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36th Chinese Control and Decision Conference (CCDC)

作者： Sun, Weiping Liu, Shixin Shanghai Baosight Software Co Ltd Informatizat Business Dept Shanghai 201999 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

With the rapid improvement of the overall manufacturing level in China, the demand for steel products by downstream manufacturing enterprises is more personalized. This trend has brought greater difficulties to the production planning and scheduling of steel enterprises. In order to solve production scheduling problems of continuous annealing process in cold rolling, a mathematical model is established by converting violations of width jump, thickness jump and annealing temperature jump into penalty values. An iterative greedy algorithm is designed according to the characteristics of the model. The model and algorithm are verified and tested by taking use of actual production instances of iron and steel enterprises. The experimental results show that the model is correct and the algorithm is effective. Scheduling plans obtained by the proposed algorithm is obviously better than scheduling plans based on human experience.

关键词： cold rolling continuous annealing production scheduling mathematical model iterative greedy algorithm

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Energy-efficient optimization for distributed blocking hybrid flowshop scheduling: a self-regulating iterative greedy algorithm under makespan constraint

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OPTIMIZATION AND ENGINEERING 2025年第1期26卷 431-478页

作者： Wang, Yong Han, Yuyan Wang, Yuting Liu, Yiping Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

This paper investigates an energy-efficient distributed blocking hybrid flowshop scheduling problem, constrained by the makespan upper-bound criterion. This problem is an extension of the distributed hybrid flowshop scheduling problem and closely resembles practical production scenarios, denoted as DHFmblock epsilon TEC/Cmax\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DHF_{m} \left| {block} \right|\varepsilon \left( {{{TEC} \mathord{\left/ {\vphantom {{TEC} {C_{{max}} }}} \right. \kern-\nulldelimiterspace} {C_{{max}} }}} \right)$$\end{document}. Initially, we formulate the issue into a mixed integer linear programming (MILP) model that reflects its unique characteristics and leverage the Gurobi solver for validation purposes. Building upon this groundwork, we develop a self-regulating iterative greedy (SIG) algorithm, designed to autonomously fine-tune its strategies and parameters in response to the quality of solutions derived during iterative processes. Within the SIG, we design a double-layer destruction-reconstruction, accompanied by a self-regulating variable neighborhood descent strategy, to facilitate the exploration of diverse search spaces and augment the global search capability of the algorithm. To evaluate the performance of the proposed algorithm, we implement an extensive series of simulation experiments. Based on the experimental result, the average total energy consumption and relative percentage increase obtained by SIG are 2.12 and 82% better than the four comparison algorithms, respectively. These statistics underscore SIG's superior performance in addressing DHFmblock epsilon TEC/Cmax\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setl

关键词： Energy-efficient Distributed hybrid flowshop Blocking iterative greedy algorithm

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An iterative greedy algorithm for Solving a Multiobjective Distributed Assembly Flexible Job Shop Scheduling Problem With Fuzzy Processing Time

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第5期55卷 2302-2315页

作者： Zhao, Fuqing Du, Yuqing Zhuang, Changxue Wang, Ling Yu, Yang Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China

Deterministic processing time are no longer applicable under realistic circumstances because of the uncertainties involved in manufacturing and production processes. The present study aims to address a multiobjective distributed assembly flexible job shop scheduling problem with type-2 fuzzy time (DAT2FFJSP), focusing on the optimization objectives of minimizing the makespan and total energy consumption. To address this problem, a mixed-integer linear programming model is presented. Then, a population-based iterative greedy algorithm (PBIGA) with a Q-learning mechanism is proposed, which possesses the following characteristics: 1) a hybrid initialization method is used to generate the population;2) six local search operators, crossover operators, and mutation operators are applied to explore and exploit the solution space;and 3) the Q-learning mechanism intelligently utilizes historical information on the success of local search operator updates to determine the most suitable perturbation operator;and 4) an energy-saving strategy is applied to improve the candidate solutions. Finally, the effectiveness of the proposed components is validated through extensive experiments that are conducted on 30 instances. The PBIGA outperforms the state-of-the-art algorithms on the DAT2FFJSP.

关键词： Q-learning Flexible job shop scheduling iterative greedy algorithm Q-learning iterative greedy algorithm type-2 fuzzy processing time type-2 fuzzy processing time

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An iterative greedy algorithm With Q-Learning Mechanism for the Multiobjective Distributed No-Idle Permutation Flowshop Scheduling

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第5期54卷 3207-3219页

作者： Zhao, Fuqing Zhuang, Changxue Wang, Ling Dong, Chenxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Qingdao Hengxing Univ Sci & Technol Dept Automot Engn BNRIST Qingdao 266100 Peoples R China

The distributed no-idle permutation flowshop scheduling problem (DNIPFSP) has widely existed in various manufacturing systems. The makespan and total tardiness are optimized simultaneously considering the variety of scales of the problems with introducing an improved iterative greedy (IIG) algorithm. The variable neighborhood descent (VND) algorithm is applied to the local search method of the iterative greedy algorithm. Two perturbation operators based on the critical factory are proposed as the neighborhood structure of VND. In the destruction phase, the scale of the destruction varies with the size of the problem. An insertion operator-based perturbation strategy sorts the undeleted jobs after the destruction phase. The Q -learning mechanism for selecting the weighting coefficients is introduced to obtain a relatively small objective value. Finally, the proposed algorithm is tested on a benchmark suite and compared with other existing algorithms. The experiments show that the IIG algorithm obtained more satisfactory results.

