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Multi-product supply chain scheduling method based on hybrid genetic algorithm

International Journal of Manufacturing Technology and Management 2025年第1-2期39卷 122-136页

作者： Yang, Lingmin College of Management Hubei University of Education Wuhan430205 China

In order to solve the shortcomings of traditional methods such as high Hamming loss value and low demand supply rate, a multi-product supply chain scheduling method based on hybrid genetic algorithm was designed. First, build supply time series, and sort the priority of supply chain scheduling according to the priority attribute values of backup plans in each link of the supply chain closed-loop model. Then, taking the shortest scheduling time as the objective function and the location constraints of the revolving warehouse as the constraints, the scheduling results are obtained by combining the genetic algorithm. In order to avoid the genetic algorithm falling into the local optimum, the simulated annealing algorithm is introduced to solve the global optimal solution of the objective function to achieve the coordinated scheduling of the supply chain. The experimental results show that this method can achieve multi-product supply chain scheduling more reasonably and effectively. © 2025 Inderscience Enterprises Ltd.

关键词： scheduling algorithms

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DYNAMIC scheduling OF MULTI-AGENT ELECTROMECHANICAL PRODUCTION LINE BASED ON BIOLOGICAL ITERATIVE ALGORITHM

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Scalable Computing 2025年第3期26卷 1326-1335页

作者： Zhang, Yan Li, Zipeng Yellow River Conservancy Technical Institute Department of mechanical engineering Henan Kaifeng475000 China

In order to solve the dynamic job scheduling problem in current intelligent machining systems, the author proposes a multi-agent electromechanical production line dynamic scheduling based on iterative algorithms. The author designed a collaborative control method for hybrid micro assembly production lines based on multi-agent technology. Firstly, a mathematical model is used to describe the collaborative control objectives of the production line, and a hybrid micro assembly production line information collection and integration framework is constructed to obtain production line information. By combining the dynamic coordination performance of multi-agent technology, a collaborative control model for production lines is constructed, with the goal of minimizing processing costs as the collaborative control objective. The optimal collaborative control scheme is solved to achieve collaborative control of hybrid micro assembly production lines. The experimental results show that compared with traditional methods, the collaborative control task allocation time obtained by applying this method is shorter, with a minimum value of 15.38 seconds, indicating that this method has higher efficiency in collaborative control of production lines. Compared with traditional methods, the collaborative control task allocation time after applying this method is shorter, effectively reducing the production line processing cost, proving the feasibility of this method. © 2025 SCPE

关键词： scheduling algorithms

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An MINLP model for project scheduling with feeding buffer

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International Journal of Applied Decision Sciences 2024年第6期17卷 710-732页

作者： Ayough, Ashkan Rabieh, Masood Javadi, Mohammad Naeem Khorshidvand, Behrooz Industrial Management Department Faculty of Management & Accounting Shahid Beheshti University Tehran Iran Tehran Iran

This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of the feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm's performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed. © 2024 Inderscience Enterprises Ltd.

关键词： scheduling algorithms

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An open-source framework for solving shop scheduling problems in manufacturing environments

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ANNALS OF OPERATIONS RESEARCH 2024年 1-29页

作者： Marquez, Carlos R. H. Braganholo, Vanessa Ribeiro, Celso C. Univ Fed Fluminense Inst Comp Niteroi Brazil

scheduling problems refer to the decision-making process of allocating tasks to resources, usually scarce and in high demand, to optimize different performance measures. We consider the class of shop scheduling problems arising in the context of manufacturing systems, which are often NP-hard and challenging to solve. Exact methods have limitations in finding optimal solutions in reasonable computation times, even for instances of moderate size. Therefore, in real-life production environments, finding high-quality solutions is often satisfactory, even if they are not optimal. We contribute to the solution of shop scheduling problems with the design and implementation of the SSP-3M framework, oriented by three main guidelines: versatility, extensibility, and independence of the optimization method. These characteristics reduce the gap between scheduling theory and practice in real-life environments and improve the integration of the scheduling framework with other process planning or functions such as Computer-aided Process Planning, Advanced Planning and scheduling, Integrated Process Planning and scheduling, and Computer Integrated Manufacturing. The problem and solution representations adopted in our framework design make it possible to handle six shop scheduling problem variants, illustrating its versatility: job shop, flow shop, permutation flow shop, generalized flow shop, flexible flow shop, and flexible job shop. SSP-3M is open-source and can be used by any interested party. Our experimental evaluation shows that it can successfully be integrated with external optimization methods. We claim that SSP-3M is a good choice for companies that need free and quick-to-develop solutions to shop scheduling problems.

