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
Automation & Control Systems
Yuxia Pan, Kaizhou Gao, Zhiwu Li, Naiqi Wu
Summary: A distributed flow-shop scheduling problem with lot-streaming is addressed in this paper. A biobjective mathematic model is developed and an improved Jaya algorithm is proposed to solve the problem. Experimental results show that the strategies designed for the algorithm are competitive for solving the problem with makespan and total energy consumption criteria.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Hao-Xiang Qin, Yu-Yan Han, Biao Zhang, Lei-Lei Meng, Yi-Ping Liu, Quan-Ke Pan, Dun-Wei Gong
Summary: With the development of national economies, attention has been drawn to the issues of energy consumption and pollution emissions in manufacturing. Most existing research has focused on reducing economic costs and energy consumption, with limited studies on the energy-efficient hybrid flow shop scheduling problem, especially with blocking constraints. This paper presents a mathematical model for the blocking hybrid flow shop problem with an energy-efficient criterion and proposes a modified Iterative Greedy algorithm to optimize the model.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yibing Li, Cheng Liao, Lei Wang, Yu Xiao, Yan Cao, Shunsheng Guo
Summary: This paper proposes a hybrid algorithm RL-ABC, which combines Reinforcement Learning and Artificial Bee Colony, to solve the difficult and unstable FJSP-LS problem. The algorithm divides the solution into two stages and uses initialization, local search strategies, and reinforcement learning to determine the best dispatch and sublot schemes. Experimental results show that RL-ABC algorithm outperforms other compared algorithms in terms of effectiveness and robustness.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Fawu Xie, Lingling Li, Li Li, Yangpeng Huang, Zaixiang He
Summary: This study investigated the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which is rarely considered in the literature. A multi-objective mixed-integer linear programming model (MILP) was formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. In order to solve the problem efficiently, a decomposition based multi-objective Jaya algorithm (MOJA/D) was proposed, which combines the Jaya algorithm and decomposition idea.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Industrial
Jiaxin Fan, Chunjiang Zhang, Weiming Shen, Liang Gao
Summary: This paper investigates a flexible job shop scheduling problem with lot-streaming and machine reconfigurations (FJSP-LSMR) for the total weighted tardiness minimisation. A matheuristic method with a variable neighbourhood search component (MH-VNS) is developed to address the problem. The proposed MH-VNS can well balance the solution quality and computational costs for reasonably integrating the GA- and MILP-based local search strategies.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Yuxia Pan, Kaizhou Gao, Zhiwu Li, Naiqi Wu
Summary: This paper addresses a distributed lot-streaming permutation flow shop scheduling problem and proposes five meta-heuristics to solve it. Experimental results show that the artificial bee colony algorithm with improved strategies exhibits the best competitiveness for solving the problem with makespan criteria.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Hong Lu, Fei Qiao
Summary: This article focuses on energy saving in hybrid flow shop scheduling problem with batch production, proposing an efficient adaptive genetic algorithm which is validated through experiments for the effectiveness of the model and efficiency of the algorithm.
Article
Engineering, Industrial
Lixin Cheng, Qiuhua Tang, Liping Zhang
Summary: This paper investigates the mixed-model assembly job-shop scheduling problem with lot streaming and proposes a mathematical model and an adaptive simulated annealing algorithm to solve the problem. Experimental results show that the algorithm performs well.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jingcao Cai, Deming Lei
Summary: In this paper, a cooperated shuffled frog-leaping algorithm (CSFLA) is proposed to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption for the distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP). Extensive experiments validate the promising advantages of CSFLA in solving DEHFSP.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Hongjing Wei, Shaobo Li, Huafeng Quan, Dacheng Liu, Shu Rao, Chuanjiang Li, Jianjun Hu
Summary: In recent years, there has been growing concern about the environmental impact of traditional manufacturing, particularly in terms of energy consumption and carbon dioxide emissions. Production scheduling, along with machine status switching, can effectively reduce total energy consumption in manufacturing plants. The use of a multi-objective optimization algorithm such as U-NSGA-III has been shown to significantly decrease non-processing energy consumption and improve overall energy efficiency in manufacturing processes.
Article
Computer Science, Theory & Methods
Jianhui Mou, Peiyong Duan, Liang Gao, Xinhua Liu, Junqing Li
Summary: This paper introduces an energy-efficient distributed permutation flow-shop inverse scheduling problem and proposes an effective hybrid collaborative algorithm to meet dynamic market demand. By improving heuristic and random methods for population initialization, the algorithm's performance is enhanced. The algorithm achieves a balance between global exploration and local development capability through a double-population cooperative search link based on learning mechanism.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Operations Research & Management Science
Shi Qiang Liu, Erhan Kozan, Mahmoud Masoud, Debiao Li, Kai Luo
Summary: The study introduces a critical problem in open-pit mining: how to determine appropriate sizes of mining jobs and optimize the allocation and sequencing of mining equipment. By introducing a new integrated planning-scheduling problem and combining it with the theory of parallel-machine flow shop scheduling, an innovative math-heuristic approach is proposed to solve this problem efficiently.
ANNALS OF OPERATIONS RESEARCH
(2022)
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, 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)