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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： JAMES C.CHEN MABEL H.HSAIO CHUN-CHIEH CHEN CHENG-JU SUN Department of Industrial and Systems Engineering Chung Yuan Christian University Chung Li Taiwan 320 R.O.C

The garment manufacturing is a traditional and fashion industry, that is globally competitive and customer centric. The most critical operation process is sewing, as it generally involves a great number of operations. The aim of assembly line balancing planning in sewing lines is to assign task to the workstation in order that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines in a reasonable *** paper presents a grouping genetic algorithm (GGA) for assembly line balancing problem of sewing lines in garment industry. GGA was first developed by Falkenauer in 1992 as a type of GA which exploits the special structure of grouping problem, and overcomes the drawbacks of GA. GGA allocates workload among machines as evenly as possible, so the minimum mean absolute deviations (MAD) can be minimized. The performance is verified through solving two real problems in garment industry. The computational results reveal that GGA outperforms GA in both simple and complex problems by 13.81％ and 8.81％, respectively. This shows GGA's effectiveness in solving ALBP.

关键词： Assembly Line Balancing Problem grouping genetic algorithm Garment Industry

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An experimental approach to design deterministic and adaptive control schemes for grouping genetic algorithms

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Neural Computing and Applications 2025年 1-30页

作者： Flores-Torres, Leonardo Quiroz-Castellanos, Marcela Ramos-Figueroa, Octavio Amador-Larrea, Stephanie Artificial Intelligence Research Institute Universidad Veracruzana Veracruz Xalapa Mexico

genetic algorithms can solve many complex problems, including designing and optimizing machine learning techniques like neural networks, as well as challenges in production management and engineering. Paradoxically, the design of these methods, which aim to solve optimization problems efficiently, depends, in turn, on their components’ design and optimal configuration. grouping genetic algorithms (GGAs) have excelled in performance and adaptability as one of the best metaheuristics for solving combinatorial optimization problems that require finding optimal partitions of sets of items;however, their performance relies heavily on the proper configuration of parameters like population size, crossover rate, and mutation rate. This paper presents an experimental approach for automated parameter control of GGAs, looking for a dynamic adjustment, enhancing the algorithm’s ability to explore the solution space efficiently, avoid premature convergence, and improve overall solution quality. A comprehensive set of deterministic and adaptive control schemes is introduced for on-line parameter setting in GGAs. The approach is tested by studying three state-of-the-art algorithms for solving complex instances of three NP-hard optimization problems: the grouping genetic algorithm with Controlled Gene Transmission for the One-Dimensional Bin Packing Problem, the grouping genetic algorithm with Intelligent Heuristic Strategies for the Parallel-Machine Scheduling Problem with Unrelated Machines and Makespan Minimization, and the grouping genetic algorithm for Variable Decomposition in Large-Scale Constrained Optimization Problems. The experimental results showed that the proposed approach allows for identifying parameter control schemes that save the extensive task of off-line parameter fine-tuning, obtaining a robust and competitive performance on different benchmark sets, and outperforming the published results for some classes of instances. © The Author(s), under exclusive licence

关键词： Adaptive control Deterministic control grouping genetic algorithm Parameter control Parameter setting

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grouping genetic algorithms for solving single machine multiple orders per job scheduling problems

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ANNALS OF OPERATIONS RESEARCH 2015年第1期235卷 709-739页

作者： Sobeyko, Oleh Moench, Lars Univ Hagen Dept Math & Comp Sci D-58097 Hagen Germany

In this paper, we discuss scheduling problems motivated by a real-world production setting found in 300-mm semiconductor wafer fabrication facilities (wafer fabs). Front opening unified pods (FOUPs) transfer wafers in 300-mm wafer fabs. Several orders can be grouped in one FOUP. We study lot and single item processing environments. The total weighted tardiness (TWT) and the weighted number of tardy orders (WNTO) objectives are considered. Mixed integer programming (MIP) formulations are presented for the scheduling problems. We prove that the researched scheduling problems are NP-hard. grouping genetic algorithms (GGAs) are proposed to form the content of the FOUPs. We compare the performance of the GGAs with another GA from the literature available for the problem with TWT measure based on randomly generated problem instances. It turns out that the GGA outperforms the heuristic from the literature for both environments. For the WTNO measure, we assess the performance of the GGA approach using MIP formulations for small-size problem instances and a GA-based heuristic for large-size problem instances. Again, the GGA performs well with respect to solution quality and amount of computing time.

