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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 iterated greedy algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第4期30卷 1241-1252页

作者： Yuan, Shuaipeng Wang, Bailin Li, Tike Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry.

关键词： group processing iterated greedy algorithm parallel machine scheduling programming model re-entrant

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An iterated greedy algorithm for distributed flowshops with tardiness and rejection costs to maximize total profit

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Lin, Zhen Jing, Xue-Lei Jia, Bao-Xian Liaocheng Univ Sch Comp Sci & Technol Liaocheng 252000 Peoples R China Liaocheng Univ Network Informat Ctr Liaocheng 252000 Peoples R China

Distributed permutation flowshop scheduling problems (DPFSP) have become research hotspots. However, a DPFSP with tardiness and rejection costs (DPTR), which has important practical significance, has not yet been addressed. As manufacturers are most concerned about the maximization of the total profit, this study addresses the DPFSP to maximize the total profit. A mixed-integer linear programming model is used to describe the DPTR. An iterated greedy algorithm named IG_TR is proposed. IG_TR generates initial solutions using an improved NEH heuristic using four rules based on the deadline, due date, tardiness and rejection costs. For IG_TR, a dynamic number of jobs are removed from factories in the destruction phase. In the reconstruction phase, the removed jobs and those waiting to be processed are selected to be inserted into appropriate positions in the factories. Two local search methods are designed to improve the quality of solutions generated in the reconstruction phase. During the acceptance phase, a restart mechanism is proposed to avoid falling into local optimal. The experimental results of the component analysis demonstrate the effectiveness of IG_TR. Several comprehensive experiments show that IG_TR outperforms five related algorithms.

关键词： Distributed flowshops iterated greedy algorithm Total profit Local search

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A cooperative population-based iterated greedy algorithm for distributed permutation flowshop group scheduling problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Zhao, Hui Pan, Quan-Ke Gao, Kai-Zhou Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Peoples R China

This paper studies the distributed permutation flowshop group scheduling problem (DPFGSP) with the consideration of minimizing total flowtime (TF), which has important applications in the modern manufac-turing process. Based on the characteristics of the problem, a cooperative population-based iterated greedy (CPIG) algorithm is proposed by combining the advantages of the divide-and-rule policy, population-based evolution and iterated greedy algorithm. The CPIG divides the DPFGSP into two coupled sub-problems of group scheduling sub-problem and job scheduling sub-problem, and starts with a single population for simplicity. Unlike in the traditional cooperative co-evolutionary algorithms, the two-coupled sub-problems are addressed with a certain probability that can be determined in favor of solving the whole scheduling problem. Some advanced technologies are used, including the constructive heuristics based initialization, the critical factories based destruction and construction, the new best solution based population updating mechanism. The comprehensive experimental evaluation of 810 instances shows that the CPIG algorithm performs much better than the five state-of-the-art metaheuristics in the literature which are closely related to the considered scheduling problem.

关键词： Distributed permutation flowshop Group scheduling Total flowtime iterated greedy algorithm Co-evolutionary

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An effective self-adaptive iterated greedy algorithm for a multi-AGVs scheduling problem with charging and maintenance

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期216卷

作者： Zou, Wen-qiang Pan, Quan-ke Meng, Lei-lei Sang, Hong-yan Han, Yu-yan Li, Jun-qing Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci 1 Hunan Rd Liaocheng 252000 Peoples R China

The automatic guided vehicle (AGV) scheduling problem in matrix manufacturing workshop has been a research hotspot in recent years because of its wide industrial applications. However, the research on multi-AGVs scheduling problem with charging and maintenance (MAGVSCM) is rarely reported. In the paper, the MAGVSCM is investigated to reduce the total cost composed of travel cost, penalty cost and vehicle cost. To this end, a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm are pro-posed. Based on the problem characteristics, a new solution presentation and the solution accelerated evaluation method are presented and applied in the SAIG. In the SAIG, several nearest-neighbor-based improved heuristics are proposed and used to generate an initial solution. A destruction procedure with self-adaptive strategy is developed to enhance the exploration capability of the algorithm. A merging local search method is used to reduce the number of AGVs as much as possible without lowering the total cost. An acceptance criterion with restart strategy is designed to determine the current solution in the next iteration. A large number of experi-mental results show that the proposed algorithm significantly outperforms the existing algorithms in solving the problem under consideration.

关键词： Automated guided vehicle Scheduling iterated greedy algorithm Charging Maintenance

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A cooperative iterated greedy algorithm for the distributed flowshop group robust scheduling problem with uncertain processing times

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Wang, Zhi-Yuan Pan, Quan-Ke Gao, Liang Jing, Xue-Lei Sun, Qing Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Liaocheng Univ Network Informat Ctr Liaocheng 252000 Peoples R China

This paper studies a distributed flowshop group robust scheduling problem with uncertain processing times, which has great significance in actual production activities. First, we formulate the problem and establish a robust mathematical model with an expect-risk rule. Second, we develop an effective cooperative iterated greedy (CIG) algorithm to address the studied problem. In the CIG, we propose a heuristic method with two modified sequence rules to obtain an initial solution. We present a dummy scenario method to reduce the complexity of the multi-scenario environment and speed up the process of convergence. We utilize different iterated greedy processes to optimize the family scheduling sub-problem and job scheduling sub-problem respectively. In each iterated greedy process, we design the corresponding operators based on the problem-specific characteristics. We also propose a cooperation mechanism to link the iterated greedy processes to emphasize the coupling rela-tionship between the two sub-problems. Finally, we conduct comparative and comprehensive evaluation ex-periments by comparing the CIG with six high-performing algorithms in the literature. The results indicate that the proposed CIG significantly outperforms the other competitors from the average relative deviation index.

