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multi-objective scheduling of Scientific Workflows in multisite clouds

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年 63卷 76-95页

作者： Liu, Ji Pacitti, Esther Valduriez, Patrick de Oliveira, Daniel Mattoso, Marta Inria Microsoft Inria Joint Ctr LIRMM Montpellier France Univ Montpellier Montpellier France Univ Fed Fluminense Inst Comp Niteroi RJ Brazil Univ Fed Rio de Janeiro COPPE BR-21941 Rio De Janeiro Brazil

Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Scientific workflow Scientific workflow management system multi-objective scheduling Parallel execution multisite cloud

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multi-objective scheduling of dynamic job shop using variable neighborhood search

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 282-287页

作者： Adibi, M. A. Zandieh, M. Amiri, M. Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management Tehran Iran Qazvin Azad Univ Fac Ind & Mech Engn Qazvin Iran Allameh Tabatabaei Univ Management & Accounting Fac Dept Ind Management Tehran Iran

Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions. (C) 2009 Published by Elsevier Ltd.

关键词： Dynamic job shop multi-objective scheduling Variable neighborhood search Artificial neural networks

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multi-objective scheduling Model for Cascade Hydropower Stations Based on Comprehensive Unit Condition Evaluation 7

Multi-Objective Scheduling Model for Cascade Hydropower Stat...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Hou, Menglong Ji, Huaijie Fang, Yong Guo, Yuxiang Han, Qiang Southern Power Grid Peak Shaving & Frequency Modu Qingxu Project Dept Operat Branch China Guangzhou Peoples R China IKAS Ind Co Ltd Chongqing Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

ISBN: (纸本)9798350375794;9798350375800

Against the backdrop of current energy shortages and the emphasis on environmental sustainability, cascade hydropower stations play a crucial role in the energy sector as a clean and renewable energy source. This study focuses on a multi-objective scheduling model for cascade hydropower stations based on comprehensive unit condition evaluation, aimed at enhancing the optimization efficiency of cascade hydropower station scheduling. We have constructed a scheduling model with the objective functions of minimizing the total residual load and peak-valley difference. This model is designed to satisfy various constraints, including the output, electricity generation, water balance, reservoir capacity, spillage, and the on-off duration of hydroelectric units. Following the determination of unit start-stop priorities, we employed the Levy Flight Search Mechanism (LFSM) and an improved differential evolution algorithm with pollen dispersal for model resolution, obtaining optimal scheduling results for cascade hydropower stations. Experimental results indicate that the model, through comprehensive evaluation of the operational states of hydroelectric units, has significantly improved the selection of participating units in the scheduling process, resulting in a notable increase in unit output post-optimization. The fluctuation in the residual load curve is decreased by about 50%, compared with the original load.

关键词： Comprehensive State Evaluation Cascade Hydropower Station multi-objective scheduling Improved Pollen Algorithm Levy Search Mechanism Start-Stop Priority

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multi-objective scheduling of Electric Car Charging Jobs Based on Fuzzy Goal Programming

Multi-objective Scheduling of Electric Car Charging Jobs Bas...

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IEEE International Conference on Automation and Logistics (ICAL)

作者： Cai, Longsheng Zeng, Xuecong Murata, Tomohiro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781467303644

Rapid development of electric cars has raised a problem of charging batteries of them. As for the owner of the charging machines, how to make the charging jobs' schedule more efficient and more profitable is worthy of concern. Both cost related goals and operation related goals are expected to be satisfied simultaneously. This paper considered a single-machine charging job scheduling problem and proposed a fuzzy goal programming method to get an optimized cost and operational result by considering the different charging cost brought by Time-of-use price mechanism and the characteristic of batteries, which affects the charging process time, charging efficiency and charging unit power consumption. In the proposed method, a fuzzy relation model is developed to deal with the multi-objective situation to satisfy various goals, by considering the importance relation between goals. The intention of the decision maker is satisfied well. The decision maker can control the parameter to balance the emphasis on goals or on relations according to variety of manage policy. A numerical example is given to illustrate the effectiveness of the proposed method.

关键词： electric car charging fuzzy goal programming multi-objective scheduling

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Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

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JOURNAL OF INTELLIGENT MANUFACTURING 2008年第4期19卷 421-432页

作者： Restrepo, Indira Molina Balakrishnan, S. Univ Manitoba Dept Mech & Mfg Engn Winnipeg MB R3T 2N2 Canada

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.

