ORITAMES APS Scheduler v2.5 Breaks Traditional Barriers
Thanks to increased efforts in autonomous Artificial Intelligence, the ORITAMES APS Scheduler v2.5 – created by Brussels-based company MangoGem S.A., is now capable of handling levels of resources and scenarios of equipment, people, and consumables.
“The ORITAMES APS Scheduler v2.5 uses AI-assisted autonomous machine learning to explore optimization scenarios better than traditional approaches, combining the best of machine computing power and human know-how to facilitate better planning and scheduling,” said Ben Rodriguez, Chief Technology Officer of MangoGem. With solver heuristics notoriously difficult to fine tune, the ORITAMES APS Scheduler v2.5 discovers which settings work best, checks validity and detects trends in data to improve ramp-up and adoption. It can also discover the most promising improvement scenarios and propose them as potential solutions to human planners. It combines several AI-assisted machine learning meta-heuristics including Genetic Algorithms (GA), Taboo Search (TS), Simulated Annealing (SA), Swarm Intelligence (SI) amongst others.”
By breaking the barriers of a traditional APS, ORITAMES APS v2.5 sets a new standard by taking on and solving complex and multi-layered cases typical for larger companies.
“The use of AI-assisted autonomous machine learning also eases the integration and reduces the cost of implementation of the ORITAMES APS Scheduler v2.5 within overall supply chain management (SCM) strategies by making modeling easier, improving data quality and decreasing dependency on human expertise,” he concluded.
This advanced software platform was specifically designed to cater to a variety of industries including discrete manufacturing, construction and infrastructure projects, logistics and transportation, equipment and property maintenance, engineering projects and service organizations.
“The AI-assisted disruptive technology delivered in ORITAMES APS Scheduler v2.5 is capable of solving many of the operational performance challenges of Industry 4.0,” concluded Ben Rodriguez, CTO of MangoGem. “It uses AI-assisted autonomous machine learning techniques with multiple solvers and heuristics and, depending on an analysis of the case at hand, will apply many methods to find the one that produces the best results.”