A Heuristic Integrated Scheduling Algorithm Based on Improved Dijkstra Algorithm

Zhou, Pengwei; Xie, Zhiqiang; Zhou, Wei; Tan, Zhenjiang

doi:10.3390/electronics12204189

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…[10,11]. At present, global path planning algorithms mainly include A* [12], Dijkstra [13], and rapidly-exploring random trees (RRTs) [14]. Local path planning algorithms include the artificial potential field algorithm (APF) [15], DWA [16], and deep learning method (DL) [17].…”

Section: Related Workmentioning

confidence: 99%

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

Zhang,

Yang,

Zhou

et al. 2024

Applied Sciences

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In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

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Section: Related Workmentioning

confidence: 99%

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

Zhang,

Yang,

Zhou

et al. 2024

Applied Sciences

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“…Therefore, for the third type of product manufacturing scheduling mode with complex constrained processes, integrated scheduling came into being, which can be summarized as follows: (1) the complex process of the product is represented by the treelike structure, (2) the product quantity is single or small batch, (3) the processing and assembly of the product are coordinated [16]. Now a series of research results have been achieved [17][18][19][20][21][22][23][24][25][26]. For example, literature [24] proposed strategies such as sub-tree decomposition to improve the manufacturing efficiency of complex products [24].…”

Section: Introductionmentioning

confidence: 99%

Integrated scheduling algorithm with Dynamic Adjustment on Machine Idle Time

Zhou,

Ding,

Xie

et al. 2024

Preprint

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During the scheduling of the production of small batch and multi-variety products with treelike structure, many devices are idle due to the process constraints. This increases the makespan of production and reduces the utilization rate of the equipment. Aiming at improving this situation, a resource cooperative scheduling algorithm is proposed to dynamically adjust the idle time of equipment: the integrated scheduling algorithm with dynamic adjustment on machine idle time(ISA_DA). This algorithm adopts the critical path chain and the optimal scheduling strategy. Besides, the adjustment strategy considering the backward movement of the process is put forward to improve the intensity of the close connection of the process and the intensity of parallel processing. Experimental results show that the effect of the proposed algorithm is better than the comparison algorithms: the hierarchical scheduling order algorithm and the process tree cyclic decomposition algorithm. It can increase the equipment utilization rate by 10% and 11.5% respectively, and reduce the makespan by 28.6%.

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“…The scheduling problem is a classic NP-hard problem [12] and is also one of the key links for the efficient operation of an intelligent production system because dynamic scheduling can optimize several KPIs in the production space; for example, it can reduce the tardiness [13], the cost of storage [14], minimize the makespan [15], reduce the travelling time [16], and benefit other KPIs that change from company to company. Intelligent production has numerous advantages in terms of flexibility, maintainability, and cost.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

Del Gallo,

Mazzuto,

Ciarapica

et al. 2023

Electronics

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This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

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A Heuristic Integrated Scheduling Algorithm Based on Improved Dijkstra Algorithm

Cited by 6 publications

References 27 publications

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

Integrated scheduling algorithm with Dynamic Adjustment on Machine Idle Time

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

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