关键词： fuzzy logic multi-objective scheduling flexible manufacturing cell

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A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第2期28卷 337-351页

作者： Yan, Hong-Sen Li, Wen-Chao Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ MOE Key Lab Measurement & Control Complex Syst En Nanjing 210096 Jiangsu Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China

A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell (JSKMC) is proposed in this paper, targeting such scheduling issues as make-span, mean complete time of tasks, total tardiness of tasks, number of tardy tasks and the maximum tardiness. Four matrixes are designed to represent the scheduling model of JSKMC. Properties of the key arcs of tasks are discussed and it is found helpless to seek a better solution by reversing the direction of the middle key arcs of tasks. A simplified neighborhood is then established whereby the number of feasible solutions to be searched for is greatly reduced. Based on the above a multi-objective scheduling algorithm with self-evolutionary feature for JSKMC is proposed. Adaptive heuristic critic method is adopted in the algorithm, whose associate search element (ASE) module is designed to select the appropriate action for acquisition of a better solution in the next step by using the knowledge obtained from learning;such an ability of this module can be improved progressively with the increasing training. A scheduling algorithm based on ASE is developed, in which a Pareto archive is embedded to obtain the Pareto optimal solutions. Numerical simulation results confirm the strong ability of the proposed algorithm to home in on the optimal solution by self-evolution via learning.

关键词： Self-evolution multi-objective scheduling Knowledgeable manufacturing system Job-shop Adaptive heuristic critic

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An evolutionary approach to multi-objective scheduling of mixed model assembly lines

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COMPUTERS & INDUSTRIAL ENGINEERING 1999年第1-2期37卷 69-73页

作者： Celano, G Fichera, S Grasso, V La Commare, U Perrone, G Univ Catania Ist Macchine I-95124 Catania Italy Univ Palermo Dipartimento Tecnol & Prod Meccan I-90133 Palermo Italy

In this paper a multi-objective genetic algorithm for the scheduling of a mixed model assembly line is proposed, pursuing the line stop time minimisation together with the component usage smoothing. Specific features of the developed GA are step by step random selection of diversified crossover and mutation operators, population control for the substitution of duplicate chromosomes, and in-process updating of GA control parameters. Three different formulation of the fitness function were been tested with some distinct line configurations. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： mixed model assembly lines multi-objective scheduling genetic algorithm

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multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

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JOURNAL OF CLEANER PRODUCTION 2023年 428卷

作者： Su, Pengfei Zhou, Yue Wu, Jianzhong Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future.

关键词： Steel industry Sustainability multi-objective scheduling Uncertainties Energy storage system Resource task network

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APPLYING SUB-POPULATION MEMETIC ALGORITHM FOR multi-objective scheduling PROBLEMS

APPLYING SUB-POPULATION MEMETIC ALGORITHM FOR MULTI-OBJECTIV...

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6th International Conference on Informatics in Control, Automation and Robotics

作者： Wang, Yen-Wen Fan, Chin-Yuan Liu, Chen-Hao Chin Yun Tech Univ Dept Ind Engn & Management 229 Chien Hsin Rd Tao Yuan Taiwan Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan Kainan Univ Dept Informat Management Taoyuan Taiwan

ISBN: (纸本)9789898111999

Memetic Algorithm is a population-based approach for heuristic search in optimization problems. It has shown that this mechanic performs better than traditional Genetic Algorithms for some problem. In order to apply in the multi-objective problem, the basic local search heuristics are combined with crossover operator in the sub-population in this research. This approach proposed is named as Sub-population with Memetic Algorithm, which is applied to deal with multi-objective Flowshop scheduling Problems. Besides, the Artificial Chromosome with probability matrix will be introduced when the algorithm evolves to certain iteration for injecting to individual to search better combination of chromosomes, this mechanism will make faster convergent time for evolving. Compares with other three algorithms which are MGISPGA, NSGA-II and SPEA2, the experiments result show that this algorithm possess fast convergence and average scatter of Pareto solutions simultaneously for solving multi-objective Flowshop scheduling Problems in test instances.

关键词： Flowshop scheduling problem multi-objective scheduling Memetic Algorithm

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Develop a Sub-population Memetic Algorithm for multi-objective scheduling Problems

Develop a Sub-population Memetic Algorithm for Multi-objecti...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Wang Yen-Wen Liu Chen-Hao Fan Chin-Yuan Ching Yun Univ Dept Ind Engn & Management Taoyuan Taiwan Kainan Univ Dept Informat Management Taoyuan Taiwan Ming Dao Univ Dept Business Innovat & Dev Changhua Taiwan

ISBN: (纸本)9781424455690

Memetic Algorithm is a population-based approach for heuristic search in optimization problems. It has shown that this mechanic performs better than traditional Genetic Algorithms for some problem. In order to apply in the multi-objective problem, the basic local search heuristics are combined with crossover operator in the sub-population in this research. This approach proposed is named as Sub-population with Memetic Algorithm, which is applied to deal with multi-objective Flowshop scheduling Problems. Besides, the Artificial Chromosome with probability matrix will be introduced when the algorithm evolves to certain iteration for injecting to individual to search better combination of chromosomes, this mechanism will make faster convergent time for evolving. Compares with MOSA, the experiments result show that this algorithm possess fast convergence and average scatter of Pareto solutions simultaneously for solving multi-objective Flowshop scheduling Problems in test instances.

关键词： Flowshop scheduling problem multi-objective scheduling Memetic Algorithm

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