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Optimal production and maintenance scheduling for a degrading multi-failure modes single-machine production environment

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APPLIED SOFT COMPUTING 2021年 106卷 107312-107312页

作者： Sharifi, Mani Taghipour, Sharareh Ryerson Univ Mech & Ind Engn Dept Toronto ON Canada

Two important aspects of manufacturing systems are production scheduling and maintenance planning. These aspects are interdependent, but in most research work, this dependency is ignored. This paper proposed an integrated mathematical model for joint production scheduling and maintenance planning for a degrading multi-failure single-machine manufacturing system, in which the machine has discrete deterioration states. The machine is subject to different failure modes. The first failure mode is the machine's full deterioration, which is detected at the end of a job's processing, and the other failure mode is random breakdowns, which are detected at the time of failure. Two machine deterioration state-based thresholds are considered, and five different maintenance actions may carry out a replacement, preventive, and corrective perfect or imperfect maintenance. Since the machine's states' transitions follow an exponential distribution, a closed-form matrix-based mathematical model with probabilistic input parameters is presented. This paper aims to optimize the total system's cost, including the maintenance cost, machine energy consumption cost as well as the makespan penalty for exceeding a pre-determined threshold. The proposed model determines the optimal jobs' sequence as well as the machine's deterioration state-based thresholds. Due to the complexity of the developed model, a genetic algorithm (GA), simulated annealing (SA) algorithm, and a teaching-learning-based optimization (TLBO) algorithm have been used to solve the presented model. The algorithms are validated using a full enumeration technique, and the model is validated by applying different maintenance strategies. Our results demonstrate the superiority of the GA compared to the other algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Single-machine Production scheduling Maintenance planning Deterioration Multi-state failure modes Genetic algorithm Simulated annealing teaching-learning-based optimization

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Hybrid Symbiotic Organism Search algorithms for Automatic Data Clustering 4

Hybrid Symbiotic Organism Search algorithms for Automatic Da...

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4th Annual Conference on Information Communications Technology and Society (ICTAS)

作者： Rajah, Vidushen Ezugwu, Absalom E. Univ KwaZulu Natal Sch Comp Sci King Edward RdPietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

ISBN: (纸本)9781728137704;9781728137698

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been proposed and implemented for which most are able to find the good quality or optimal clustering solutions. However, most of these algorithms still depend on the number of a cluster being provided a priori. In dealing with real-life problems, the number of clusters is unknown and determining the optimal number of clusters for a large density and high dimensionality dataset is quite a difficult task to handle. This paper, therefore, proposes five new hybrid symbiotic organism search algorithms to automatically partition datasets without any prior information regarding the number of clusters. Furthermore, the hybrid algorithms will be evaluated in terms of solution quality using the Davies-Bouldin clustering validity index. The simulation results show that the performance of the hybrid symbiotic organisms search particle swarm optimization algorithm is superior to the other proposed hybrid algorithms.

关键词： Automatic clustering particle swarm optimization symbiotic organisms search algorithm firefly algorithm differential evolution teaching-learning-based optimization

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Fuzzy-based Reliability Prediction Model for Secure Routing Protocol Using GA and TLBO for Implementation of Black Hole Attacks in WSN

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2021年第6期30卷 p1-20页

作者： Nosratian, Sajad Moradkhani, Masoud Tavakoli, Mohammad Bagher Islamic Azad Univ Dept Elect Engn Arak Branch Arak Iran Islamic Azad Univ Dept Elect Engn Ilam Branch Ilam Iran

This study manifests a fuzzy-based trust prediction model for detecting malicious nodes in wireless sensor networks (WSN) by preventing black hole attack. Besides, a new routing protocol based on the shortest path and trust to the path nodes is presented with a fuzzy estimator, which uses the data mining methods to detect the malicious nodes (black holes). In a black hole attack, a malicious node selects the RREP (Route Replay) message as the shortest path from the source node to the destination node. After that, the packet sent to the malicious node is not received by the network. Eventually, the malicious node releases the entire data packet instead of sending it to the destination node. Optimal path selection is performed according to different algorithms on the objective function and its results are observed for route selection. After defining the objective function, different algorithms such as Genetic Algorithm (GA) and teaching-learning-based optimization (TLBO) are utilized for routing. For each algorithm, the objective function for the most secure node is evaluated and simulated based on the parameters defined in fuzzy logic. based on the simulation results under MATLAB software, TLBO algorithm has obtained the best response for path selection with the least cost for the target performance. Significantly, the proposed method is simple and based on the exchange of control packets between the sensor node and the base station. In accession, the results show that the proposed algorithms are effective in detecting and preventing black hole attacks.

关键词： Wireless sensor networks black hole malicious fuzzy logic genetic algorithm teaching-learning-based optimization

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Ambidextrous Socio-Cultural Algorithms 20th

Ambidextrous Socio-Cultural Algorithms

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Lemus-Romani, Jose Crawford, Broderick Soto, Ricardo Astorga, Gino Misra, Sanjay Crawford, Kathleen Foschino, Giancarla Salas-Fernandez, Agustin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Valparaiso Valparaiso Chile Covenant Univ Ota Nigeria Univ Diego Portales Santiago Chile

ISBN: (纸本)9783030588175;9783030588168

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature's behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

关键词： Social-inspired metaheuristics Socio-cultural inspired metaheuristics Human-based algorithm teaching-learning-based optimization Twitter optimization Ambidextrous metaheuristics

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An Improved Harmony Search based on teaching-learning Strategy for Unconstrained Binary Quadratic Programming

An Improved Harmony Search Based on Teaching-Learning Strate...