关键词： Scheduling Multiple orders per job grouping genetic algorithm Semiconductor manufacturing Computational experiments

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Evaluating performance advantages of grouping genetic algorithms

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2005年第1期18卷 1-12页

作者： Brown, EC Sumichrast, RT Virginia Polytech Inst & State Univ Dept Business Informat Technol Blacksburg VA 24061 USA Louisiana State Univ Ourso Coll Business Adm Baton Rouge LA 70803 USA

The genetic algorithm (GA) and a related procedure called the grouping genetic algorithm (GGA) are solution methodologies used to search for optimal solutions in constrained optimization problems. While the GA has been successfully applied to a range of problem types, the GGA was created specifically for problems involving the formation of groups. Falkenauer (JORBEL-Belg. J. Oper. Res. Stat. Comput. Sci. 33 (1992) 79), the originator of the GGA. and subsequent researchers have proposed reasons for expecting the GGA to perforin more efficiently than the GA on grouping problems. Yet, there has been no research published to date which tests claims of GGA superiority. This paper describes empirical tests of the performance of GA and GGA in three domains which have substantial, practical importance, and which have been the subject of considerable academic research. Our purpose is not to determine which of these two approaches is better across an entire problem domain. but rather to begin to document practical differences between a standard off-the-shelf GA and a tailored GGA. Based on the level of solution quality desired, it may be the case that the additional time and resources required to design a tailored GGA may not be justified if the improvement in solution quality is only minor or non-existent. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： genetic algorithm grouping genetic algorithm grouping problems constrained optimization bin packing machine-part cell formation assembly line balancing

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Metaheuristics for solving a flexible flow-shop scheduling problem with s-batching machines

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第1期32卷 38-68页

作者： Rocholl, Jens Moench, Lars Univ Hagen Dept Math & Comp Sci D-58097 Hagen Germany

A scheduling problem for a two-stage flexible flow shop with s-batching machines motivated by processes in additive manufacturing is considered. A batch is a group of jobs that are processed together on a single machine. Each job belongs to an incompatible family. Only jobs of the same family can be batched together. A maximum batch size is given. A setup time occurs between different batches. The jobs of a batch are processed in a serial manner, that is, the processing time of a batch is the sum of the processing times of the jobs forming the batch. Batch availability is assumed. A job has a weight, a due date, and a release date. The total weighted tardiness is considered as performance measure. We establish a mixed integer linear programming formulation for this scheduling problem. For large-sized problem instances, an iterative decomposition approach (IDA) is proposed that uses a grouping genetic algorithm or an iterated local search (ILS) scheme to solve the single-stage subproblems. Moreover, an alternative ILS scheme based on a disjunctive graph representation of the problem at hand is designed. Results of computational experiments based on randomly generated problem instances demonstrate that the IDA hybridized with ILS outperforms the two other schemes.

关键词： flow-shop scheduling s-batching, decomposition disjunctive graph Iterated local search grouping genetic algorithm

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An experimental approach to designing grouping genetic algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Ramos-Figueroa, Octavio Quiroz-Castellanos, Marcela Univ Veracruzana Artificial Intelligence Res Inst Xalapa Mexico

grouping problems are a special case of combinatorial problems that emerge in several practical and theoretical situations, where the goal is to find the optimal groups that minimize an objective function. One of the most outstanding metaheuristics to solve them is the grouping genetic algorithm (GGA). Nevertheless, many grouping problems have very complex search spaces with unique and hard features that demand sophisticated search processes;therefore, their solution implies designing GGAs with specific-purpose intelligent heuristic strategies. This work presents an experimental approach to designing high-performance GGAs by analyzing in isolation its components and studying the weaknesses and strengths of its optimization process. Furthermore, we introduce a case study to develop a GGA for the Parallel Machine-Scheduling with Unrelated Machines and Makespan Minimization (R||C-max) problem to show the aspects to consider by implementing the proposed experimental approach. The information generated from this study allowed us to recognize the appropriate intelligent heuristic strategies for every genetic component, giving rise to the grouping genetic algorithm with Intelligent Heuristic Strategies (GGA-IHS). The experimental results showed the usefulness of our proposal since GGA-IHS is about 91% better than the state-of-the-art grouping genetic algorithm with Controlled Genes Transmission (GGA-CGT) for the R||C-max problem. Additionally, the experimental results indicate that the GGA-IHS performance is comparable with state-of-the-art solution methods by using only 10000 generations. The outcome of this work demonstrates the importance of knowing in-depth the problem to solve and the algorithmic behavior of each intelligent heuristic strategy included in every GGA.

