A cross-enterprise collaborative production scheduling decision support algorithm with multi-agent support DOI Open Access
Lili Chen,

Jianbing Yang

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract This study harnesses the capabilities of intelligent agent technology to develop a framework for cross-enterprise collaborative production scheduling decision-making. It conducts comprehensive examination business processes and decisions encapsulated within this framework. The research begins by pinpointing challenges inherent in scheduling. Subsequently, it introduces genetic algorithm tailored agent-based decision-making context delineates its algorithmic parameters. effectiveness approach is validated through series simulation experiments focused on case from an agent-oriented perspective. findings indicate that implementing structure algorithms scenario involving ten workpieces machines (10×10) results new job reach time 30, workshop load 0.5338, average reduction 11.60%. These underscore efficacy proposed enhancing decision support scheduling, thereby laying scientific foundation achieving heightened efficiency technology.

Language: Английский

Measuring hydrological alterations and landscape patterns for sustainable development through ecosystem connectivity in Hastinapur Wildlife Sanctuary, India DOI
Sonali Kundu, Narendra Kumar Rana, Barnali Kundu

et al.

Journal of Environmental Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0

Groundwater–Vegetation Interactions in Rangeland Ecosystems: A Review DOI Open Access
Monde Rapiya, Abel Ramoelo

Water, Journal Year: 2025, Volume and Issue: 17(8), P. 1174 - 1174

Published: April 14, 2025

Water scarcity is a growing global issue, especially in arid and semi-arid rangelands, primarily due to climate change population growth. Groundwater crucial resource for vegetation these ecosystems, yet its role supporting plant life often not fully understood. This review explores the interactions between groundwater dynamics various rangeland types. serves as critical water source that helps sustain plants, but changes availability, depth, quality can significantly impact health, biodiversity, ecosystem stability. Research indicates depth affects types their distribution, with specific plants thriving at certain levels. For instance, grasslands, shallow support diverse herbaceous species, while deeper conditions may favor drought-tolerant shrubs trees. Similarly, forest extensive root systems access both soil moisture, playing vital regulation. Savanna environments showcase complex interactions, where trees grasses compete water, potentially benefiting during dry seasons. Climate poses additional challenges by altering rainfall patterns temperatures, affecting recharge availability. As result, it develop effective management strategies integrate conservation health. Innovative monitoring techniques, including remote sensing, provide valuable information about levels on vegetation, enhancing management. emphasizes importance of understanding groundwater–vegetation guide sustainable land practices. By our knowledge connections utilizing advanced technologies, we promote resilience, secure resources, biodiversity systems. Collaborative efforts among local communities, scientists, policymakers are essential address pressing issues ensure sustainability ecosystems future generations.

Language: Английский

Citations

0

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis DOI

M. Subbulakshmi,

Sachikanta Nanda

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(11), P. 5395 - 5416

Published: Aug. 15, 2024

Language: Английский

Citations

1

A cross-enterprise collaborative production scheduling decision support algorithm with multi-agent support DOI Open Access
Lili Chen,

Jianbing Yang

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract This study harnesses the capabilities of intelligent agent technology to develop a framework for cross-enterprise collaborative production scheduling decision-making. It conducts comprehensive examination business processes and decisions encapsulated within this framework. The research begins by pinpointing challenges inherent in scheduling. Subsequently, it introduces genetic algorithm tailored agent-based decision-making context delineates its algorithmic parameters. effectiveness approach is validated through series simulation experiments focused on case from an agent-oriented perspective. findings indicate that implementing structure algorithms scenario involving ten workpieces machines (10×10) results new job reach time 30, workshop load 0.5338, average reduction 11.60%. These underscore efficacy proposed enhancing decision support scheduling, thereby laying scientific foundation achieving heightened efficiency technology.

Language: Английский

Citations

0