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An effective iterated greedy algorithm for PCBs grouping problem to minimize setup times

APPLIED SOFT COMPUTING 2021年 112卷 107830-107830页

作者： Huang, Jiang-Ping Pan, Quan-Ke Gao, Liang Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 PR Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The printed circuit boards (PCBs) grouping problem (PGP) is an essential part of PCBs assembly that has attracted much attention in recent years. In this paper, we propose a mathematical model and an iterated greedy (IG) algorithm, called IGP, for solving the PGP with setup time criterion. Based on the problem characteristics, two speed-up theorems are proposed and applied in the IGP. In the IGP, a new solution representation consisting of PCBs assignment sequence and component assignment sequence is adapted, and a heuristic based on PCB pairs and an iterated scheme is presented to create an initial solution. Then a merge operator is introduced to further improve the initial solution. A local search method based on the shift and swap operators is applied to improve solutions from the construction phase. To ensure the diversity of solutions, an acceptance criterion with probability is presented. Additionally, a detailed design experiment is carried out to calibrate the parameters for the presented IGP algorithm. The IGP is assessed by comparing it with the state-of-the-art algorithms in the literature. The experimental results show that the proposed IGP achieves the best performance among the tested methods for PGP. (C) 2021 Elsevier B.V. All rights reserved.

关键词： PCBs Grouping problem iterated greedy algorithm Metaheuristics

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An improved iterated greedy algorithm for the no-wait flow shop scheduling problem with makespan criterion

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第7-8期38卷 778-786页

作者： Pan, Quan-Ke Wang, Ling Zhao, Bao-Hua Univ Sci & Technol China Dept Comp Sci Hefei 230026 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

An improved iterated greedy algorithm (IIGA) is proposed in this paper to solve the no-wait flow shop scheduling problem with the objective to minimize the makespan. In the proposed IIGA, firstly, a speed-up method for the insert neighborhood is developed to evaluate the whole insert neighborhood of a single solution with (n-1)(2)supercript stop neighbors in time O(n(2)), where n is the number of jobs;secondly, an improved Nawaz-Enscore-Ham (NEH) heuristic is presented for constructing solutions in the initial stage and searching process;thirdly, a simple local search algorithm based on the speed-up method is incorporated into the iterated greedy algorithm to perform exploitation. The computational results based on some well-known benchmarks show that the proposed IIGA can obtain results better than those from some existing approaches in the literature.

关键词： no-wait flow shop makespan iterated greedy algorithm speed-up method insert neighborhood local search

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A population-based iterated greedy algorithm for the minimum weight vertex cover problem

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APPLIED SOFT COMPUTING 2012年第6期12卷 1632-1639页

作者： Bouamama, Salim Blum, Christian Boukerram, Abdellah Univ Politecn Cataluna ALBCOM Res Grp Barcelona Spain Univ Msila Dept Comp Sci Msila 28000 Algeria Ferhat Abbas Univ Dept Comp Sci Fac Sci Setif 19000 Algeria

Given an undirected, vertex-weighted graph, the goal of the minimum weight vertex cover problem is to find a subset of the vertices of the graph such that the subset is a vertex cover and the sum of the weights of its vertices is minimal. This problem is known to be NP-hard and no efficient algorithm is known to solve it to optimality. Therefore, most existing techniques are based on heuristics for providing approximate solutions in a reasonable computation time. Population-based search approaches have shown to be effective for solving a multitude of combinatorial optimization problems. Their advantage can be identified as their ability to find areas of the space containing high quality solutions. This paper proposes a simple and efficient population-based iterated greedy algorithm for tackling the minimum weight vertex cover problem. At each iteration, a population of solutions is established and refined using a fast randomized iterated greedy heuristic based on successive phases of destruction and reconstruction. An extensive experimental evaluation on a commonly used set of benchmark instances shows that our algorithm outperforms current state-of-the-art approaches. (C) 2012 Elsevier B. V. All rights reserved.

关键词： iterated greedy algorithm Minimum weight vertex cover problem Population-based techniques

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An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

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APPLIED SOFT COMPUTING 2015年 30卷 604-613页

作者： Ding, Jian-Ya Song, Shiji Gupta, Jatinder N. D. Zhang, Rui Chiong, Raymond Wu, Cheng Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Alabama Coll Business Adm Huntsville AL 35899 USA Nanchang Univ Sch Econ & Management Nanchang 330031 Peoples R China Univ Newcastle Sch Design Commun & Informat Technol Callaghan NSW 2308 Australia

This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms. (C) 2015 Elsevier By. All rights reserved.

关键词： No-wait flow shop iterated greedy algorithm Tabu search Makespan

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An iterated greedy algorithm with acceleration of job allocation probability for distributed heterogeneous permutation flowshop scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 88卷

作者： Li, Haoran Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl Key Lab Intelligent Mfg Equipment & Technol State Key Lab Digital Man Wuhan 430074 Hubei Peoples R China