关键词： Characterization algorithmic behavior grouping genetic algorithm grouping problems Parallel machine-scheduling problem

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Variation Operators for grouping genetic algorithms: A Review

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Ramos-Figueroa, Octavio Quiroz-Castellanos, Marcela Mezura-Montes, Efren Kharel, Rupak Univ Veracruzana Artificial Intelligence Res Ctr Xalapa Veracruz Mexico Manchester Metropolitan Univ Dept Comp & Math Manchester Lancs England

grouping problems are combinatorial optimization problems, most of them NP-hard, related to the partition of a set of items into different groups or clusters. Given their numerous real-world applications, different solution approaches have been presented to deal with the high complexity of NP-hard grouping problems. However, the grouping genetic algorithm (GGA) is one of the most outstanding solution methods. GGA is an extension to the traditional genetic algorithm (GA) that uses a representation scheme based on groups and variation operators adapted to work at the groups level. Since its emergence, GGA has been used to address several grouping problems with distinct traits. Therefore, at present, there are different variation operators developed to solve problems with diverse grouping constraints and conditions. This paper presents a review of variation operators included in GGAs solving NP-hard grouping problems. Three classifications are introduced, organizing the variation operators according to the variation-degree, the solutions encoding, and the parameter setting-level, respectively.

关键词： grouping problems grouping genetic algorithm grouping representation schemes Variation operators

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Using group genetic algorithm to improve performance of attribute clustering

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APPLIED SOFT COMPUTING 2015年 29卷 371-378页

作者： Hong, Tzung-Pei Chen, Chun-Hao Lin, Feng-Shih Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan Tamkang Univ Dept Comp Sci & Informat Engn Taipei 251 Taiwan

Feature selection is a pre-processing step in data mining and machine learning, and is very important in analyzing high-dimensional data. Attribute clustering has been proposed for feature selection. If similar attributes can be clustered into groups, they can then be easily replaced by others in the same group when some attribute values are missing. Hong et al. proposed a genetic algorithm (GA) to find appropriate attribute clusters. However, in their approaches, multiple chromosomes represent the same attribute clustering result (feasible solution) due to the combinatorial property, and thus the search space is larger than necessary. This study improves the performance of the GA-based attribute clustering process based on the grouping genetic algorithm (GGA). In the proposed approach, the general GGA representation and operators are used to reduce redundancy in the chromosome representation for attribute clustering. Experiments are also conducted to compare the efficiency of the proposed approach with that of an existing approach. The results indicate that the proposed approach can derive attribute grouping results in an effective way. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Attribute clustering Feature selection genetic algorithm grouping genetic algorithm Data mining

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Modular design method for filament winding process equipment based on GGA and NSGA-II

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期94卷 2057-2076页

作者： Xu, Xiaoming Zhang, Wuxiang Ding, Xilun Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

The traditional filament winding machine design method leads to high costs and a long delivery cycle if the winding method or process parameters are changed. To solve this problem, the modular design and configuration design methods are proposed to cluster components into standard modules that can be changed or replaced and to combine the types of modules to create a complete system according to the customer's requirements. The system structure is first analyzed, and the relationship matrices between the components and the influence of the modular driving forces (MDFs) on each component are subsequently established with consideration for function and stability in the life cycle. The grouping genetic algorithm (GGA) method is later employed to conduct modular optimization to maximize the interaction and stability between the components within modules. Several different types are designed based on the result of the modular design method. The relationship matrices between the module types, the parameter performance, and the customer's requirement are established, and a multi-objective optimization method based on the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to create a complete system that combines the different types with simultaneous consideration of performance and processing cost. Sensitivity analysis of GGA and NSGA-II demonstrates that the proposed method is robust. An example for the requirements of the filament winding is used to demonstrate the validity of the proposed method. The general procedure of the modular design method using GGA and NSGA-II is proposed, and the key parameters that influence the results are discussed.

关键词： Filament winding process Modular design Configuration optimization grouping genetic algorithm Non-dominated sorting genetic algorithm II

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Modular design to support green life-cycle engineering

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第4期34卷 2524-2537页

作者： Tseng, Hwai-En Chang, Chien-Chen Li, Jia-Diann Natl Chin Yi Univ Technol Dept Ind Engn & Management Taiping Taichung Taiwan Huafan Univ Dept Ind Design Taipei Hsien Taiwan

The severe competition in the market has driven enterprises to produce a wider variety of products to meet consumers' needs. However, frequent variation of product specifications causes the assembly and disassembly of components and modules to become more and more complicated. As a result, the issue of product modular design is a problem worthy of concern. In this study, engineering attributes were added to the liaison graph model for the evaluation of part connections. The engineering attributes added, including contact type, combination type, tool type, and accessed direction, serve to offer designers criteria for evaluating the component liaison intensity during the design stage. A grouping genetic algorithm (GGA) is then employed for clustering the components and crossover mechanisms are modified according to the need of modular design. Furthermore, a reasonable green modular design evaluation is conducted using the green material cost analysis. According to the results, adjusted design proposals are suggested and materials that cause less pollution are recommended to replace the components with pollution values higher than those in the module. Finally, the authors use Borland C++ 6.0 to evaluate the system and clustering method. To illustrate the methodology proposed in this study, a table lamp is offered as an example. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： modular design green life-cycle engineering grouping genetic algorithm liaison intensity green analysis

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