This website uses cookies primarily for visitor analytics. Certain pages will ask you to fill in contact details to receive additional information. On these pages you have the option of having the site log your details for future visits. Indicating you want the site to remember your details will place a cookie on your device. To view our full cookie policy, please click here. You can also view it at any time by going to our Contact Us page.

MangoGem launches ORITAMES APS Scheduler v2.5

04 April 2019

MangoGem S.A., an innovative provider of intelligent software solutions for challenging enterprise problems, has launched Version 2.5 of its powerful ORITAMES Advanced Planning and Scheduling system.

The ORITAMES APS Scheduler v2.5 incorporates autonomous Artificial Intelligence (AI) capabilities for improved planning and schedule optimization as well as improved modeling of sequence dependent setups, batching and time constraints, making it ideally suited to large, real-life problems taking into account the complex constraints of real cases.

“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.

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.

Unlike traditional APS schedulers, which can only handle one type of resource or only a single resource per activity, ORITAMES APS Scheduler v2.5 is a multi-resource scheduler capable of handling multiple instances of equipment, people, consumables, tools, locations etc.

It can also combine several resources together and any activity can have multiple modes to closely model reality.

It also supports the selection of multiple objectives and multiple criteria, such as makespan, deadline satisfaction, cost reduction, Just-in-Time, throughput, resource utilization or consumables usage, combining several key performance indicators (KPIs) together, producing optimal schedules and analyzing objective trade-offs and “what if” scenarios without effort.

ORITAMES APS Scheduler v2.5 is platform-agnostic, running on MS Windows, Mac OS and a variety of UNIX and Linux platforms.

It can be used as a standalone application or it can be easily integrated with other Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) tools.

It can be used offline, online and even in real-time and its parallel, multi-threaded solvers ensure that it creates optimal scheduling solutions extremely fast.

ORITAMES APS Scheduler v2.5 is ideally suited to a wide variety of industries, organizations and applications including discrete manufacturing (metal, glass, furniture, electronics, quarries, food processing etc.), construction and infrastructure projects, logistics and transportation, equipment and property maintenance, engineering projects and service organizations (schools, hospitals etc.).

It's flexible modeling capabilities and business rules scripting extensions, make it easily adaptable to many applications without the need for heavy customised programming.

“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.”

Print this page | E-mail this page


View more articles
Article image

Why the Law Says You Need a Nappy Bin Disposal Service

At home, parents are used to disposing of their babies’ used nappies the same way they do any other domestic waste - bagging it up and sticking it in the r...
Article image

Making smart decisions when purchasing new technology

We asked industry experts if the implementation of new technology should be expected to reduce cost, in addition to improving the operation of facilities, ...
Article image

“I don’t understand the PFM Awards”

The information provided below is intended to provide the FM industry with more understanding of the industry’s oldest awards initiative, which continues t...
Article image

Sustainable business policies making good business sense

PFM examines how the alignment of sustainable practices are becoming an important element in the winning or contin...

Benchmarking maintenance

BSRIA has just published this year's operation and maintenance benchmarking report as a guide for building operators to evaluate their performance against ...
Article image

Training course for non-native invasive weeds launched

An initiative launched by the Property Care Association (PCA) is designed to help FMs improve their identification of non-native invasive weeds....