Shu-Chen Cheng

Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm

The proportionate flow shop (PFS) is considered as a unique case of the flow shop problem in which the processing times of the operations belonging to the same job are equal. A proportionate flexible flow shop (PFFS) is a machine environment with parallel identical machines at each stage. This study presents an effective hybrid approach based on constructive genetic algorithm (CGA) for PFFS scheduling with the criterion to minimize the total weighted completion time (WCT). Minimizing the WCT in a PFFS problem significantly differs from the parallel-identical-machine scheduling problem, an optimal schedule in which the jobs on each machine are in weighted shortest processing time (WSPT) order. The proposed approach incorporates two fitness functions, and a population trained by a local improvement search based on tabu search with a candidate list strategy into CGA. Simulation results are compared with those of the column generation (CG) approach to demonstrate the effectiveness of the proposed hybrid approach. In particular, the CG approach has been applied successfully to solve various parallel machine scheduling problems, and yields high-quality solutions.

Automatic and interactive e-Learning auxiliary material generation utilizing particle swarm optimization

The purpose of this research was to utilize a PSO-based algorithm, serial blog article composition particle swarm optimization (SBACPSO) algorithm, to automatically and intelligently generate auxiliary materials. Contrary to previous fixed content auxiliary materials, the proposed auxiliary materials, which consist of blogs posted by learners, provide more interactive and cooperative characteristics for the learning process. With a few blog features such as comments, trackbacks, difficulty levels, and association degree related to a specific topic, the best combination of blog articles is produced as an auxiliary material. The generated auxiliary materials from a real course are presented in a system demonstration. The experimental results and satisfaction analysis also indicate that the proposed algorithm can achieve the expected convergence, with participants being satisfied with interaction, assistance, usability, and flexibility.

Dynamic hard-real-time scheduling using genetic algorithm for multiprocessor task with resource and timing constraints

Most publications in shop scheduling area focus on the static scheduling problems and seldom take into account the dynamic disturbances such as machine breakdown or new job arrivals. Motivated by the computational complexity of the scheduling problems, genetic algorithms (GAs) have been applied to improve both the efficiency and the effectiveness for NP-hard optimization problems. However, a pure GA-based approach tends to generate illegal schedules due to the crossover and the mutation operators. It is often the case that the gene expression or the genetic operators need to be specially tailored to fit the problem domain or some other schemes may be combined to solve the scheduling problems. This study presents a GA-based approach combined with a feasible energy function for multiprocessor scheduling problems with resource and timing constraints in dynamic real-time scheduling. Moreover, an easy-understood genotype is designed to generate legal schedules. The results of the experiments demonstrate that the proposed approach performs rapid convergence to address its applicability and generate good-quality schedules.

The Experience of Adopting Game-Based Learning in Library Instruction

The research is based on the course of library instruction and also a pilot study before adopting game-based learning into real library instruction course. The difficulty of the research is how we can make the original course like a game but with educational function. And another challenge is how to create a game which is executing in the silent library. After the trial experiment we have some findings and suggestions. These data will be useful to the further researches and helpful to make this educational game becoming a real course in the school.

An Automatic Course Generation System for Organizing Existent Learning Objects Using Particle Swarm Optimization

The purpose of this study is to consider that how to help instructors re-organize a well-structured teaching material utilizing existing learning objects. To cope with this problem, this study adopts a novel approach - particle swarm optimization (PSO) to achieve this aim. By employing this algorithm, the proposed approach can automatically re-organize a teaching material and meet multiple criteria specified by the instructors. In addition, evaluation results have demonstrated that the proposed approach is applicable that can help most instructors re-organize teaching materials efficiently and effectively.