关键词： scheduling framework Shop scheduling Manufacturing systems Job shop problem Flow shop problem scheduling algorithms Combinatorial optimization Open-source Manufacturing environment

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scheduling Multi-Component Applications Across Federated Edge Clusters With Phare

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 1814-1826页

作者： Castellano, Gabriele Galantino, Stefano Risso, Fulvio Manzalini, Antonio Univ Cote Azur Ctr Inria F-06902 Valbonne France Politecn Torino Dept Comp & Control Engn I-10129 Turin Italy Telecom Italia Mobile Innovat Labs I-10148 Turin Italy

The shift towards agile microservice architecture has enabled significant benefits for IT companies but has also resulted in increased complexity for Cloud orchestration tools. Traditional tools were designed for centralized data centers and are ineffective for locating microservices in geographically-distributed edge-like infrastructures. This paper presents Phare, a decentralized scheduling algorithm designed to optimize the placement of microservices by satisfying their computing and communication demands while minimizing deployment costs. Phare employs a heuristic-based approach to solve the NP-Hard scheduling problem, prioritizing the microservices with the more stringent requirements and placing them on the most convenient computing facilities, based on the concept of affinity, contributing to the field by providing a more holistic approach to resource scheduling in edge computing. We validate our approach against Firmament, the state-of-the-art workload scheduling algorithm for component-based applications, on simulated edge infrastructures with hundreds of clusters. Phare achieves up to a 10x reduction in terms of deployment costs compared to Firmament while providing a much lower scheduling latency.

关键词： Costs Microservice architectures Task analysis scheduling algorithms Data centers Cloud computing Resource management Resource sharing cloud-to-edge service allocation

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An Efficient Task scheduling Algorithm in the Cloud and Edge Collaborative Environment

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Chinese Journal of Electronics 2024年第5期33卷 1296-1307页

作者： Saiqin LONG Cong WANG Weifan LONG Haolin LIU Qingyong DENG Zhetao LI Jinan University School of Computer Science Xiangtan University Guangxi Normal University

With the advent of the 5G era and the accelerated development of edge computing and Internet of Things technologies, the number of tasks to be processed by mobile devices continues to increase. Edge nodes become incapable of facing massive tasks due to their own limited computing capabilities, and thus the cloud and edge collaborative environment is produced. In order to complete as many tasks as possible while meeting the deadline constraints, we consider the task scheduling problem in the cloud-edge and edge-edge collaboration scenarios. As the number of tasks on edge nodes increases, the solution space becomes larger. Considering that each edge node has its own communication range, we design an edge node based clustering algorithm(ENCA), which can reduce the feasible region while dividing the edge node set. We transform the edge nodes inside the cluster into a bipartite graph,and then propose a task scheduling algorithm based on maximum matching(SAMM). Our ENCA and SAMM are used to solve the task scheduling problem. Compared with the other benchmark algorithms, experimental results show that our algorithms increase the number of the tasks which can be completed and meet the latest deadline constraints by 32%–47.2% under high load conditions.

关键词： Cloud computing scheduling algorithms 5G mobile communication Clustering algorithms Collaboration Transforms scheduling

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Optimized Universal Disk scheduling Algorithm

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 31-101页

作者： Dash, Amar Ranjan Dash, Baishnobi Sahu, Sandipta Kumar Sahu, Neha Kumari Panda, Srinibash CSE Parala Maharaja Engineering College Sitalapalli Odisha Berhampur761003 India CSE National Institute of Science and Technology Pallur Hills Odisha Berhampur761008 India IT Amazon Perungudi Tamil Nadu Chennai600096 India Development Apmosys Technology Pvt. Ltd. Mahape Maharashtra Navi Mumbai400710 India

For efficient and faster working of computer, researchers tries to optimize different scheduling algorithms of operating system. Along with modernization of computer, the speed of hardware (like processor, RAM, and hard disk) gradually increases. But till now processor works in nanoseconds per instruction execution, where as hard disk works in microseconds per memory access. During execution, process needs both CPU bound and I/O bound time. The slowness of hard disk may detain the speed of processor, as due to its sluggishness it is unable to provide the needed data on time. For high-performance computing, the disk scheduling algorithms also need to be optimized along with optimization of CPU scheduling. The proposed scheduling algorithm improves the performance measure of the disk scheduling algorithm by reducing the seek time, the rotational latency, and the platter selection time. Proposed algorithm decreases the space complexity of the kernel as it is able to deal with different situation like difference in partition table, file system, number of hard disk, and RAID technology. The proposed algorithm also able to detect and manage physical bit bad sector of the hard disk. © The Institution of Engineers (India) 2024.