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第33届中国控制与决策会议

作者： Longquan Yong School of Mathematics and Computer Science Shaanxi University of Technology Shaanxi Key Laboratory of Industrial Automation

Unconstrained binary quadratic programming(UBQP) problem plays an important role in operational research due to its application potential and its computational *** paper presents a new hybrid algorithm based on Harmony Search(HS) and teaching-learning-based *** main features of the proposed algorithm called harmony search with teaching-learning(HSTL) are the integration of teaching-learning strategy in the basic harmony *** hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the teaching-Leaming-based *** on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.

关键词： Unconstrained binary quadratic programming Harmony search teaching-learning-based optimization

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teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

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COMPUTER-AIDED DESIGN 2011年第3期43卷 303-315页

作者： Rao, R. V. Savsani, V. J. Vakharia, D. P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

A new efficient optimization method, called 'teaching-learning-based optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： teaching-learning-based optimization Constrained benchmark functions Mechanical design optimization

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An optimization method of active distribution network considering uncertainties of renewable DGs 10

An optimization method of active distribution network consid...

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10th International Conference on Applied Energy (ICAE)

作者： Yong, Chengsi Kong, Xiangyu Chen, Ying Xu, Quan Yu, Li Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China China Southern Power Gnat Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China

With the access of the renewable DGs such as wind turbines and photovoltaic generations, network operation state is uncertain due to the randomness of these renewable DGs. This paper proposes a novel optimal method of the active distribution network (ADN) considering the uncertain conditions and the coordination control of source-network-load. By optimizing the controllable distributed power output, controlling the network switches, and managing the demand-side load synchronously, the impact of distributed renewable energies can be reduced and the reliable operation of ADN can be ensured. The chance constrained programming is used to deal with the uncertainties. The proposed model is settled by the improved teaching-learning-based optimization algorithm (ITLBO) and the performance of the algorithm is verified by the comparison with the TLBO in the modified IEEE 33-bus distribution system. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Active distribution network distributed renewable energy teaching-learning-based optimization chance-constrained programming

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Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

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ACTA GEOPHYSICA 2019年第6期67卷 1693-1705页

作者： Yilmaz, Banu Aras, Egemen Kankal, Murat Nacar, Sinan Karadeniz Tech Univ Fac Technol Dept Civil Engn TR-61830 Trabzon Turkey Bursa Tech Univ Fac Engn & Nat Sci Dept Civil Engn TR-16330 Bursa Turkey Bursa Uludag Univ Fac Engn Dept Civil Engn TR-16059 Bursa Turkey Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey

The main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching-learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, Inanli and Altinsu, in Coruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.

关键词： Artificial bee colony Coruh river basin Estimation Suspended sediment loading teaching-learning-based optimization

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Scheduling just-in-time part replenishment of the automobile assembly line with unrelated parallel machines

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第14期233卷 5113-5130页

作者： Zhou, Binghai Peng, Tao Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China

With increasing product customization, just-in-time part replenishment has become a significant scheduling problem in the automobile assembly system. This paper investigates a new unrelated parallel machine scheduling problem of an assembly line, where machines are employed to deliver material boxes from an in-house warehouse to workstations. The schedule is to appropriately specify the assignment and sequence of material boxes on each machine for minimizing line-side inventories under no stock-out constraints. By taking advantages of domain properties, an exact algorithm is developed to cope up with small-scale instances. In terms of real-world scale instances, a hybrid teaching-learning-based optimization metaheuristic is established by integrating teaching-learning-based optimization with a beam search technique. Experimental results indicate that the scheduling algorithms are effective and efficient in solving the proposed unrelated parallel machine scheduling.

关键词： Automobile assembly line part replenishment just-in-time unrelated parallel machines backtracking teaching-learning-based optimization

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Application of Jaya algorithm-trained artificial neural networks for prediction of energy use in the nation of Turkey

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2019年第5期14卷 183-200页

作者： Uzlu, Ergun Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey

In this study, a novel artificial neural network (ANN)-Jaya algorithm hybrid artificial intelligence model was developed to estimate Turkey's future energy use. The model estimates energy consumption based on gross domestic product (GDP), population, import data, and export data. The Jaya algorithm used in our model's development is a simple and powerful metaheuristic algorithm that overcomes the complexity of difficult optimization problems;it provides optimal results quickly owing to its ease of applicability and simple structure. Our ANN-Jaya model's performance was compared with the performance of artificial bee colony (ABC) and teaching learning based optimization (TLBO) algorithm-trained ANN models. According to the root mean square error (RMSE) values obtained for the test set, the proposed ANN-Jaya model performed 36.7% and 46.2% better than the ANN-ABC and ANN-TLBO models, respectively. After defining the optimal configurations, three energy consumption prediction scenarios were developed and compared with previously published forecasts.

关键词： Artificial neural network teaching-learning-based optimization artificial bee colony algorithm Jaya algorithm energy consumption demand

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