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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 population-based iterated greedy algorithm to minimize total flowtime for the distributed blocking flowshop scheduling problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104375-104375页

作者： Chen, Shuai Pan, Quan-Ke Gao, Liang Sang, Hong-yan 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 Peoples R China

We consider the distributed blocking flowshop scheduling problem (DBFSP) which is a meaningful generalization of the blocking flowshop scheduling problem in the distributed production environment. The objective of minimizing the total flowtime is relevant and important in the current dynamic manufacturing environment, but, as far as we know, it has not been investigated in the DBFSP previously. In this paper, a population-based iterated greedy (PBIG) algorithm is proposed to solve the DBFSP with the total flowtime criterion, which takes the advantage of both the population-based search approach and the iterated greedy algorithm. First, an effective constructive heuristic is proposed by integrating two existing constructive approaches to initialize the population with a high level of quality and diversity. Second, three different procedures to generate the offspring solutions are tested for the effective exploration capability, each of which rationally combines the destruction, reconstruction and selection operator. Third, the insertion neighborhood and swap neighborhood are investigated to enhance the local exploitation ability and a hybrid local search procedure that utilizes simultaneously both the two neighborhoods are proposed. The comprehensive experimental evaluation based on a total of 720 well-known instances shows that the proposed algorithms outperform the existing effective algorithms at a significant margin.

关键词： Flowshop Total flowtime Distributed iterated greedy algorithm Blocking

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Regionalization of primary health care units: An iterated greedy algorithm for large-scale instances

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Mendoza-Gomez, Rodolfo Rios-Mercado, Roger Z. Tecnol Monterrey Sch Engn & Sci Eugenio Garza Sada SN Guanajuato 37190 Mexico Univ Autonoma Nuevo Leon UANL Grad Program Elect Engn Ave Univ S-nCd Univ San Nicolas De Los Garza 66455 Nuevo Leon Mexico

In this paper, we study the problem of multi -institutional regionalization of primary health care units. The problem consists of deciding where to place new facilities, capacity expansions for existing facilities, and demand allocation in a multi -institutional system to minimize the total travel distance from demand points to health care units. It is known that traditional exact methods as branch -and -bound are limited to solving small- to medium -size instances of the problem. Given that real world -instances can be large, in this paper we propose an iterated greedy algorithm with variable neighborhood descent search for handling large-scale instances. Within this solution framework, several methods are developed. A greedy constructive method and two deconstruction strategies are developed. Another interesting component is the exact optimization of a demand allocation subproblem that is obtained when the location of facilities is previously fixed. An empirical assessment using real -world data from the State of Mexico's Public Health Care System is carried out. The results demonstrate the effectiveness of the proposed metaheuristic in handling large-scale instances.

关键词： Public health care planning Facility location Metaheuristics iterated greedy algorithm

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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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A simple and effective iterated greedy algorithm for structural balance in signed networks

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2022年第21期36卷 2250129-2250129页

作者： Duan, Wenqiang Kang, Qinma Kang, Yunfan Chen, Jianwen Qin, Qingfeng Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Shandong Peoples R China Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Ctr Big Data Wendeng Weihai 264499 Shandong Peoples R China Ctr Big Data Rushan Weihai 264500 Shandong Peoples R China

Recently, problems related to structural balance have received widespread attention in the research fields of signed networks, such as signed network partition, community detection and correlation clustering. Structural balance problems aim to find a partition of a signed network such that the frustration index, the sum of positive edges between subsets and negative edges within subsets, is minimized, which is known to be nondeterministic polynomial-time (NP)-complete and remains an open challenge. Many heuristic and meta-heuristic algorithms are developed to minimize the frustration index. However, most extant algorithms require quite a few parameters and are not efficient enough for large signed networks. In this study, we present a simple and effective iterated greedy (IG) algorithm for solving the structural balance problems with the objective of frustration minimization. In the algorithm, a constructive greedy heuristic is proposed to generate and reconstruct solutions to the problem with high efficiency. A two-stage local search procedure is designed to exploit the search space at different levels of granularity. An adaptive destruction method is developed to enhance the exploitation ability of the algorithm in the early stage and maintain the exploration ability later. Additionally, two acceleration methods are proposed to help the algorithm get a better performance in large signed networks. The experimental results indicate that the proposed IG algorithm outperforms other meta-heuristics in the literature especially in terms of the computational time.

关键词： Structural balance signed networks frustration index iterated greedy algorithm

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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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Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6361-6381页

作者： Chen, Shuai Pan, Quan-Ke Gao, Liang Miao, Zhong-Hua Peng, Chen Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

In recent years, both distributed scheduling and energy-efficient scheduling have attracted great attention in production systems. This paper studies an energy-efficient distributed blocking flowshop scheduling problem where several heterogeneous factories cooperate to process jobs. A knowledge-based iterated Pareto greedy algorithm (KBIPG) is proposed to minimize simultaneously the makespan and total energy consumption. Based on a speed scaling framework that allows machines to process different jobs at different speed levels or remain in the standby mode, the KBIPG has two stages, where the difference lies in whether to adjust the processing speed. First, two multi-objective insertion procedures are proposed to form construction procedures. Second, we presented an efficient destruction procedure for each stage separately. Third, two local intensification methods are designed based on adjusting machine speeds, including the energy-saving procedure that optimizes the total energy consumption and the speedup-based local search procedure that optimizes the makespan. The KBIPG algorithm starts with generating solutions under various initial machine speed matrixes with different levels and then goes through a two-stage loop based on the proposed procedures. Computational experiments and comparisons with five algorithms demonstrate the effectiveness of the proposed KBIPG algorithm.

关键词： Energy-efficient scheduling Distributed blocking flowshop Multi-objective iterated greedy algorithm

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A parallel-optimized iterated greedy algorithm for blocking hybrid flow shop scheduling problem

A parallel-optimized iterated greedy algorithm for blocking ...

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第35届中国控制与决策会议

作者： Yong Wang Yuting Wang Chengshuai Li Chenyao Zhang Yuhang Wang School of Computer Science Liaocheng University

We study the blocking hybrid flow shop scheduling problem(BHFSP) aiming at maximum completion time in this paper,which has practical implications in the manufacturing industry.A paralleloptimized iterated greedy(POIG) algorithm is presented to optimize the objective of minimizing the maximum completion *** whole POIG algorithm adopts a parallel scheme to increase the solution ***,the NEH and MME heuristics are used to initialize the two solutions;subsequently,to strengthen the performance of the POIG and make machine blocking timeless,two permutation-based local perturbation strategies are proposed;using simulated annealing(SA)algorithm to increase the solution *** proposed algorithm is tested against other algorithms on 100 instance sets of different *** final simulation experiments show that the POIG performs better than other comparison algorithms,and it can be considered as an effective solution to the BHFSP.

关键词： Blocking parallel optimization iterated greedy algorithm hybrid flow shop scheduling problem makespan

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