关键词： scheduling algorithms

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Deep Reinforcement Learning Approach for HAPS User scheduling in Massive MIMO Communications

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 1-14页

作者： Sharifi, Sara Khoshkbari, Hesam Kaddoum, Georges Akhrif, Ouassima Ecole Technol Super Montreal Dept Elect Engn Montreal PQ H3C 1K3 Canada

In this paper, we devise a deep SARSA reinforcement learning (DSRL) user scheduling algorithm for a base station (BS) that uses a high-altitude platform station (HAPS) as a backup to serve multiple users in a wireless cellular network. Considering a realistic scenario, we assume that only the outdated channel state information (CSI) of the terrestrial base station (TBS) is available in our defined user scheduling problem. We model this user scheduling problem using a Markov decision process (MDP) framework, aiming to maximize the sum-rate while minimizing the number of active antennas at the HAPS. Our performance analysis shows that the sum-rate obtained with our proposed DSRL algorithm is close to the optimal sum-rate achieved with an exhaustive search method. We also develop a heuristic optimization method to solve the user scheduling problem at the BS. We show that for a scenario where perfect CSI is not available, our proposed DSRL algorithm outperforms the heuristic optimization method.

关键词： Resource management Antennas scheduling scheduling algorithms Satellites Mathematical models Base stations HAPS deep reinforcement learning user scheduling Markov decision process

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algorithms for Canvas-Based Attention scheduling with Resizing

Algorithms for Canvas-Based Attention Scheduling with Resizi...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Yigong Hu Ila Gokarn Shengzhong Liu Archan Misra Tarek Abdelzaher University of Illinois at Urbana-Champaign Singapore Management University Shanghai Jiao Tong University

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

Canvas-based attention scheduling was recently pro-posed to improve the efficiency of real-time machine perception systems. This framework introduces a notion of focus locales, referring to those areas where the attention of the inference system should “allocate its attention”. Data from these locales (e.g., parts of the input video frames containing objects of interest) are packed together into a smaller canvas frame which is processed by the downstream machine learning algorithm. Compared with processing the entire input data frame, this practice saves resources while maintaining inference quality. Previous work was limited to a simplified solution where the focus locales are quantized to a small set of allowed sizes for the ease of packing into the canvas in a best-effort manner. In this paper, we remove this limiting constraint thus obviating quantization, and derive the first spatiotemporal schedulability bound for objects of arbitrary sizes in a canvas-based attention scheduling framework. We further allow object resizing and design a set of scheduling algorithms to adapt to varying workloads dynamically. Experiments on a representative AI-powered embedded platform with a real-world video dataset demonstrate the improvements in performance and inform the design and capacity planning of modern real-time machine perception pipelines.

关键词： Quantization (signal) Machine learning algorithms Limiting scheduling algorithms Pipelines Streaming media Inspection

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Performance Evaluation of the Nature-Inspired algorithms for Container scheduling for Elastic Containerized Multi-Cloud

Performance Evaluation of the Nature-Inspired Algorithms for...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Kanika Sharma Parul Khurana School Of Computer Application Lovely Professional University Phagwara India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In the dynamic world of elastic containerized multi-cloud environments, task scheduling algorithms are pivotal in maximizing resource utilization to attain performance objectives. This research focuses on several container scheduling algorithms based on Ant Colony Optimization, Chicken Swarm Optimization, Genetic Algorithm, Bee Colony Optimization, and Particle Swarm Optimization. In this respect, it seeks to evaluate these algorithms over metrics such as makespan, reliability, idle time, energy utilization, response time, CPU utilization, execution cost, and task migration. The paper analyzes these factors to identify the strengths and weaknesses of each algorithm. Using the standardized configuration of 50 containers and 5000 tasks, the evaluation gives the following results: all the algorithms have excellent reliability values of 0.99, a very good indication of their efficiency in the execution of tasks with minimal failure cases. ACO has an excellent CPU utilization of 86% and the lowest make span of 10 ms, indicating its efficiency in allocating resources and executing tasks. BCO has the highest CPU utilization at 88% and energy utilization at 1900 Joules. This indicates that it can effectively and very well exploit resources at the cost of increased energy consumption. ACO has the lowest response time of 7 ms, which ensures it responds quickly in general, and also with the lowest execution cost of $110, which indicates it is cost-effective. The performance of CSO and PSO is somewhat close to each other; GA has a balanced performance overall. To implement the abovementioned approach Container Cloud sim 4.0.0 is used. The paper presents a comparative study that helps to get deep insights into the performance characteristics of the different container scheduling algorithms for making an appropriate decision regarding deploying an application in elastic containerized multi-cloud environments.

关键词： Costs scheduling algorithms Containers Reliability engineering scheduling Time factors Resource management Security Particle swarm optimization Genetic algorithms

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