关键词： Distributed permutation no-idle flowshop iterative greedy algorithm makespan total tardiness (TTD)

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A novel collaborative iterative greedy algorithm for hybrid flowshop scheduling problem with batch processing machines and variable sublots

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第11期62卷 4076-4096页

作者： Li, Chengshuai Han, Yuyan Zhang, Biao Wang, Yuting Li, Junqing Gao, Kaizhou Liaocheng Univ Sch Comp Sci Hunan Rd Liaocheng 252000 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

Lot streaming technology enables continuous overlapping operations, which is of great significance in shortening production cycles, reducing unnecessary waiting time, and increasing production capacity. However, the capacity constraint of batch processing machines may lead to inevitable variations in sublots. Therefore, the key focus of our research is to control variations of sublot for maximising benefits. In view of this, we investigate a hybrid flowshop scheduling problem (HFSP) with batch processing machines and variable sublots (HFSP-BVS) integrating sequence-dependent setup times and transportation times. To address HFSP-BVS, a MILP model is first established, and a novel collaborative iterative greedy (NCIG) algorithm is proposed to optimise the cumulative payoffs associated with delivery dates. In NCIG, a collaborative initialisation method by extracting good information from an archive is proposed, and a specific destruction-reconfiguration strategy is designed to control the variations of sublots in the batch processing stage. Furthermore, a dynamic acceptance criterion is designed to balance the algorithm's exploitation and exploration capabilities. Lastly, we conduct comparisons between the NCIG algorithm and five other metaheuristic algorithms on 100 test instances. The results show that NCIG outperforms them by 1.89% and 61.42% on average in terms of the total penalty and RPI values, respectively.

关键词： Variable sublots batch processing machines cumulative payoffs collaboration iterative greedy algorithm

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Energy-Efficient iterative greedy algorithm for the Distributed Hybrid Flow Shop Scheduling With Blocking Constraints

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第5期7卷 1442-1457页

作者： Qin, Haoxiang Han, Yuyan Chen, Qingda Wang, Ling Wang, Yuting Li, Junqing Liu, Yiping Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China Tsinghua Univ Dept Automation Beijing 100084 Peoples R China Shandong Normal Univ Sch Informat & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

With the global energy shortage, climate anomalies, environmental pollution becoming increasingly prominent, energy saving scheduling has attracted more and more concern than before. This paper studies the energy-efficient distributed hybrid flow-shop scheduling problem (DHFSP) with blocking constraints. Our aim is to find the job sequence with low energy consumption as much as possible in a limited time. In this paper, we formulate a mathematical model of the DHFSP with blocking constraints and propose an improved iterative greedy (IG) algorithm to optimize the energy consumption of job sequence. In the proposed algorithm, first, a problem-specific strategy is presented, namely, the global search strategy, which can assign appropriate jobs to the factory and minimize the energy consumption of each processing factory. Next, a new selection mechanism inspired by Q-learning is proposed to provide strategic guidance for factory scheduling. This selection mechanism provides historical experience for different factories. Finally, five types of local search strategies are designed for blocking constraints of machines and sequence to be scheduled. These proposed strategies can further improve the local search ability of the QIG algorithm and reduce the energy consumption caused by blocking. Simulation results and statistical analysis on 90 test problems show that the proposed algorithm is superior to several high-performance algorithms on convergence rate and quality of solution.

关键词： Production facilities Job shop scheduling Energy consumption Search problems Optimal scheduling Time factors Resource management Energy-efficient distributed hybrid flow-shop scheduling blocking constraints iterative greedy algorithm selection mechanism

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A multi-threaded parallel iterative greedy algorithm for distributed flowshop group scheduling problems with preventive maintenance

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2025年第3期19卷 265-286页

作者： Sun, Xiaobin Sang, Hongyan Wu, Wanzhong Zhao, Yasheng Han, Qiuyang Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China

In actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. For the continuity of production machines, this paper investigates the distributed flow shop group scheduling problem with preventive maintenance (DFGSP/PM). In order to minimise the makespan, a mathematical model of DFGSP/PM is developed and a multi-threaded parallel iterative greedy (MPIG) algorithm is proposed. A greedy NEH (GNEH) method is designed to generate the initial solution. In order to couple the two subproblems of DFGSP/PM, a two-stage destruction and reconstruction is designed. A multi-threaded parallel local search strategy (MPLS) is introduced to improve the search efficiency of the MPIG, so that the optimal insertion positions of the groups in the sequence can be searched faster and the two subproblems can be coupled effectively. Effectiveness analysis has demonstrated that the proposed MPIG significantly reduces computation time and expands the search space.

