algorithm designed to solve the health examination scheduling problem efficiently and effectively. HESA has two primary objectives: minimizing examinee waiting time and minimizing doctor waiting time. To minimize examinee waiting time, HESA schedules the various parts of each examinee’s checkup for times when the examinee is available, taking the sequence of the examination procedures and the availability of the resources required into account. To minimize doctor waiting time, HESA focuses on doctors instead of examinees, assigning waiting examinees to a doctor as soon as one becomes available. Both complexity analysis and computational analyses have shown that HESA is very efficient in solving the health examination scheduling problem. In addition to the theoretical results, the results of HESA’s application to the concrete health examination scheduling problems of two large hospitals in Taiwan are also reported.

Using fuzzy super-efficiency slack-based measure data envelopment analysis to evaluate Taiwan's commercial bank efficiency

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Therefore, we propose the use of a fuzzy super-efficiency slack-based measure DEA to analyze the operational performance of 24 commercial banks facing problems on loan and investment parameters with vague characteristics. After our analysis, we find that the fuzzy slack-based measure of efficiency (Fuzzy SBM)/fuzzy super-efficiency slack-based measure of efficiency (Fuzzy Super SBM) can not only effectively characterize uncertainty, but also have a higher capability to evaluate bank efficiency than the conventional Fuzzy DEA approach.

Determining a threshold control policy for an imperfect production system with rework jobs

Consider a threshold control policy for an imperfect production system with only a work center handling both regular and rework jobs. An imperfect production system studied here, generates defect jobs by factors other than machine failures. A threshold control or (ω, s) policy sets the guideline for a work center to switch between regular and rework jobs. A production cycle begins with loading and processing of several batches of regular jobs with a lot size equal to s. The outcome of each completed regular job is an independent Bernoulli trial with three possibilities: good, rework, or scrap. Once the work center accumulates more than a threshold ω of rework jobs, it finishes the last batch of regular jobs and switches to rework jobs. The objective of this research is to find a threshold ω and a lot size s that maximize the average long-term profit. The ultimate goal is to construct a simple algorithm to search for ω and s that can be implemented directly in production management systems, as a result of this work.

Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network

This study focuses on solving the multi-objective master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycled components. This study considers five objectives in the planning process: (1) minimizing the delay cost, (2) minimizing the substitution priority, (3) minimizing the recycling penalty, (4) minimizing the substitution cost, and (5) minimizing the cost of production, processing, inventory holding and transportation. This study proposes a heuristic algorithm, called the GA-based Master Planning Algorithm (GAMPA), to solve the supply-chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain that plans and searches the sub-networks for each final product. GAMPA then uses a genetic algorithm to sort and sequence the demands. GAMPA selects the chromosome that generates the best planning result according to the priority of the objectives. GAMPA plans each demand sequentially according to the selected chromosome and a randomly-selected production tree. GAMPA tries different production trees for each demand and selects the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested using complexity analysis and computational analysis to demonstrate the power of GAMPA.

Performance evaluation with the entropy-based weighted Russell measure in data envelopment analysis

Conventional Data envelopment analysis is based on the Debreu-Farrell optimal solution in evaluating the decision-making units efficiency. Even if Farrell efficiency is achieved, there may exist slacks in individual input or output. To solve this problem, the Russell measure can be used to address the inherent shortcomings of the Farrell measure and devise an optimal solution for the Pareto-Koopmans concept of efficiency. However, the non-proportional radial measure may lead to distorted efficiency measurement of inefficient decision-making units, due to assumptions about its implicit importance of inputs and outputs. Therefore, this paper uses a simple method in calculating the weighting measurements, in order to override this assumption using the concept of entropy. By introducing entropy, the Russell measure easily uses input, output, and system weighting to evaluate performance. In addition, we also use the entropy-concepts applied to slack-based measure. Moreover, we illustrate this entropy-based Russell measure using data gathered from 24 of Taiwans commercial banks in order to rank and compare it with the conventional Russell measure.

A heuristic relief transportation planning algorithm for emergency supply chain management

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPAs effectiveness and efficiency. This algorithms power was demonstrated using computational and complexity analysis.

Affective mechanisms linking Internet use to learning performance in high school students: A moderated mediation study

Although previous studies have concluded that Internet use can help students in learning and research, a number of empirical investigations have confirmed that Internet addiction or excessive Internet use has negative effect on students. Thus, if the Internet does not always benefit students, under which conditions can Internet use have positive effects? Since students’ beliefs in their academic self-efficacy and their abilities to begin, continue, and complete their studies are as important as their academic successes and performances, this study hypothesizes that academic self-efficacy acts as a mediator for Internet use and academic performance. Based on Social cognitive theory, we argue that student academic performance will be mediated by academic self-efficacy with respect to Internet use. Two kinds of Internet use, general and professional, are considered to be antecedents of academic self-efficacy. Survey data from 212 twelfth-grade vocational high school students in Taiwan indicate that general Internet use has an indirect positive effect on student academic performance, which is also mediated through academic self-efficacy. In contrast, general Internet use has no significant direct impact on students learning performance. This study also shows that Internet anxiety moderates the relationship between academic self-efficacy and learning performance. In students with low Internet anxiety, the relationship is moderated, which results in enhanced learning performance.

Measurement of analytical knowledge-based corporate memory and its application

In the current knowledge-driven economy, businesses are increasingly required to function as knowledge-based organizations. In these organizations, knowledge usually serves as the means for attainment of competitive advantage. It is clear that organizational knowledge has to be carefully managed, and knowledge management measurement is important to businesses. In this paper, corporate memory (CM) is viewed as an organization memory for managing knowledge. Generically and concretely, CM is constructed using analytical knowledge (AK), which is defined as the knowledge formatted with 5W1H (who, when, where, what, why, and how). AK is extracted from data storage systems and domain experts by aggregating information, where data analysts, knowledge workers, and knowledge users are involved in a knowledge discovery process. The research objective of this study is to propose a measurement approach, which provides a generic and applicable methodology for measuring the performance and quality of CM. To represent the uncertainty and fuzzy terms in the evaluation environments, and to explicate the invisible impact induced by information technology (IT), the fuzzy set theory is applied. An effective procedure is also proposed to apply the measurement approach in practice. Highlights? A generic and applicable approach for measuring the performance and quality of CM ? The fuzzy set theory and the S-shaped logistic model are employed. ? A definite and effective procedure is able to support the measurement in practice. ? The method is able to manage knowledge and then develop strategies in businesses. ? The method facilitates the development of strategies for intellectual capital.