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An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 240-252页

作者： Rubaiee, Saeed Yildirim, Mehmet Bayram Univ Jeddah Dept Ind Engn POB 80327 Jeddah 21589 Saudi Arabia Wichita State Univ Dept Ind & Mfg Engn 1845 Fairmount Wichita KS 67260 USA

Energy-aware scheduling in manufacturing operations with time-of-use electricity tariffs is a challenging problem. Sustainable manufacturing is gaining significant momentum: companies are not only improving their product quality, but also optimizing the production processes to improve energy consumption (i.e., minimizing energy cost) in order to manage environmental challenges which contribute to global climate change. The purpose of this paper is to study a preemptive scheduling problem on a single-machine to minimize the total completion time and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To solve these objectives, we develop several new holistic ant colony optimization algorithms. The proposed model is solved via several methods including weighted sum method (WSM) using CPLEX, and multiobjective ant colony optimization based on a dominance ranking (ACO-DR) or based on a dominance ranking procedure and crowding distance comparison (ACO-DRC) or based on a heuristic approach to obtain an approximate Pareto-front and also provide information on when to start and resume each job for any solution on the Pareto-front. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we demonstrate how the results of the multiobjective model could be utilized in decision making using the multiobjective optimization on the basis of ratio analysis (MOORA) method. This proposed model and heuristics allows decision makers to operate in challenging-data enabled environments in industrial interne of things ecosystem, and assists in optimizing production planning to improve energy cost.

关键词： Single-machine preemptive scheduling Energy-aware scheduling Time-of-use electricity tariffs Multiobjective optimization Internet of things (IoT) ant colony optimization algorithm Green and sustainable manufacturing

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UAV path path planning method for digital terrain model reconstruction - A debris fan example

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AUTOMATION IN CONSTRUCTION 2018年 93卷 214-230页

作者： Yang, Cheng-Hsuan Tsai, Meng-Han Kang, Shih-Chung Hung, Chi-Yao Natl Taiwan Univ Dept Civil Engn Taipei Taiwan Natl Taiwan Univ Ctr Weather Climate & Disaster Res Taipei Taiwan Natl Chung Hsing Univ Dept Soil & Water Conservat Taichung Taiwan

This research develops an unmanned aerial vehicle (UAV) path-planning method that aims to ensure the required image overlap and optimize the flying routes when applying the UAV for digital terrain model's (DTM) reconstruction. To collect images on a terrain for image modeling, enough overlap between each collected image must be ensured. In addition, when planning the optimized flying routes for collecting images on a debris fan, the specifications of the debris fan and the limitations of the UAV should both be taken into consideration. The path planning method takes a debris fan as an example and refers to the specifications of a debris fan and the limitations of the UAV. The developed method can help the operators to ensure the image overlap through dividing the debris fan into cells by the UAV's maximum image collection distance interval. The near-optimized UAV flying paths are calculated though applying a modified ant colony optimization algorithm (ACO). The developed method is validated to be able to help operators to sufficiently use the limited UAV batteries and evaluate the efficiency of the image collection process. A site experiment was also conducted for validating the workability of the developed method. The result of the comparison shows that the path-planning method can reduce 18.5% of the image collection time. It also confirms that applying the method on an actual debris fan can guarantee the required image overlapping and generate a complete DTM without model breaking.

关键词： Debris fan Digital terrain model Unmanned aerial vehicle Path planning ant colony optimization algorithm

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Research on shared parking allocation considering the heterogeneity of parking slot providers' temporary parking demand

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2024年第10期16卷 1305-1317页

作者： Li, Qiaoru Cheng, Juanjuan Chen, Liang Hebei Univ Technol Sch Civil & Transportat Tianjin 300401 Peoples R China

Studies on the allocation of parking demand during the sharing period primarily focus on public parking users, ignoring parking slot providers' temporary parking demand with heterogeneity. Therefore, this paper takes the allocation of parking slot providers' temporary parking demand as the research object and establishes a differentiated parking allocation (DPA) model to maximize the platform's net profit. The model is solved using the ant colony optimization (ACO) algorithm and compared with the First-Come-First-Served (FCFS) algorithm. Then, the platform adopts differentiated or undifferentiated charge measures when charging for the parking slot providers' temporary parking demand. The numerical analysis is performed to select three indicators for evaluation: 1) the utilization rate, 2) the net profit, and 3) the degree of time fragmentation. Results show that the ACO algorithm has an excellent optimization effect in allocating, and the differentiated allocation-undifferentiated charges for the parking slot providers' temporary parking demand is feasible.

