Opinion - (2023) Volume 13, Issue 2
Received: 03-Apr-2023, Manuscript No. jbmr-23-98601;
Editor assigned: 05-Apr-2023, Pre QC No. P-98601;
Reviewed: 17-Apr-2023, QC No. Q-98601;
Revised: 22-Apr-2023, Manuscript No. R-98601;
Published:
28-Apr-2023
, DOI: 10.37421/2795-5833.2023.13.492
Citation: Marin, Perito. “An Investigation into Multi-Equipment Collaborative Scheduling Algorithms under Composite Constraints.” Arabian J Bus Manag Review 13 (2023): 492.
Copyright: © 2023 Marin P. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In multi-equipment processes, many idle time fragments and transport waiting times are caused by frequent scheduling of multiple processes and multiple equipment under complex constraints. To further develop gear usage and lessen inactive transportation time, a creation interaction streamlining planning calculation with "least handling time and least transportation time" is proposed. A hybrid algorithm that combines a one-dimensional search algorithm with a dual NSGA-II algorithm is used to carry out the scheduling optimization, taking into account factors such as product priority, equipment priority, process priority, and overall task adjustment. In comparison to other algorithms, the scheduling algorithm proposed in this article not only reduces the minimum amount of time needed for processing, but it also tries to get the most out of each piece of equipment, cutting the company's processing time by 8% or more, shortening the total amount of time needed for transportation and indirectly lowering costs. Practice demonstrates the superiority of this algorithm by demonstrating its lower scheduling process complexity and practicality in actual operation. The key to achieving Made in China 2025, carbon neutrality, and carbon peak goals is the optimization of complex and variable manufacturing processes. Multi-gear cooperative (MEC) activity in assembling processes is a significant method for acknowledging item broadening and little clump creation. MEC operation is a crucial type of production process that has evolved into an efficient method for increasing equipment utilization ratios and decreasing energy consumption [1,2].
For streamlined planning of the creation cycle, researchers have proposed numerous hereditary calculations, including molecule swarm calculations, NSGA calculations, super-heuristic calculations, half breed calculations, and so on. Zhou and co. introduced new dynamic programming operators based on the decomposed multi-objective evolution algorithm and conducted experiments to demonstrate its superiority. Zhu and co. proposed and compared a number of different algorithms to a multi-objective optimal foraging algorithm based on fuzzy relative entropy to solve the scheduling problem for a workshop assembly line. Shao et al. proposed and demonstrated an effective iterative greedy algorithm that enhances the heuristic algorithm for resolving the distributed workshop scheduling issue. Li and co. laid out a better fake safe framework calculation and approved the arrangement interaction for adaptable studio booking. Chen and co. examined and checked a multi-objective powerful adaptable activity studio planning circumstance with respect to machine flaws, utilizing a NSGA-II calculation. Rakovitis, others compared its advantages to those of other algorithms and added flexible coefficients to the improved cellspecific temporal representation in order to solve flexible workshop scheduling issues. Gong et al. for the multi-objective shop tuning problem, an elite nondominant ranking hybrid algorithm was proposed. Jiang and co. proposed a superior decay based multi-objective development calculation for tackling shop-floor green planning. They designed a more advanced discrete whale swarm optimization algorithm to address the scheduling issue in workshops. Sun and co. proposed a viable half and half co-advancement calculation that joins hereditary calculations with molecule swarm enhancement calculations to tackle the adaptable booking issue in assembling frameworks. Zhang and co. utilized cutting-edge metaheuristics to make dynamic scheduling decisions for job shops. Chen and co. proposed and verified a hyper-heuristic genetic algorithm in steel production scheduling for network-based physical systems. Gao and co. fostered a half and half hereditary calculation (hGA) with an inventive neighborhood search strategy (bottleneck moving) for work shop booking issues. Moin and co. proposed and verified a multiparent crossover hybrid genetic algorithm for scheduling issues in job shops proposed and solved multi-production-cell collaborative scheduling issues in parallel manufacturing using a piecewise cooperative genetic algorithm. Peng and co. proposed a hybrid evolutionary algorithm (HEA) to solve the multi-depot green vehicle routing problem [3-6].
A studio plan is completed in a studio, which is basically unique in relation to the MEC cycle. In one workshop, an MEC process may employ a variety of equipment types, while in other workshops; multiple types of equipment may work together. The problem of large amounts of fragmentation time and transport waiting caused by simultaneous use of multiple equipment under complex constraints is proposed to be solved by a hybrid algorithm that combines a one-way search algorithm with a double NAGA-II algorithm. Multi-equipment cooperative operation's speed and accuracy are found to be effectively enhanced by the proposed method.
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