Build and evaluate a variety of mine scenarios to ensure you produce operationally viable plans and schedules.Learn More
Keep track of key activities across your processes to continually increase production, efficiency and safety.Learn More
Point cloud processing & analysis
3D laser scanning & imaging
3D mine planning & geological modelling
Material tracking and reconciliation systems
Maptek provides responsive technical support for our products. Find office, email and telephone contact details to get the help you need. Submit a support request online.
Maptek Account provides online licensing, flexible options for working offline, product download-update service and streamlined technical support to improve your experience with Maptek products.
The free quarterly Maptek Forge newsletter includes case studies, product and corporate news.
Maptek blogs cover a range of industry topics, sharing insights from across our global network.
Keep up to date with the latest product solutions and news about Maptek global activities.
An online repository of all Maptek learning resources, from white papers to blogs and case studies.
An overview of Maptek, our products, solutions and operations.
A brief history of Maptek, including milestones of our 40 years of operation.
Information about careers at Maptek and current vacancies.
Find conferences, seminars and tradeshows where Maptek is exhibiting and/or presenting papers.
Discover the features and benefits of Maptek solutions in these overview videos.
Watch webinars on various topics and learn about the latest solutions from Maptek subject matter experts.
Watch tutorials outlining ways to apply various tools. Learn the latest techniques for efficient workflows.
Short bite size videos include tips and tricks for applying common tools and solutions.
Prepare yourself! You enter the head office of a large multinational mining company, ready to help their senior scheduling engineer prepare the life-of-mine schedules for a green fields site. In front of them are two screens, both with Excel spreadsheets open. It doesn’t take long to realise the scheduler is comparing two Excel based plans for the new site.
This scenario is still seen in the day-to-day operations of many mining companies around the world. They continue to use Excel or other spreadsheet-type applications to plan, track and manage their schedules, from life-of-mine to short-term execution. There’s nothing overly wrong with this approach—spreadsheets are a tried and tested mechanism, but they have their limitations.
Imagine instead that same scheduling engineer had thousands of spreadsheets, each representing slight variations in the schedule for the same mine site. Over time, solutions would be discarded as they were infeasible or ‘not as good’ as others. Some schedules may be blended together, taking the first half of one, and second half of another to form a hybrid that is better than its predecessor. This description is the basis of genetic algorithms.
Genetic algorithms are a branch of computer science that reflects the process of natural selection where the fittest individuals are selected and combined to form new—and hopefully better—solutions.
This process can take hundreds, thousands, or hundreds of thousands of solutions (in our case stage/bench sequences) and evaluate them. All the while, discarding poor or infeasible sequences and continuing to grow the pool of near optimal schedules. This process is all done with guided input from the user, for instance adding constraints that set spatial dependencies and specific mining rules to ensure practicality, as well as meeting specific blend targets, trucking hours and mill capacities.
Doing this process manually means keeping track of an almost impossible number of competing constraints, and limits the number of schedules you can compare at any one time. If we instead move this burden into a genetic algorithm, we can add any number of constraints and simply wait for a collection of near-optimal solutions to present themselves. Engineers can then quickly compare across key points, with the knowledge these schedules are both practical and feasible!
So back to the spreadsheets—are these a good solution for scheduling? In my opinion, as complexity of the schedule grows, it becomes a lot of work to manage in a spreadsheet solution. You run the risk of missing updating calculations, inadvertently changing dependent constraints, and potentially losing your audit trail. When companies adopt a program that uses genetic algorithms to create schedules, most engineers will only accept the result when they can confirm the numbers by running manual calculations in some spreadsheet tool. At least it’s a start!
In my next blog, I’ll review and compare technologies that are used to solve scheduling problems.
Global Development Strategy Manager
February 8, 2021
For additional information about Maptek, including use of the Maptek logo, product images and reproduction of case studies, please direct inquiries to Global Marketing Communications Manager firstname.lastname@example.org