Article
Energy & Fuels
Yuzhou Zhou, Jiexing Zhao, Qiaozhu Zhai
Summary: This paper presents a new multi-stage robust scheduling method for cascade hydropower system considering nonanticipativity. By simultaneously considering nonanticipativity and robustness, the method aims to ensure feasibility of scheduling solutions, increase utilization of hydropower reserves, and guarantee both operation security and economy of energy system. Numerical tests on a real energy system confirm the effectiveness of the proposed method.
Article
Computer Science, Interdisciplinary Applications
Harvinder Singh, Sanjay Tyagi, Pardeep Kumar, Sukhpal Singh Gill, Rajkumar Buyya
Summary: This paper discusses various nature-inspired metaheuristic algorithms for scheduling tasks in cloud computing environments and identifies Crow Search Algorithm as the most optimal technique in terms of efficiency and cost through comparative analysis of six algorithms.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Management
Maxence Delorme, Manuel Iori, Nilson F. M. Mendes
Summary: This study investigates the scheduling problem of jobs and maintenance activities on a set of unrelated parallel machines, where job processing time increases with a deterioration factor. The objective is to minimize makespan. Four mixed integer linear programming models are introduced, along with an iterated local search metaheuristic for large instances, supported by empirical evidence from computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Automation & Control Systems
Nariman Nakhjiri, Maria Salamo, Miquel Sanchez-Marre, Juan Carlos Morales
Summary: This paper investigates Astronomical Observations Scheduling, proposes a hybrid multi-start metaheuristic scheduler (HAP) to address the challenges and requirements, and tests its performance on a real-world mission. The results show that HAP outperforms other methods and demonstrates good scalability and adaptability.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Industrial
Baoyu Liao, Shaojun Lu, Tao Jiang, Xing Zhu
Summary: Ship maintenance service optimisation is important for improving the competitiveness of shipbuilding enterprises. This paper investigates a scheduling problem considering various factors and proposes mathematical models and algorithms to solve it. The performance of the proposed methods is validated through computational experiments.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Computer Science, Interdisciplinary Applications
Radoslaw Rudek
Summary: This paper introduces a novel optimization framework for scheduling problems with machine deterioration and maintenance activities. The framework is computationally efficient and robust, and it includes templates for implementing optimization algorithms, making it applicable within Industry 4.0. The paper demonstrates the efficiency and robustness of the proposed approach through theoretical and experimental analysis. The modular architecture of the framework allows for further integration with new algorithms to improve neighborhood search techniques.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Operations Research & Management Science
Alain Quilliot, Antoine Sarbinowski, Helene Toussaint
Summary: This study addressed a non preemptive version of managing a one-way vehicle sharing system with strong makespan restrictions, aiming to link static and online paradigms based on a vehicle-driven approach that puts vehicle routing strategies at the core of decision-making process.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Engineering, Civil
Wen-jing Niu, Zhong-kai Feng, Yu-rong Li, Shuai Liu
Summary: Reservoir operation is crucial in crisis management, but faces challenges due to the curse of dimensionality. The cooperation search algorithm, inspired by team cooperation behaviors, utilizes team communication, reflective learning, and internal competition operators to enhance search efficiency and precision.
WATER RESOURCES MANAGEMENT
(2021)
Article
Management
Ali Mohammadzadeh, Danial Javaheri, Javad Artin
Summary: This paper presents a hybrid multi-objective optimization algorithm called HSOS-SOA, which combines the Symbiotic Organisms Search and Seagull Optimization Algorithm. It utilizes chaotic maps to generate random numbers and strikes a balance between exploration and exploitation, leading to faster convergence. The HSOS-SOA is applied to solve scientific workflow scheduling problems in multisite cloud computing, considering factors such as makespan, cost, and reliability. Extensive analyses conducted in Microsoft Azure multisite cloud demonstrate that the HSOS-SOA outperforms other algorithms in terms of metrics such as IGD, Coverage Ratio, etc. Experimental results show significant improvements in makespan (5.72-28.61%), cost (5.16-45.16%), and reliability (3.11-25%) compared to well-known metaheuristic algorithms.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Mathematics
Dung-Ying Lin, Tzu-Yun Huang
Summary: In this study, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times. Empirical results show that this solution strategy outperforms a commonly used commercial optimization package and provides better schedules in a more efficient manner.
Article
Computer Science, Artificial Intelligence
Biao Zhang, Chao Lu, Lei-lei Meng, Yu-yan Han, Hong-yan Sang, Xu-chu Jiang
Summary: Inspired by a real-world cellular manufacturing system, this study focuses on a reconfigurable distributed flowshop scheduling problem with grouped jobs. A mixed integer linear programming model is developed for small-scaled instances, and a nested variable neighborhood descent algorithm is proposed for larger instances. The proposed algorithm outperforms other state-of-the-art metaheuristics and the math solver CPLEX.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Radoslaw Rudek
Summary: This study introduces a fast insert neighborhood search (FINS) for scheduling problems with general learning curves, which significantly outperforms traditional methods.
Comparing to other algorithms, the FINS method is 120 times faster and improves criterion values by up to 25% for instances with 100-800 jobs and 5-80 machines.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Latreche Imene, Slatnia Sihem, Kazar Okba, Batouche Mohamed
Summary: This article introduces the use of a third-generation multi-objective optimization method NSGA-III for task scheduling in the cloud. The performance of NSGA-III is compared with its previous version NSGA-II, and the results show that NSGA-III outperforms NSGA-II in solving this problem.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Environmental Sciences
Shuangquan Liu, Xuhan Luo, Hao Zheng, Congtong Zhang, Youxiang Wang, Kai Chen, Jinwen Wang
Summary: Operators face a dilemma in choosing the water levels of hydropower reservoirs at the end of dry seasons. This study presents a quarterly hydropower scheduling model and a rolling strategy to simulate multiple years of reservoir operations. The simulation conducted on 11 cascaded reservoirs in China suggests that targeting specific drawdown water levels leads to more hydropower generation and larger firm hydropower output.