关键词： distributed flowshop scheduling preventive maintenance iterative greedy algorithm group scheduling parallel computing

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Hybrid flow shop scheduling with continuous processing and resource threshold constraints: A case of steel plant

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 284卷

作者： Su, Zhangsheng Deng, Chao Chiong, Raymond Jiang, Sheng-Long Zhang, Kai Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Peoples R China Univ New England Sch Sci & Technol Parramatta NSW 2150 Australia Univ Newcastle Sch Informat & Phys Sci Callaghan NSW 2308 Australia Chongqing Univ Coll Mat Sci & Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China

Many scenarios in the process manufacturing industry, such as production in steel plants, can be modeled through hybrid flow shop scheduling problems. Continuous processing is a crucial constraint in this type of production, playing a vital role in maintaining the continuity of operations. Meanwhile, limited processing resources create threshold constraints that affect the conditions for starting production and lead to new bottlenecks. Therefore, we introduce and study the hybrid flow shop scheduling problem with continuous processing and resource threshold constraints (HFSSP-CPRT). To solve this problem, a population-based iterative greedy algorithm with bidirectional decoding (PBIGA-BD) is proposed. To improve encoding accuracy and decoding efficiency, stages with continuous processing and resource threshold constraints are encoded separately, and bidirectional decoding is applied. Subsequently, six initialization solutions are designed to improve the quality of the initial population. To enhance the diversity of the superior population, an improved destruction-construction method is designed. Then, a hyper-heuristic local search is developed to reduce the blindness of local search. It consists of six neighborhood search actions by considering the characteristics of HFSSP-CPRT. We tested the effectiveness of PBIGA-BD by applying it to an HFSSP-CPRT case of steel plant in China. Comprehensive experimental results showed that the proposed PBIGA-BD is able to outperform several heuristic rules and stateof-the-art algorithms being compared.

关键词： Hybrid flow shop scheduling iterative greedy algorithm Continuous processing Limited auxiliary

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Distributed hybrid flowshop scheduling with consistent sublots under delivery time windows: A penalty lot-assisted iterated greedy algorithm

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EGYPTIAN INFORMATICS JOURNAL 2024年 28卷

作者： Liu, Jinli Han, Yuyan Wang, Yuting Liu, Yiping Zhang, Biao Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Integrating the delivery time windows into the distributed hybrid flow shop scheduling contributes to ensuring the timely delivery of products and enhancing customer satisfaction. In view of this, this study focuses on distributed hybrid flowshop scheduling with consistent sublots under the delivery time windows constraint, denoted as DHFm|lotcs|epsilon(TWET/DTW). However, there exist some challenges of problem model modeling and algorithmic design for the problem to be addressed. Therefore, we first construct a mixed integer linear programming (MILP) model tailored to DHFm|lotcs|epsilon(TWET/DTW) with the aim of minimizing the total weighted earliness and tardiness (TWET). Additionally, we introduce a penalty lot-assisted iterated greedy (PL IG ITI) and idle time insertion to coincide better with delivery time windows, in which a delivery-time-based multi-rule NEH, an adaptive insertion-based reconstruction based on the changing of the delivery status, a trilaminar penalty lot-assisted local search, and an elitist list-based acceptance criterion are designed to save convergence time and reduce the late deliveries attempts. Lastly, we also introduce a completely new method to generate delivery time windows and create 400 distinct instances. Based on the average results from five runs of 400 instances, PL IG ITI demonstrates improvements of 59.0 %, 72.3 %, 76.9 %, and 25.5 % compared to HIGT, DABC, CVND, and IG MR , respectively. When considering the minimum values from each instance, PL IG ITI exhibits enhancements of 59.4 %, 71.8 %, 74.9 %, and 25.4 % over HIGT, DABC, CVND, and IG MR , respectively, it evident that PL IG ITI can effectively solve DHFm|lotcs|epsilon(TWET/DTW).

关键词： Distributed hybrid flow shop Lot streaming iterative greedy algorithm Delivery time windows

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Extrapolation of Antenna Near-Field Measurements Using the iterative greedy algorithms

Extrapolation of Antenna Near-Field Measurements Using the I...

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USNC-URSI Radio Science Meeting / IEEE International Symposium on Antennas and Propagation (AP-S)

作者： Zhu, Ming-Da Sarkar, Tapan K. Zhang, Yu Xidian Univ Sch Elect Engn Xian 710071 Shaanxi Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)9781728106922

There are often missing gaps in the antenna near-field measurements, due to various physical limitations. However, the missing data are indispensable if we want to accurately evaluate the complete far-field pattern by the near-field to far-field transformations. To address this problem, a fast extrapolation method based on the iterative greedy algorithm is proposed to reconstruct the missing part of antenna near-field measurements in this paper. The near-field data is approximated by sparse signals with the Fourier basis, which is recoverable by the greedy pursuits. As the near-field data in this paper is obtained on the spherical measurement surface, the far field of the antenna is calculated by the spherical near-field to far-field transformation with the extrapolated data. Some numerical results are given to demonstrate the applicability of the proposed scheme in extrapolating antenna near-field measurements.

关键词： antenna measurement extrapolation near-field to far-field transformation iterative greedy algorithm

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