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19th International Conference on hybrid Artificial Intelligence Systems

作者： Sabo, Cosmin Teglas, Bogdan Pop, Petrica C. Petrovan, Adrian Tech Univ Cluj Napoca North Univ Ctr Baia Mare Dr V Babes 62A Baia Mare 430083 Romania

ISBN: (纸本)9783031741821;9783031741838

The clustered minimum routing tree problem (CluMRTP) extends the classical minimum routing tree problem by considering a graph with vertices divided into a given number of clusters. The scope of the CluMRTP is to find a minimum-cost routing tree ensuring that each cluster forms a connected subgraph. In this work, we present Prufer encoding-based hybrid genetic algorithms (HGAs) developed to address the CluMRTP. Our approach involves a macro-level genetic algorithm (GA) framework, employing the Prufer code to generate trees linking the clusters, which is coupled with local-level algorithms to derive a feasible solution for the CluMRTP corresponding to a tree which spans the clusters. Preliminary computational results are reported on a set of 78 benchmark instances. These results serve to showcase the efficiency of our novel solution approaches. The achieved computational results demonstrate that our innovative hybrid genetic algorithms are strongly competitive compared with the current state-of-the-art algorithms.

关键词： hybrid genetic algorithms minimum routing tree problem clustered minimum routing tree problem

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hybrid genetic algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

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IEEE ACCESS 2024年 12卷 23029-23042页

作者： Ou, Samnang Ismail, Zool Hilmi Sariff, Nohaidda Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot Kuala Lumpur 54100 Malaysia Taylors Univ Fac Innovat & Technol Sch Engn Subang Jaya 47500 Selangor Malaysia

The Order-Picking System (OPS) is vital for inbound logistics, ensuring efficient customer order fulfillment and minimizing costs. Efficient execution and implementation of OPS are critical to meeting customer demands and reducing dissatisfaction, necessitating a thorough examination of the process of order picking features. The order picking, a cornerstone of warehouse operations, involves meticulous selection and gathering of items from designated storage locations, unfolding through stages like order assignment and order batching. In order assignment, specific orders are methodically delegated to pickers or teams, considering factors like urgency, order size, item location, and picker availability. The overarching goal is to optimize resource utilization and simultaneously reduce the time needed for order fulfillment, ensuring a streamlined and efficient approach. Conversely, order batching strategically groups multiple orders for concurrent picking, aiming to minimize trips and enhance overall efficiency and productivity. Throughout the order-picking process, pickers utilize tools like pick lists, barcode scanners, and automated storage and retrieval systems (AS/RS) for precise item location and retrieval. Post-collection, items are transported to a dedicated packing area for meticulous preparations before shipping to the customer. Orchestrating the order-picking process requires careful planning, coordination, and execution for punctual and precise customer order fulfillment. This paper highlighted a systematic reviewing process which analyzed relevant research papers, with a primary focus on the problems of order assignment and batching-a key area within the order-picking process. The objective was to provide a comprehensive overview of hybrid genetic Algorithm solutions for these challenges, achieved through a systematic review from 2018 to 2023 using Web of Science and Scopus databases. After screening, the relevant references were selected, focusing on term

关键词： Order picking system order assignment order batching hybrid genetic algorithms systematic literature review

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hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators

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NEUROCOMPUTING 2007年第4-6期70卷 749-761页

作者： Zheng, Jiaju Cao, Shuying Wang, Hongli Huang, Wenmei Tianjin Univ Sch Mech Engn Tianjin 300072 Peoples R China Hebei Univ Technol Sch Elect Engn & Automat Tianjin 300130 Peoples R China

In this paper, we present an improved hysteresis model for magnetos trictive actuators. To obtain optimal parameters of the model, we study two distinct hybrid strategies: namely, employing a gradient algorithm as a local search operation of a genetic algorithm (GA), and taking the best individual of a GA as the initial value of a gradient algorithm. Here, two different gradient algorithms, a well-known Levenberg-Marquardt algorithm (LMA) and a novel Trust-Region algorithm (TRA), are investigated. Finally, the proposed four hybrid genetic algorithms (HGAs) are applied to identify parameters of the improved model. The simulation and experimental results show the performances of the HGAs and the improved hysteresis model. (c) 2006 Elsevier B.V. All rights reserved.

关键词： hybrid genetic algorithms hysteresis magnetostrictive actuator parameter identification

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hybrid genetic algorithms with Fuzzy Logic Controller

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Journal of Systems Engineering and Electronics 2001年第3期12卷 9-15页

作者： Zheng Dawei & Gen Mitsuo Department of Industrial and Systems Engineering, Ashikaga Institute of Technology, 326, Japan Department of Industrial and Systems Engineering Ashikaga Institute of Technology 326 Japan

In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.

关键词： Machine scheduling problem hybrid genetic algorithms Fuzzy logic.

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hybrid genetic algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System

Hybrid Genetic Algorithms for Multi-Period Part Type Selecti...

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IEEE International Conference on Computational Intelligence and Cybernetics (IEEE CyberneticsCom)

作者： Mahmudy, Wayan F. Marian, Romeo M. Luong, Lee H. S. UB Dept Comp Sci Malang Indonesia Univ South Australia Sch Engn Adelaide SA Australia

ISBN: (纸本)9781467360531

This paper addresses the multi-period part type selection and machine loading problems in flexible manufacturing system (FMS) with the objective of maximizing system throughput and maintaining balance of the system for the whole planning horizon. Various flexibilities including machine and tool flexibility, routing flexibility, and alternative production plans are considered. hybridization of real coded genetic algorithms (RCGA) and variable neighborhood search (VNS) is proposed to simultaneously solve these NP-hard problems for the whole periods. The proposed hybrid genetic algorithms (HGA) are designed to balance the power of the algorithms to explore a huge search space and to exploit local search areas. The experiments show that addressing the problems for the whole periods simultaneously will produce better results comparable to those achieved by the sequential approach.

