Article
Computer Science, Information Systems
Moch Saiful Umam, Mustafid Mustafid, Suryono Suryono
Summary: This paper combines tabu search with a genetic algorithm, using a new partial opposed-based technique for population initialization to minimize makespan. By combining these two algorithms, a new algorithm is obtained that balances searches for higher quality solutions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Haoxiang Qin, Yuyan Han, Yuting Wang, Yiping Liu, Junqing Li, Quanke Pan
Summary: This paper introduces a new flow shop combinatorial optimization problem, called the blocking hybrid flow shop group scheduling problem (BHFGSP). The proposed algorithm, a novel iterated greedy algorithm, is effective in solving the BHFGSP. Experimental results demonstrate the algorithm's performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yuting Wang, Yuyan Han, Quan-ke Pan, Huan Li, Yuhang Wang
Summary: In this study, 48 available MILP models and an efficient CP model are constructed by categorizing the constraints. The experimental results show that models 24 and 48 exhibit superior performance, highlighting the effectiveness of the hybrid modeling approach.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Mathematics
Mei Li, Gai-Ge Wang, Helong Yu
Summary: This paper studies the fuzzy hybrid green shop scheduling problem with fuzzy processing time, aiming to minimize makespan and total energy consumption. By proposing a discrete artificial bee colony algorithm, it achieves higher diversity and convergence speed.
Article
Management
Alexander J. Benavides, Antony Vera
Summary: The NEH constructive heuristic and the iterated greedy algorithm are the best performing approximate methods for the permutational flow shop scheduling problem. Inserting jobs based on the resulting makespan evaluation and selecting the shortest makespan insertion positions, new tiebreakers have been proposed to improve the results and outperformed previous tiebreakers in experiments. The proposed tiebreakers, based on weighted and unweighted idle time increment approximations, embedded in the iterated greedy algorithm, prove to be the best approximate methods for the problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Zhongyuan Liang, Mei Liu, Peisi Zhong, Chao Zhang
Summary: This article introduces a new adaptive genetic algorithm and its application in solving the job shop scheduling problem. By adjusting the crossover probability and mutation probability, and making use of the idle time before critical operations through multi-operation combination and adjustment, the accuracy and convergence efficiency of the solution have been significantly improved.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Jun-Hee Han, Ju-Yong Lee
Summary: This study focuses on optimizing a two-stage assembly-type flow shop with limited waiting time constraints to minimize the makespan. A mixed-integer programming formulation and various heuristic algorithms were proposed to tackle this NP-hard problem. Computational experiments showed the effectiveness of the iterated greedy algorithm and simulated annealing algorithm on different problem sizes.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Weishi Shao, Zhongshi Shao, Dechang Pi
Summary: This paper studies a distributed heterogeneous hybrid flow shop lot-streaming scheduling problem (DHHFLSP) with the minimization of makespan. The mixed-integer linear programming model (MILP) of DHHFLSP is established, and eighteen constructive heuristics and an iterated local search algorithm (ILS) are designed to solve the problem. The comparisons with several related algorithms on extensive testing instances demonstrate the effectiveness and efficiency of the ILS algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Management
Czeslaw Smutnicki, Jaroslaw Pempera, Grzegorz Bocewicz, Zbigniew Banaszak
Summary: This paper investigates the problem of cyclic scheduling in a manufacturing system, considering the flow of jobs with identical technological routes, no-wait constraints, and missing operations. The problem is decomposed into two sub-problems, and alternative methods are provided for finding the minimal cycle time and optimal processing order of jobs. A metaheuristic approach is used to solve the latter sub-problem. Experimental examination demonstrates the efficiency and quality of the proposed algorithm.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
Mehboob Hussain, Lian-Fu Wei, Fakhar Abbas, Amir Rehman, Muqadar Ali, Abdullah Lakhan