Distributed manufacturing paradigm is becoming one of the dominant manufacturing models today. Heterogeneities between factories exist in fact due to the differences in process flexibility and machine performance. Most research focuses on identical distributed factories and overlooks the influence of heterogeneous factories on the subproblem of job allocation. This paper investigates a distributed heterogeneous permutation flowshop scheduling problem (DHPFSP) to minimize the makespan, and a mixed -integer linear programming (MILP) model is established. Building upon a special phenomenon of job assignment to a specific heterogeneous factory in DHPFSP, an iterated greedy algorithm with acceleration of job allocation probability (IGP) is proposed. To utilize this phenomenon, a job allocation probability matrix is introduced to describe the trend of job allocation, and a fast pass strategy with job allocation probability is suggested to speed up the algorithm search process. Furthermore, to generate a high -quality initial solution quickly for IGP, an NEH-based heuristic framework for distributed heterogeneous factory environment (NEHDHF) is proposed. In NEHDHF, several distinct initial job sequence generation strategies and a new first -f job allocation strategy are proposed for obtain a better initial solution. In computational experiments, there are 720 test instances designed and publicized. The IGP achieves 535 best solutions out of 720 instances. Statistically sound results demonstrate the effectiveness of the presented algorithms for the considered problem.

关键词： Distributed heterogeneous permutation flowshop Acceleration of job allocation probability NEH-based heuristic framework iterated greedy algorithm

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Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Yu, Fei Lu, Chao Zhou, Jiajun Yin, Lvjiang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (TTD). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality;(2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution;(3) A knowledge-based destruction and construction is designed to improve the exploration capability;(4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors.

关键词： Distributed hybrid flow shop scheduling problem Assembly shop problem Dual-resource constraints iterated greedy algorithm Mixed-integer linear programming model

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An iterated greedy algorithm for the single-machine total weighted tardiness problem with sequence-dependent setup times

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第3期45卷 351-362页

作者： Deng, Guanlong Gu, Xingsheng E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

This article presents an enhanced iterated greedy (EIG) algorithm that searches both insert and swap neighbourhoods for the single-machine total weighted tardiness problem with sequence-dependent setup times. Novel elimination rules and speed-ups are proposed for the swap move to make the employment of swap neighbourhood worthwhile due to its reduced computational expense. Moreover, a perturbation operator is newly designed as a substitute for the existing destruction and construction procedures to prevent the search from being attracted to local optima. To validate the proposed algorithm, computational experiments are conducted on a benchmark set from the literature. The results show that the EIG outperforms the existing state-of-the-art algorithms for the considered problem.

关键词： scheduling heuristics single machine tardiness iterated greedy algorithm

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Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced iterated greedy algorithm

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Complex System Modeling and Simulation 2023年第4期3卷 282-306页

作者： Yong Wang Yuting Wang Yuyan Han Junqing Li Kaizhou Gao Yusuke Nojima School of Computer Science Liaocheng UniversityLiaocheng 252000China School of Computer Science Shandong Normal UniversityJinan 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant ***,in actual scheduling,some adjacent machines do not have buffers between them,resulting in *** paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production *** solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi ***,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking *** balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are ***,each factory is mutually independent,and the movement within one factory does not affect the *** view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the ***,two shaking strategies are incorporated into the algorithm to mitigate premature *** advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

关键词： blocking distributed hybrid flow shop neighborhood search iterated greedy algorithm

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A novel iterated greedy algorithm for detecting communities in complex network

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SOCIAL NETWORK ANALYSIS AND MINING 2020年第1期10卷 1-17页

作者： Li, Wenquan Kang, Qinma Kong, Hanzhang Liu, Chao Kang, Yunfan Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Shandong Peoples R China Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Community structure is one of the most important properties in complex networks. Detecting such communities plays an important role in a wide range of applications such as information sharing and diffusion, recommendation, and classification. In this paper, we propose a novel iterated greedy algorithm for detecting communities in complex networks. The algorithm is based on an iterative process that combines a destruction phase and a reconstruction phase. During the destruction phase, the algorithm destructs community indexes of a certain percent nodes having lower modularity contribution. In the reconstruction phase, their community indexes are reconstructed using the well-known Louvain construction heuristic. A local search procedure can be applied after the reconstruction phase to improve the performance of the algorithm. Experiments on the computer-generated networks and real-world networks show that our algorithm is very efficient and competitive compared with several state-of-the-art methods.

关键词： Community detection iterated greedy algorithm Modularity contribution Louvain heuristic

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An Improved Multi-Objective iterated greedy algorithm for Energy-Efficient Distributed Permutation Flowshop Scheduling Problem with Sequence-dependent Setup Times 36

An Improved Multi-Objective Iterated Greedy Algorithm for En...

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36th Chinese Control and Decision Conference (CCDC)

作者： Zhong, Quan Yu, Yang Hang, YanZhao Shenyang Aerosp Univ Sch Automat Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Distributed energy-efficient scheduling has gained a lot of focus in industrial production in recent years. However, energy-efficient distributed permutation flowshop scheduling problem (EEDPFSP) with sequence-dependent setup time is closer to the actual manufacturing environment and has not attracted much attention so far. In this paper, energy-efficient distributed permutation flowshop scheduling problem with sequence-dependent setup time is investigated. An improved multi-objective NEH heuristic and an improved multi-objective iterated greedy algorithm (IMOIG) are proposed to minimize both total flowtime and total energy consumption. Effective construction, destruction and local search procedures are proposed in combination with a speed-scaling framework. Based on the sequence characteristics of the problem, the method of job block moving is proposed for local search. Finally, the effectiveness of the proposed algorithm is demonstrated through computational experiments by comparing with other four algorithms.

关键词： Energy-efficient distributed scheduling Sequence dependent iterated greedy algorithm Multi-objective

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