关键词： Shared parking temporary parking demand heterogeneity differ-entiated parking allocation model time fragmentation ant colony optimization algorithm

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ant colony algorithm for the shortest loop design problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2006年第4期50卷 358-366页

作者： Eshghi, Kourosh Kazemi, Morteza Sharif Univ Technol Dept Ind Engn Tehran Iran

In this paper, a new algorithm for solving the shortest loop design problem is presented. The shortest loop design problem is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. In this paper, first it is shown that this problem can be represented as a graph model. The properties of the presented model enable us to design a meta-heuristic based on ant colony system algorithm for solving the shortest loop design problem. Computational results show the. efficiency of our algorithm in compare to the other techniques. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： shortest loop design problem ant colony optimization algorithm meta-heuristic algorithms

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Time series importance measure-based reliability optimization for cellular manufacturing systems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 244卷

作者： Li, Haibao Cai, Zhiqiang Zhang, Shuai Zhao, Jiangbin Si, Shubin Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ MIT Key Lab Ind Engn & Intelligent Mfg Xian 710072 Peoples R China Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Peoples R China Xian Univ Sci & Technol Shaanxi Key Lab Mine Electromech Equipment Intelli Xian 710054 Peoples R China

Cellular manufacturing systems (CMSs) can improve the quality and efficiency of the manufacturing process by multiple processing cells with different functions and group technology. CMSs require high reliability to complete processing missions successively, so reliability optimization is an important part to guarantee system performance. This paper proposes a binary decision diagram-based three-step evaluation method to analyze CMS reliability. A reliability optimization model of CMS is constructed by considering the limited cost to determine the optimal combination of machine degradation parameters. Considering the advantages of ant colony optimization (ACO) and time series importance measure (TIM), a TIM-based ant colony optimization (TIACO) is developed to solve the optimization model. To verify the performance of TIACO, system reliability and running time are introduced to compare with genetic algorithm (GA), ACO, and time series importance measure-based genetic algorithm (TIGA). (1) System reliability obtained by TIACO is always the best. (2) Running time of TIACO is smaller. A case study of an unmanned aerial vehicle manufacturing company verifies the effectiveness of TIACO, and machines with higher TIMs should be given priority to improving their degradation parameters, which provides a new idea for reliability evaluation and optimization of CMSs.

关键词： Cellular manufacturing systems Reliability evaluation System reliability optimization Time series importance measure ant colony optimization algorithm

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A Self-Adaptive Hybrid optimization algorithm for Solving Consecutive Reaction Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2017年第4期16卷

作者： Zhou, Di Zhu, Jiangning Wang, Yazi Sichuan Univ Arts & Sci Dazhou 635000 Peoples R China Chaoyang Teachers Coll Chaoyang Dept Math & Comp Sci Chaoyang 122000 Peoples R China Zhoukou Normal Univ Sch Math & Stat Zhoukou 466001 Peoples R China

The actual temperature control for consecutive reaction problem is a complex optimization problem. Genetic algorithms (GA) is a metaheuristic inspired by imitating the processes observed during natural evolution. It has a strong global search ability and less computation time, but it exists the premature convergence and poor stability. ant colony optimization (ACO) is a metaheuristic inspired by imitating the behavior of real ants. It has the robustness and parallel computation, but it exists the slow convergence speed and stagnation phenomenon. In this paper, a new genetic and ant colony self-adaptive hybrid (NGASAH) algorithm based on the chaotic searching strategy, multi-populations and self-adaptive parameter control strategies is presented. In the proposed NGASAH algorithm, the chaotic searching strategy is used to avoid the optimal solution. The strategy of the multiple populations is used to avoid to converge to a local extreme point of all particles. The strategy of self-adaptive parameter control is used to dynamically balance the local search ability and the global ability, and improve the convergence speed. The actual temperature control of consecutive reaction problem is used to test the validity of the NGASAH algorithm. The experiment results show that the NGASAH algorithm can obtain the global search ability and the faster convergence speed in solving the complex optimization problems.

