What is dynamic replanning?

The advent of laboratory automation has greatly improved efficiency from a variety of perspectives, including increasing the time scientists are away and increasing throughput in response to demand. Automation has made it easier to predict, plan, and optimize laboratory operations.

The key to maximizing these efficiencies is implementing an automation platform that delivers both robotics and digital. The platform must seamlessly connect instruments, streamline data flows, and support dynamic lab planning at both the software and hardware levels.

There are countless benefits to using efficient experiment planning software. Users can better ensure that time, instruments, and personnel are fully utilized, and there will be noticeable improvements in the reliability, consistency, and success rates of experiments.

Finding a scheduler that efficiently assigns tasks to a set of heterogeneous resources can be difficult. The scheduler must also consider the constraints of each specific resource, respond to changes in those parameters, and consistently deliver the complete workflow.

Photo source: Gorodenkoff/Shutterstock.com

The importance of schedules

Good experiment planning software is key to streamlining lab workflows and processes. A solid schedule can:

• Supporting the development and implementation of new, unique protocols and workflows.
• Help analyze performance to better optimize workflows and individual instruments.
• Use virtual workflow simulations to streamline experiment design and reduce testing time.
• Facilitate the simultaneous execution and tracking of multiple workflows when instruments, robots and transport systems are fully integrated.

Most laboratory systems evolved from tracking experiments on paper or using spreadsheets. Two common types of schedules, static and dynamic, are now common.

Schedulers typically use rule-based algorithms that address specific scheduling goals or problems, or mathematical optimization algorithms that aim to minimize the time it takes to complete workflows from start to finish.

Static schedules

Static schedulers make decisions based on predefined constraints. They typically assign tasks before they start executing and have no way of changing this during execution, for example in response to information from task tracking events.

Example of handling instrument errors from a static schedule

Video Source: Automata

Advantages and disadvantages of static schedules

Because known parameters are used, static schedulers are well suited to simple, predictable processes. They are reliable and robust when the data used is accurate and multithreading, synchronization, or parallel operation are not needed.

This simple approach may reduce risk but also reduce time efficiency in general applications.

The predictability provided by static schedules can help labs better allocate resources. The rigid approach of these schedules dictates how many staff members need to be available to monitor or maintain workflow, when results can be expected, and which instruments will be available for other processes.

As with many lab automation solutions, the main drawback of static schedules comes from the fact that experiments rarely follow simple paths and rules. Any variable can cause the workflow to stall, delay, or even fail completely.

With static scheduling, fault tolerance is low, load balancing is difficult, and adaptability is limited.

Dynamic schedules

Dynamic schedulers make decisions based on information provided during job execution, taking into account data from various real-time events and allocating resources based on current workload and status information.

Example of handling instrument errors from a dynamic schedule

Video Source: Automata

Advantages and disadvantages of dynamic schedules

The built-in adaptability of dynamic schedules reduces the risk of workflow failure because the scheduler will seek an alternate path to successful execution.

For example, dynamic schedulers can reassign failed or delayed tasks without waiting for intervention from the master node or operator.

This not only increases reliability and eliminates the need for manual monitoring, but also provides detailed data that is ideal for device optimization and subsequent experimental design.

Dynamic scheduling is a complex process that requires advanced programming and increased initial setup time. There is also the need to create and synchronize multiple threads.

The inability to pre-plan operations for optimization may limit experimental performance to some extent or introduce variables that adversely affect the consistency and quality of results.

Dynamic re-scheduling

Automata’s LINQ Cloud Scheduler leverages the benefits of static and dynamic schedulers to provide users with the best of both worlds.

This powerful software can take into account known constraints to efficiently plan workflows and allocate resources, applying state-of-the-art problem-solving algorithms to ensure workflow consistency and efficiency.

LINQ Cloud dynamic scheduling module takes into account:

• Time constraints
• Known conditions
• Data Transfer Events

This enables accurate prediction of experiment completion time and expected results. It does this while facilitating:

• Batch parallelization
• Real-time error handling
• Dynamic redirection
• Blockage prevention

The combination of these benefits gives users complete confidence in the successful completion of their workflow.

Enable error handling and constraints with LINQ Cloud Scheduler

Video Source: Automata

Benefits of LINQ Cloud and dynamic rescheduling

The LINQ Cloud scheduler focuses on enabling lab instrumentation to be maximized while reducing its dependence on people and other limited resources. Its ultimate goal is to optimize workflow and, ultimately, its outcomes.

Scheduler takes the best features of static and dynamic schedules and combines them with the powerful workflow software Automata. The result is a scheduling platform that is easy to use, immediately effective, and supports labs on their journey to automation.

“We have created a scheduler that outperforms all applications by offering explicit constraint management and multi-test work cells. By combining static solving with dynamic response capabilities, more labs will be able to automate and benefit from reliable automated workflow execution.” – Daniel Siden, Chief Product Officer, Automata.

Thanks

Manufactured from materials originally developed by Automata Technologies Ltd.

About Automata

A collaboration with a world-leading research laboratory, Automata brings complete workflow automation to labs struggling with the constraints of their own environment.

Accelerating the evolution of innovation

When two architects from Zaha Hadid’s research lab first got involved with robotics, their idea was to explore applications specific to architectural engineering.

But they soon discovered that modern automation wasn’t just unnecessarily complicated—it actively limited innovation. And not just in their industry—in many others as well. It was clear that robotic automation was a field where their combined experience in computational research and design could make a real difference. Assembling a team of industry experts, they founded Automata with a clear goal: to enable new opportunities for innovation through automation.

A clearer path forward

Automata focused on the industry where it believed its expertise could have the greatest impact – life sciences, particularly in biology laboratory environments.

Since then, the team has worked closely with leading pathology labs to develop pioneering protocols that enable labs to scale with precision

Automata Labs is a product of this philosophy – simplifying lab environments and empowering people who continually strive for progress.

---

Sponsored Content Policy: News-Medical.net publishes articles and related content that may come from sources with which we have a commercial relationship, provided that such content adds value to News-Medical.Net’s core editorial philosophy of educating and informing users of the site who are interested in medical research, science, medical devices and treatments.