A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search

Rezaeipanah, Amin; Matoori, Samaneh Sechin; Ahmadi, Gholamreza

doi:10.1007/s10489-020-01833-x

Cited by 50 publications

(31 citation statements)

References 71 publications

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“…Knoben and Oerlemans [6] extended the seven dimensions of cognition, culture, technology, and social proximity. ese author analyze the relevant theoretical and empirical research results of Western scholars and focus on the role of geographical proximity, technology proximity, and institutional proximity of innovation subjects in green innovation performance [7][8][9][10].…”

Section: Multidimensional Proximitymentioning

confidence: 99%

Big Data-IoT: An Analysis of Multidimensional Proximity Implications on Green Innovation Performance—An Empirical Study of the Data from the Chinese Power Industry

Xiao

Jian-bang

Luo

et al. 2021

Mobile Information Systems

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With the global energy crisis and environmental degradation getting more rigorous, an essential approach is required to attain introverted development through structural optimization, autonomous invention, and technological innovation system. It is an important way to include energy conservation, emission reductions, and the implementation of a low-carbon mode in the power industry, which is a highly polluting sector of the national economy. This article is based on the State Intellectual Property Office of China’s patent search and analysis database. We choose the number of green patents jointly submitted for innovative topics in the power industry from 2016 to 2020. The negative binomial regression is constructed from the standpoint of multidimensional closeness by employing Gephi visual analysis, Ucinet, and the Stata 15 regional model. Furthermore, we investigate the impact of geographical closeness, technological closeness, and institutional closeness, as well as their interaction, on the green innovation performance of inventive organizations in China’s power industry. According to the findings of the study, geographical and institutional closeness have an important influence in increasing the green innovation performance. The suggested model applies to the power sector, and technological closeness has an inverted U-shaped association with green innovation performance in the power business. Furthermore, the model output at inference time is just a collection of successive parameters that improve the interaction of the closeness of innovation subjects to the green innovation performance of the power industry, all of which are represented as complementary effects.

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Section: Multidimensional Proximitymentioning

confidence: 99%

Big Data-IoT: An Analysis of Multidimensional Proximity Implications on Green Innovation Performance—An Empirical Study of the Data from the Chinese Power Industry

Xiao

Jian-bang

Luo

et al. 2021

Mobile Information Systems

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“…Scheduling or timetabling is an important problem and active area of research with applications in many fields [ 19 ], e.g., transportation [ 20 ], sport [ 21 ], healthcare (e.g., nurse rostering [ 22 ], surgery [ 23 ]), industrial production [ 8 ], employee [ 24 ], educational institutes (e.g., secondary school [ 25 ], university [ 18 ]). The university course timetabling problem (UCTP) is a classic and famous optimisation problem in the field of computer science and management science [ 26 ]. The UCTP is to find a method to allocate all course or events into the given timeslots and classrooms, whilst all pre-defined constraints must be satisfied [ 27 ].…”

Section: Course Timetabling Problemmentioning

confidence: 99%

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

2021

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Due to the COVID-19 pandemic, many universities across the globe are unexpectedly accelerated to face another major financial crisis. An effective university course timetabling has a direct effect on the utilisation of the university resources and its operating costs. The university course timetabling is classified to be a Non-deterministic Polynomial (NP)-hard problem. Constructing the optimal timetables without an intelligence timetabling tool is extremely difficult task and very time-consuming. A Hybrid Particle Swarm Optimisation-based Timetabling (HPSOT) tool has been developed for optimising the academic operating costs. In the present study, two variants of Particle Swarm Optimisation (PSO) including Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) were embedded in the HPSOT program. Five combinations of Insertion Operator (IO) and Exchange Operator (EO) were also proposed and integrated within the HPSOT program aimed at improving the performance of the proposed PSO variants. The statistical design and analysis indicated that five combination results of IO and EO for hybrid SPSO and MCPSO were significantly better than those obtained from the original PSO variants for all eleven problem instances. The average computational times taken by the proposed hybrid methods were also faster than the conventional SPSO and MCPSO for all cases.

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“…e rationality of the comprehensive evaluation results of the quality of mathematics teaching model reform can enable school leaders, administrations, and the staff of the mathematics teaching model reform quality evaluation management department. Moreover, this will also help all the teachers themselves to timely understand and discover the existence of their own mathematics teaching model reforms and make appropriate adjustments within time [11]. It has an irreplaceable effect on the development of colleges and universities, the cultivation of students, and the continuous improvement of teachers' mathematics teaching model reform.…”

Section: E Quality Evaluation System Of the Reform Of Mathematics Teachingmentioning

confidence: 99%

Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

Yang

2021

Scientific Programming

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The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.

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A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search

Cited by 50 publications

References 71 publications

Big Data-IoT: An Analysis of Multidimensional Proximity Implications on Green Innovation Performance—An Empirical Study of the Data from the Chinese Power Industry

Big Data-IoT: An Analysis of Multidimensional Proximity Implications on Green Innovation Performance—An Empirical Study of the Data from the Chinese Power Industry

Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

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