关键词： Genetic algorithm ant colony optimization algorithm self-adaptive control strategy multi-strategies consecutive reaction

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RESEARCH ON INTEGRATING BLOCKCHAIN AND MACHINE LEARNING LPP algorithm IN ONLINE EDUCATION PLATFORM UNDER COVID-19 ENVIRONMENT

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1442-1454页

作者： Wang, Daojun Wang, Meishu Xing, Xinli Qingdao Agr Univ Dept Phys Educ Qingdao 266109 Peoples R China

Learners confront the issue of navigating an enormous quantity of resources in the developing field of online education, which has been exacerbated by the COVID-19 pandemic. To solve this, our research proposes a novel Learner Path Planning (LPP) model that integrates blockchain and machine learning technologies to maximize the online learning experience. This model employs the ant colony optimization technique, which has been upgraded with blockchain for enhanced security and machine learning for intelligent path planning, to provide a more personalized and efficient learning experience. Our approach determines the extent of concept realization and interaction by examining the interaction degrees of knowledge points, establishing heuristic information and initial pheromone levels for the optimization process. This technique not only optimizes teaching duration based on instructional efficacy, but it also adapts dynamically to individual learner needs. Our empirical data reveal that goal success rates improve significantly across all learner levels. For example, elementary students in 2021 had the highest goal achievement rate of 0.5896. In 2019, intermediate and advanced learners attained rates of 0.7726 and 0.9058, with a significant association between course similarity and target achievement. Blockchain integration ensures secure and transparent processing of educational data, while machine learning algorithms successfully personalize learning routes to meet the various demands of learners. This study not only assists learners in effectively identifying suitable resources, but it also provides useful insights for instructors in improving online teaching approaches. The model's adaptability and scalability make it particularly applicable in the context of the COVID-19 pandemic's rapid developments and problems in the education sector.

关键词： COVID-19 Online education platform ant colony optimization algorithm Learner behavior characteristics Path planning

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Self-Adaptive PCNN Based on the ACO algorithm and its Application on Medical Image Segmentation

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2017年第2期23卷 303-310页

作者： Xu, Xinzheng Liang, Tianming Wang, Guanying Wang, Maxin Wang, Xuesong China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Peoples R China

Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of ant colony optimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN's parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO's convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal sections and noisy medical images, were used to test and validate the performance of the proposed method. The experimental results illustrated that our method was accurate and effective to MRI medical images.

关键词： Pulse-coupled neural networks self-adaptive ant colony optimization algorithm medical image segmentation

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New heuristic approaches for the dominating tree problem

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APPLIED SOFT COMPUTING 2013年第12期13卷 4695-4703页

作者： Sundar, Shyam Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given an undirected, connected and edge-weighted graph, the dominating tree problem (DTP) seeks on this graph a tree with minimum total edge weight such that each vertex of the graph is either in this tree or adjacent to a vertex in this tree. The DTP is a.V2-Hard problem. In the literature only two heuristics for this problem are proposed so far in spite of the fact that it has several practical applications in the field of wireless sensor networks. In this paper, we propose one heuristic and two swarm intelligence techniques, viz, an artificial bee colony algorithm and an ant colony optimization algorithm for the DTP. Computational results show the effectiveness of our approaches. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Artificial bee colony algorithm ant colony optimization algorithm Dominating tree Heuristic Swarm intelligence Wireless sensor networks

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Layout and size optimization of tree-like pipe networks by incremental solution building ants

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2008年第2期35卷 129-139页

作者： Afshar, M. H. Iran Univ Sci & Technol Dept Civil Engn Tehran 16844 Iran

Application of an ant colony optimization algorithm (ACOA) for simultaneous layout and size optimization of tree-like pipe networks is described in this paper using two different formulations. In the first formulation, each link of the base graph is considered as the decision point of the problem. Each decision point is considered in turn and the ants are then required to choose any of the available options at the current decision point. The list of available pipe diameters with the null option included for each link constitutes the available options in this formulation. In the second approach, the network nodes are considered as the decision points of the problem. The available options in this formulation are represented by the list of allowable pipe diameters for all plausible links such that the resulting network is a tree network. The plausible links at each decision point are provided by a tree-growing algorithm. This formulation leads to a very small search space compared with the first algorithm, as each ant is now forced to create a feasible solution regarding the layout geometry of the network. This approach fully exploits the sequential nature of the ACOA in building solutions, which is believed to be one of the main advantages of these algorithms compared with other general metaheuristics. The proposed methods are applied to find the optimal layout of two benchmark examples in the published literature and the results are presented and compared with the existing results.

关键词： pipe networks ant colony optimization algorithm tree networks layout and size optimization

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