关键词： Distributed flowshop Uncertain processing time Group scheduling iterated greedy algorithm

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An effective two-stage iterated greedy algorithm for distributed flowshop group scheduling problem with setup time

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Wang, Yuhang Han, Yuyan Wang, Yuting Li, Junqing Gao, Kaizhou Liu, Yiping Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Macao Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

The distributed flow shop group scheduling problem (DFGSP) has wide industrial applications. Due to the strong coupling of DFGSP, three issues should be solved, such as assigning groups to factories, arranging the sequence of groups in each factory and scheduling the sequence of jobs in each group. Meanwhile, the calculation for the objective has a high time complexity. To solve the above problems, we first build a mixed -integer linear programming model of DFGSP and verify its correctness by using the Gurobi solver. By exploring the implicit characteristics of the problem, two rapid evaluation methods based on group insertion and job insertion are designed to accelerate the evaluation of the objective. Then, an effective two-stage iterated greedy algorithm (tIGA) is proposed to solve the above three coupled subproblem. In the proposed tIGA, two intra-factory and inter-factory cooperative neighborhood search strategies and two intra-group and inter-group enhanced search strategies are proposed, respectively, to improve the search breadth and depth. The results of comprehensive statistical experiments on 810 test instances show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage increase values, and demonstrates the effectiveness of the proposed tIGA.

关键词： Group scheduling Distributed flow shop iterated greedy algorithm Makespan

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Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

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HELIYON 2023年第9期9卷 e20133页

作者： Alweshah, Mohammed Aldabbas, Yasmeen Abu-Salih, Bilal Oqeil, Saleh Hasan, Hazem S. Alkhalaileh, Saleh Kassaymeh, Sofian Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun Te Al Salt Jordan Al Balqa Appl Univ Fac Agr Technol Dept Plant Prod & Protect Al Salt Jordan Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Sci Amman Jordan Aqaba Univ Technol Fac Informat Technol Software Engn Dept Aqaba Jordan

Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the iterated greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification

关键词： Gene selection Feature selection Medical diagnosis iterated greedy algorithm Black widow optimization Metaheuristic hybridization

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iterated greedy algorithm for Solving a Hybrid Flow Shop Scheduling Problem with Reentrant Jobs 32

Iterated Greedy Algorithm for Solving a Hybrid Flow Shop Sch...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang, Qi Tian, Zheng Wang, Sen Liu, Shixin Northeast Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Shanghai Baosight Software Co Ltd Shanghai 201900 Peoples R China

ISBN: (纸本)9781728158556

This paper studies a hybrid flow shop scheduling problem with reentrant jobs from a copper strip cold rolling process in copper plants. The objective is to find a near-optimal job sequences to minimize makespan. A mixed integer program is created to describe this problem. An iterated greedy heuristic algorithm is proposed to solve this problem. Practical instances are collected from a Chinese copper plant to test the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the large-scale instances.

关键词： Hybrid flow shop scheduling Reentrant jobs iterated greedy algorithm

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An effective multi-restart iterated greedy algorithm for multi-AGVs dispatching problem in the matrix manufacturing workshop

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期252卷

作者： Liu, Zi-Jiang Sang, Hong-Yan Zheng, Chang-Zhe Chi, Hao Gao, Kai-Zhou Han, Yu-Yan Liaocheng Univ Sch Comp Liaocheng 252059 Peoples R China

An excellent automated guided vehicle (AGV) scheduling scheme can effectively improve the productivity of automated manufacturing factories and reduce costs, so the AGV dispatching problem (AGVDP) has become a research hotspot. This paper studies AGVDP with AGV capacity and the latest delivery time to reduce the cost of the total objective consisting of AGV cost, travel cost, and penalty cost. To this end, a mixed-integer linear programming model and a multi-restart iterated greedy (MRIG) algorithm are proposed. According to the characteristics of the problem, an improved nearest neighbor-based heuristic algorithm (INNH) is proposed to generate an initial solution. INNH includes a balanced two-factor selection strategy and a multiple-try insertion strategy. In the MRIG, the destruction stage has been improved to have an adaptive parameter. To enhance the quality of the initial solution and the depth of the evolution, two improved reference local searches are proposed. Furthermore, a multi-restart strategy with a checking mechanism and five restart methods is designed to avoid MRIG falling into the local optimum. The results of sufficient statistical experiments show that the MRIG is obviously superior to the existing algorithms when solving AGVDP.

关键词： Automated guided vehicle Dispatching iterated greedy algorithm Matrix manufacturing workshop

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