关键词： flexible manufacturing system multi-period production planning part type selection problem machine loading problem hybrid genetic algorithms

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hybrid genetic algorithms FOR MINIMIZING THE MAXIMUM COMPLETION TIME FOR THE WAFER PROBING SCHEDULING PROBLEM

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2005年第3期22卷 218-225页

作者： Yang, Ming-Hsien Lo, Chien-Chung Chen, Hsin-Min Huang, Hau-Lieng Natl United Univ Dept Business Management 1 Lien Da Miaoli 36003 Taiwan

The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

关键词： hybrid genetic algorithms initial population minimum make span sequence dependent setup time due dates

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Enhancing the performance of hybrid genetic algorithms by differential improvement

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COMPUTERS & OPERATIONS RESEARCH 2013年第4期40卷 1038-1046页

作者： Drezner, Zvi Misevicius, Alfonsas Calif State Univ Fullerton Coll Business & Econ Fullerton CA 92834 USA Kaunas Univ Technol Dept Multimedia Engn Kaunas Lithuania

A differential improvement modification to hybrid genetic algorithms is proposed. The general idea is to perform more extensive improvement algorithms on higher quality solutions. Our proposed Differential Improvement (DI) approach is of rather general character. It can be implemented in many different ways. The paradigm remains invariant and can be easily applied to a wider class of optimization problems. Moreover, the DI framework can also be used within other hybrid metaheuristics like hybrid Scatter Search algorithms, Particle Swarm Optimization, or Bee Colony Optimization techniques. Extensive experiments show that the new approach enables to improve significantly the performance of hybrid genetic algorithms without adding extra computer time. Additional experiments investigated the trade-off between the number of generations and the number of iterations of the improvement algorithm. These experiments yielded six new best known solutions to benchmark quadratic assignment problems. Many other variants of the proposed algorithm are suggested for future research. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： genetic algorithms hybrid genetic algorithms Memetic algorithms Tabu Search Modified Robust Tabu Quadratic assignment problem

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Multicriteria-multistage planning for the optimal path selection using hybrid genetic algorithms

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APPLIED MATHEMATICS AND COMPUTATION 2006年第2期180卷 549-558页

作者： Lin, Chi-Ming Kainan Univ Coll Management Ctr Gen Educ Tao Yuan Taiwan Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

The problem of the competence set expansion involves determining the optimal expansion path under the minimum cost and time. As we know, the conventional competence set model only considers the problems of the static situation and the single objective. However, the dynamic situation and the multicriteria should also be simultaneously considered in practice. In this paper, a multicriteria and multistage competence set model is proposed. In order to efficiently obtain an optimal expansion path, hybrid genetic algorithms (HGA) are employed. In addition, a numerical example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide a sound competence set model by simultaneously considering the multicriteria and multistage situations. (c) 2006 Published by Elsevier Inc.

关键词： competence set optimal expansion path multistage planning multieriteria planning hybrid genetic algorithms

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Multiobjective fuzzy competence set expansion problem by multistage decision-based hybrid genetic algorithms

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APPLIED MATHEMATICS AND COMPUTATION 2006年第2期181卷 1402-1416页

作者： Lin, Chi-Ming Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Kainan Univ Coll Management Ctr Gen Educ Tao Yuan Taiwan

The problem of competence set expansion involves determining the optimal expansion path under the minimum cost and has been widely used for the problem of process planning. As we know, the conventional competence set model only considers the problems of the single objective and the static situations. In practice, however, the multiobjective and the dynamic situations usually occur in the optimal expansion path problems. Furthermore, due to the subjective judgment and the restriction of information, the degree of uncertainty should also be considered. In this paper, a multiobjective and multistage fuzzy competence set model is proposed to overcome the problems above simultaneously. In order to efficiently obtain the optimal expansion path, hybrid genetic algorithms (hGA) are employed here. In addition, a particular job-shop scheduling example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide a sound fuzzy competence set model by considering the multiobjective and the multistage situations simultaneously. (c) 2006 Elsevier Inc. All rights reserved.

关键词： fuzzy competence set optimal expansion path multiobjective planning multistage planning hybrid genetic algorithms

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Solving a timetabling problem using hybrid genetic algorithms

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SOFTWARE-PRACTICE & EXPERIENCE 1997年第10期27卷 1121-1134页

作者： Kragelund, LV Department of Computer Science University of Aarhus Building 540 8000 Aarhus C Ny Munkegade Denmark

All over the world, human resources are used on all kinds of different scheduling problems, many of which are time-consuming and tedious. Scheduling tools are thus very welcome. This paper presents a research project, where genetic algorithms (GAs) are used as the basis for solving a timetabling problem concerning medical doctors attached to an emergency service. All the doctors express personal preferences, thereby making the scheduling rather difficult. In its natural form, the timetabling problem for the emergency service Is stated as a number of constraints to be fulfilled. For this reason, it was decided to compare the strength of a Go-evolutionary Constraint Satisfaction (CCS) technique with that of two other GA approaches. Distributed GAs and a simple special-purpose hill climber were introduced, to improve the performance of the three algorithms. Finally, the performance of the GAs was compared with that of some standard, nonGA approaches. The distributed hybrid GAs were by far the most successful, and one of these hybrid algorithms is currently used for solving the timetabling problem at the emergency service. (C) 1997 by John Wiley & Sons, Ltd.

关键词： genetic algorithms timetabling problem co-evolutionary constraint satisfaction hybrid genetic algorithms distributed genetic algorithms

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