An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

Meng, Leilei; Cheng, Weiyao; Zhang, Biao; Zou, Wen-Qiang; Fang, Weikang; Duan, Ping

doi:10.3390/s23083815

Cited by 22 publications

(12 citation statements)

References 38 publications

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“…. taking a longer amount of time to arrive at the best possible answer (Meng et al, 2023;Sun et al, 2023). Besides that, the IPSO-based meta-heuristic algorithms provide superior outcomes in a shorter amount of time when compared to the other methods.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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“…Equation (9) means that the number of AGVs arriving at the checkpoint entrance at any moment should be less than the maximum storage capacity of the checkpoint entrance; Equation (10) means that each storage space is transported by only one AGV at any moment. Equation (11) indicates that the quantity of material in the storage space w meets the demand for material i at the verification line p; Equation (12) indicates that the deviation of the order from the average distribution is within δ. N wi indicates the number of commodities owned by each pallet; Equations ( 13) and ( 14) indicate that there is at most one AGV traveling through any node at time t to avoid the occurrence of AGV node conflicts; Equation (15) indicates that the path network that can be exercised by AGVs is a unidirectional guided path; Equation ( 16) represents the time that the AGV passes through the path node m → n segment; Equation (17) represents the continuity of AGV trolley transportation time; Equation (18) represents the need to maintain a safe time interval when entering the same path node through any two trolleys. Equation ( 19) represents the charging duration of the AGV; Equation (20) represents the AGV charging operation, i.e., the current charge is charged if it is less than or equal to the minimum charge and vice versa.…”

Section: Symbol Descriptionmentioning

confidence: 99%

“…For multi-load AGV scheduling problems with capacity constraints, the literature [17] proposed an improved ant colony optimization-simulated annealing algorithm based on multi-attribute scheduling rules, but without considering AGV quantity limits. Further taking into account that the number of AGVs is limited, the literature [18] proposed an improved genetic algorithm to solve flexible job shop scheduling problems with multiple AGVs. The literature [19] balanced the distribution of traffic loads for all AGVs executing tasks in the network to avoid local congestion.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

Shi,

Zhang,

et al. 2023

Symmetry

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Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model.

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“…To ensure that the capacity of a conventional manufacturing machine is maintained, it is necessary to define the time a production resource j requires to process a lot of specific basic tasks assigned to a certain product p. For the factory-planning process, average setup and changeover times from product A to product B are sufficient. Scheduling considerations like in the research on flexible job-shop scheduling (e.g., [143][144][145][146][147][148][149]) are unnecessary. Thus, the time for processing a certain lot (T p ij ) on a conventional machine can be formulated, taking into account the processing time of one basic task i on machine j (t ij ), the lot size of product p (L p ), and the average changeover time of machine j for basic task i (t r ij ).…”

Section: Capacity Restriction Of Conventional and Additive Resourcesmentioning

confidence: 99%

A Cost/Benefit and Flexibility Evaluation Framework for Additive Technologies in Strategic Factory Planning

2023

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There is a growing demand for more flexibility in manufacturing to counter the volatility and unpredictability of the markets and provide more individualization for customers. However, the design and implementation of flexibility within manufacturing systems are costly and only economically viable if applicable to actual demand fluctuations. To this end, companies are considering additive manufacturing (AM) to make production more flexible. This paper develops a conceptual model for the impact quantification of AM on volume and mix flexibility within production systems in the early stages of the factory-planning process. Together with the model, an application guideline is presented to help planners with the flexibility quantification and the factory design process. Following the development of the model and guideline, a case study is presented to indicate the potential impact additive technologies can have on manufacturing flexibility Within the case study, various scenarios with different production system configurations and production programs are analyzed, and the impact of the additive technologies on volume and mix flexibility is calculated. This work will allow factory planners to determine the potential impacts of AM on manufacturing flexibility in an early planning stage and design their production systems accordingly.

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An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

Cited by 22 publications

References 38 publications

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

A Cost/Benefit and Flexibility Evaluation Framework for Additive Technologies in Strategic Factory Planning

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