An Event-Based Parameterized Active Scheduler for Classical Job Shop Problem

In order to complete the offshore platform project scheduling intelligently, an improved scheduling optimization system based on the parallel genetic algorithm was proposed. An optimal model for the large-scale offshore platform project scheduling problem (LSOPPSP) was built and produced the mathematic model of LSOPPSP, based on the characteristics of the abundance of activities, long duration, high uncertainty, and frequent changes. In long-term unsteady manufacturing, this model can provide good robustness. In addition, the essential steps of the multitime window parallel genetic algorithm were proposed. An improved population initialization algorithm was designed, as well as the coevolution strategy among populations was also proposed in parallel computing. These two strategies can increase population variety while also speeding up convergence. Finally, the suggested parallel scheduling system was deployed in our self-developed schedule optimization software for offshore platform enterprises, and the outperformance of the improved algorithm was proven by simulated examples and practical application.

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A Multitime Window Parallel Scheduling System for Large-Scale Offshore Platform Project

Yang

Yin

et al. 2022

Wireless Communications and Mobile Computing

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An Event-Based Parameterized Active Scheduler for Classical Job Shop Problem

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A Multitime Window Parallel Scheduling System for Large-Scale Offshore Platform Project

A Multitime Window Parallel Scheduling System for Large-Scale Offshore Platform Project

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