Formulation and Methods for a Class of Two-stage Flow-shop Scheduling Problem with the Batch Processor

Shen, Yilan; Wang, Weihao; Shi, Leyuan

doi:10.1109/case48305.2020.9216748

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Under this optimization objective, the average time saving is 10.35%, and the average energy saving is 10.63%. The fitness calculation method is changed to: 𝑓𝑖𝑡𝑛𝑒𝑠𝑠 = 𝑒 𝑇𝑖𝑚𝑒 𝑒𝑥𝑝 −𝑇𝑖𝑚𝑒 𝑐𝑜𝑢𝑛𝑡 + 𝑊 𝑒𝑥𝑝 𝑊 𝑐𝑜𝑢𝑛𝑡 (22) This fitness calculation method amplifies the influence of power consumption parameters on fitness and reduces the influence of time. Using this method to optimize the scheduling of batch 4 workpieces, the final consumption time is 7063.54 min, and the energy consumption is 42.12 GJ.…”

Section: Change Optimization Goalsmentioning

confidence: 99%

“…Two scientific articles [19,20] used an exact algorithm to solve the scheduling optimization problem, but the total time required for optimization was longer when the number of orders was large. In other scientific articles [21][22][23], researchers used a heuristic algorithm to optimize the workshop scheduling problem. Still, the heuristic algorithm is a suggested optimization method; its final solution is only a better solution, and the overall optimization effect is poor.…”

Section: Introductionmentioning

confidence: 99%

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Production Scheduling Optimization during Thermoforming of Ring Forgings Based on Genetic Algorithms

Chen

Xie

et al. 2022

Metals

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In the aerospace industry, many important components are made of ring forgings with characteristics of multi-variety and multi-batch. Such components have many steps and complex parameters in the thermoforming process. The process orders are dynamic and time-varying, and, thus, optimizing the total production time and energy consumption is difficult. To solve the mentioned troublesome and time-consuming problem, this work transformed the workpiece’s required heating temperature and time index into the furnace temperature change and holding time index. Based on a genetic algorithm, an integrated production scheduling optimization of ring forging heating and model forming was established. The genetic algorithm for model improvement was optimized. The optimization objective was changed by using different fitness calculation methods. A multi-time simulation algorithm was designed to calculate each heating furnace’s time and furnace temperature. The proposed optimization method was used for a thermoforming process of ring forgings. When the optimization objective was designed to consider energy consumption and time consumption comprehensively, the average time saving was 6.93%, and the average energy saving was 12.99%. When the optimization objective was designed to prioritize energy consumption, the average time saving was 3.89%, and the average energy saving was 16.53%. When the optimization objective was designed to prioritize time consumption, the average time saving was 10.35%, and the average energy saving was 10.63%. Using the scheduling results for production, compared with the practical factory data, the errors in the simulation time and energy consumption were 2.4% and 1.6%. The results show that the scheduling efficiency of integrated thermoforming production is significantly improved by using this optimization model, and the simulation results have high reliability. The energy consumption of orders is greatly reduced, and the total production time is greatly shortened.

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Section: Change Optimization Goalsmentioning

confidence: 99%