Summary: This study proposes a Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address workflow scheduling problems in hybrid cloud environments. By reducing makespan and energy consumption simultaneously, the algorithm utilizes quantum principles, such as qubits and quantum rotation gates, to improve population diversity and convergence.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Gaurav Agarwal, Sachi Gupta, Rakesh Ahuja, Atul Kumar Rai
Summary: Multiprocessor task scheduling is the operation of processing more than two tasks simultaneously in the system. The fog-cloud multiprocessor computing structures are a category of exchanged collateral structures with high demand. However, the existing fog-cloud system faces challenges such as scheduling and energy consumption due to excess clients and various services. To overcome these challenges, a hybrid genetic algorithm and energy conscious scheduling approach is proposed, which integrates genetic algorithm and energy conscious scheduling model. The proposed method has been compared with existing methods and proven to be more efficient.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Mechanical
Wangming Li, Dong Han, Liang Gao, Xinyu Li, Yang Li
Summary: This paper investigates the integrated production and transportation scheduling problem in hybrid flow shops, proposes an effective solution method, and validates its performance through experiments. The results show that the proposed method is effective.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Rafael Lucas Costa Souza, Alireza Ghasemi, Ahmed Saif, Abolfazl Gharaei
Summary: This paper presents a modeling and solution approach for a robust job-shop scheduling problem, considering machine availability and degradation over time, and develops two metaheuristic algorithms to optimize job sequences and preventive maintenance tasks. Experimental results show excellent performance of the proposed algorithms in terms of both quality and runtime.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yan Qiao, NaiQi Wu, YunFang He, ZhiWu Li, Tao Chen
Summary: This paper investigates the scheduling problem of a class of two-stage hybrid flow shops and proposes an adaptive genetic algorithm and a local search method to solve it. The experiments show that the proposed method can find high-quality solutions in a short time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Ming Li, Bin Su, Deming Lei
Summary: The paper considers the fuzzy distributed assembly flow shop scheduling problem and proposes an algorithm optimized through imperialist cooperation. Experimental results demonstrate the excellent performance of the algorithm in solving the problem.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhiqiang Lu, Weiwei Cui, Xiaole Han
COMPUTERS & INDUSTRIAL ENGINEERING
(2015)
Article
Computer Science, Interdisciplinary Applications
Weiwei Cui, Zhiqiang Lu, Chen Li, Xiaole Han
COMPUTERS & INDUSTRIAL ENGINEERING
(2018)
Article
Engineering, Industrial
Weiwei Cui, Lin Li
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2018)
Article
Computer Science, Interdisciplinary Applications
Wei-Wei Cui, Zhiqiang Lu, Ershun Pan
COMPUTERS & OPERATIONS RESEARCH
(2014)
Article
Operations Research & Management Science
Weiwei Cui, Lin Li, Zhiqiang Lu
NAVAL RESEARCH LOGISTICS
(2019)
Article
Management
Weiwei Cui, Huali Sun, Beixin Xia
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2020)
Article
Green & Sustainable Science & Technology
Weiwei Cui, Biao Lu
Article
Computer Science, Interdisciplinary Applications
Weiwei Cui, Biao Lu
Summary: This study proposes a mathematical model for the energy-aware operations management of a manufacturing plant to enhance its competitiveness in the global market. The model integrates production, maintenance, and energy aspects under Time-of-Use electricity tariff, and a two-layer math-heuristic approach is developed to efficiently solve the model. The tradeoff between energy cost and makespan shows that more profit can be achieved through the proposed model.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Proceedings Paper
Engineering, Industrial
W. Cui, Y. J. Yang
2018 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM)
(2018)
Article
Computer Science, Interdisciplinary Applications
Wei-Wei Cui, Zhiqiang Lu
COMPUTERS & OPERATIONS RESEARCH
(2017)
Proceedings Paper
Automation & Control Systems
Wei-Wei Cui, Zhiqiang Lu
2013 10TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA)
(2013)
