https://ira.lib.polyu.edu.hk/bitstream/10397/1269/1/SetupSurvey-2006%28June-19%29.pdf

A survey of scheduling problems with setup times or costs

http://yoksis.bilkent.edu.tr/doi_getpdf/articles/10.1016-j.ejor.2007.06.008.pdf

Network hub location problems : The state of the art

Integer programming formulations of discrete hub location problems

http://www.intelligence.tuc.gr/~petrakis/publications/mape02.pdf

A survey on industrial vision systems, applications and tools

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Proposing a novel framework for evaluating teaching performance based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method.Determining the factors and sub-factors in the evaluation index system, and then calculating the factor and sub-factor weights by the extent analysis fuzzy AHP method.On the basis of the constructed system, evaluating teaching performance can be conducted by the fuzzy comprehensive evaluation method.The approach can provide an effective, reasonable and accurate results of the evaluation. Evaluating teaching performance is a main means to improve teaching quality and can plays an important role in strengthening the management of higher education institutions. In this paper, we present a novel framework for teaching performance evaluation based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method. Specifically, after determining the factors and sub-factors, the teaching performance index system was established. In the index system, the factor and sub-factor weights were then estimated by the extent analysis fuzzy AHP method. Employing the fuzzy AHP method in group decision-making can facilitate a consensus of decision-makers and reduce uncertainty. On the basis of the system, the fuzzy comprehensive evaluation method was employed to evaluate teaching performance. A case application was also used to illustrate the proposed framework. The application of this framework can make the evaluation results more scientific, accurate, and objective. It is expected that this work may serve as an assistance tool for managers of higher education institutions in improving the educational quality level.

Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach

The uncapacitated single allocation hub location problem (USAHLP), with the hub-and-spoke network structure, is a decision problem in regard to the number of hubs and location–allocation. In a pure hub-and-spoke network, all hubs, which act as switching points for internodal flows, are interconnected and none of the non-hubs (i.e., spokes) are directly connected. The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs. In this paper two approaches to determine the upper bound for the number of hubs along with a hybrid heuristic based on the simulated annealing method, tabu list, and improvement procedures are proposed to resolve the USAHLP. Computational experiences indicate that by applying the derived upper bound for the number of hubs the proposed heuristic is capable of obtaining optimal solutions for all small-scaled problems very efficiently. Computational results also demonstrate that the proposed hybrid heuristic outperforms a genetic algorithm and a simulated annealing method in solving USAHLP.

A hybrid heuristic for the uncapacitated single allocation hub location problem

The evaluation of normalized cross correlations for defect detection

This research deals with scheduling jobs on unrelated parallel machines with auxiliary equipment constraints Each job has a due date and requires a single operation A setup for dies is incurred if there is a switch from processing one type of job to another type For a die type, the number of dies is limited Due to the attributes of the machines and the fitness of dies to each, the processing time for a job depends on the machine on which the job is processed, each job being restricted to processing on certain machines In this paper, an effective heuristic based on threshold-accepting methods, tabu lists, and improvement procedures is proposed to minimize total tardiness An extensive experiment is conducted to evaluate the computational characteristics of the proposed heuristic Computational experiences demonstrate that the proposed heuristic is capable of obtaining optimal solutions for small-sized problems, and significantly outperforms an ATCS procedure and a simulated annealing method for problems in larger sizes

Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints

In this study we use machine vision to assess surface roughness of machined parts produced by the shaping and milling processes. Machine vision allows for the assessment of surface roughness without touching or scratching the surface, and provides the flexibility for inspecting parts without fixing them in a precise position. The quantitative measures of surface roughness are extracted in the spatial frequency domain using a two-dimensional Fourier transform. Two artificial neural networks, which take roughness features as the input, are developed to determine the surface roughness. The first network is for test parts placed in a fixed orientation, which minimises the deviation of roughness measures. The second network is for test parts placed in random orientations, which gives maximum flexibility for inspection tasks. Experimental results have shown that the proposed roughness features and neural networks are efficient and effective for automated classification of surface roughness.

Jeng-Fung Chen

Papers

Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach

A hybrid heuristic for the uncapacitated single allocation hub location problem

The evaluation of normalized cross correlations for defect detection

Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints

A vision system for surface roughness assessment using neural networks