What is dynamic replanning?

The advent of lab automation has significantly improved efficiency from various perspectives, including increasing scientists' walkaway time and boosting throughput in response to demand. Automation has made laboratory operations easier to predict, plan, and optimize.

The key to maximizing these efficiencies lies in deploying an automation platform that delivers both robotically and digitally. The platform must seamlessly connect instruments, streamline data flows, and support dynamic lab scheduling at both a software and hardware level.

There are a myriad of advantages to the use of efficient experiment scheduling software. Users can better ensure that time, instrumentation, and staff are used to their full potential, while there will also be notable improvements to reliability, consistency, and experiment success rates.

It can be difficult to find a scheduler that efficiently assigns tasks to a set of heterogeneous resources. The scheduler must also take into account the constraints of each specific resource, respond to changes in these parameters, and consistently deliver a complete workflow.

Image Credit: Gorodenkoff/Shutterstock.com

The importance of schedulers

Good experiment scheduling software is key to streamlining workflows and processes in the lab. A robust scheduler can:

• Support the development and implementation of new and unique protocols and workflows.
• Help analyze performance to optimize workflows and individual instruments better.
• Enable virtual workflow simulations for improved experiment design and testing timing.
• Facilitate the simultaneous execution and tracking of several workflows, provided instruments, robots, and transport systems are fully integrated.

Most lab systems have evolved from tracking experiments on paper or via spreadsheets. Two popular types of schedulers, static and dynamic, are now commonplace.

Schedulers generally employ rule-based algorithms, which consider specific scheduling goals or issues, or mathematical optimization algorithms, which aim to minimize workflows’ end-to-end execution time.

Static schedulers

Static schedulers make decisions based on predetermined constraints. They generally allocate tasks prior to commencing execution, and they do not have the capacity to alter this during the run time, for example, in response to information from task-tracking events.

Example instrument error handling from a static scheduler

Video Credit: Automata

Advantages and disadvantages of static schedulers

As known parameters are employed, static schedulers are well-suited for simple, predictable processes. They are reliable and robust when the data in use is accurate and multiple threads, synchronization, or parallel operation are not needed.

This straightforward approach may reduce risk but can also limit time efficiencies in generalized applications.

The predictability afforded by static schedulers can help labs better allocate resources. These schedulers’ rigid approach dictates how many staff must be available to monitor or support the workflow, when results can be expected, and which instruments will be available for other processes.

Like many lab automation solutions, the primary disadvantage of static schedulers stems from the fact that experiments rarely follow simple routes and rules. Each variable could cause the workflow to pause, delay, or fail even entirely.

Fault tolerance is low with static scheduling, load balancing is challenging, and adaptability is limited.

Dynamic schedulers

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

Example instrument error handling from a dynamic scheduler

Video Credit: Automata

Advantages and disadvantages of dynamic schedulers

Dynamic schedulers’ built-in adaptability lowers any risk of workflow failure because the scheduler will look for an alternative route to successful execution.

For example, dynamic schedulers may reassign failed or delayed tasks without waiting for intervention by a master node or operator.

This not only improves reliability and removes the need for manual monitoring; it also provides detailed data ideal for instrument optimization and subsequent experiment design.

Dynamic scheduling is a complex process, requiring advanced programming and increased initial set-up times. There is also a need to create and synchronize multiple threads.

The inability to pre-plan executions for optimality can hinder experiment throughput to some degree or introduce variables that adversely affect the consistency and quality of results.

Dynamic replanning schedulers

The LINQ Cloud scheduler from Automata leverages the benefits of both static and dynamic schedulers to offer users the best of both approaches.

This powerful software can consider known constraints for effective workflow planning and resource allocation, employing state-of-the-art solving algorithms to ensure that workflow delivery is consistent and efficient.

LINQ Cloud’s dynamic replanner scheduling engine takes into account:

• Time constraints
• Known conditionals
• Data transfer events

This enables an accurate prediction of the experiment’s time to completion and expected results. It does this while also facilitating:

• Batch parallelization
• Real-time error handling
• Dynamic rerouting
• Deadlock prevention

These advantages combine to afford users full confidence in successful workflow completion.

Run error and constraint handling by LINQ Cloud’s scheduler

Video Credit: Automata

Advantages of LINQ Cloud and dynamic replanning scheduling

The LINQ Cloud scheduler focuses on enabling the maximization of a lab’s instrumentation while limiting its reliance on people and other scarce resources. Its ultimate goal is to optimize the workflow and, ultimately, its results.

The scheduler takes the most advantageous features of static and dynamic schedulers and combines these with Automata’s powerful workflow builder software. The result is a scheduling platform that is easy to use, immediately effective, and supports labs on their journey toward automation.

“We have created a scheduler that outperforms across applications, featuring explicit constraints handling and multi-assay workcells. By combining static solving with dynamic response capabilities, more labs will be able to automate and reap the benefits of reliable automated workflow execution.” - Daniel Siden, Director of Product, Automata.

Acknowledgments

Produced from materials originally authored by Automata Technologies Ltd.

About Automata

Born from a world-leading research lab, Automata is making total workflow automation accessible to labs frustrated by the limitations of their own environment.

Accelerating the innovation evolution

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

But they soon discovered that modern automation wasn’t just unnecessarily complex – it was actively restricting innovation. And not just within their industry – within many others too. 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, Automata was founded, with a clear aim: to enable new opportunities for innovation with automation.

A clearer path to progress

Automata’s focus narrowed on an industry where they felt their expertise could have the most impact – life sciences, and particularly within biolab environments.

Since then, the team has been working closely with leading pathology labs to pioneer protocols that enable labs to scale with precision

Automata Labs is the product of that philosophy – simplifying lab environments and empowering the people working tirelessly in the pursuit of progress.

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