Amir SohailAUSTIN, Texas, July 20, 2026 (ZM NEWSWIRE) — AutoScheduler.AI, a leading AI-based Decision Intelligence Platform, today announced the Next-Generation Optimization Engine, a re-architected version of the company’s optimization engine.
AutoScheduler’s original engine unifies plant schedules, inventory locations, and carrier workflows into a single execution plan, built around a predefined operational flow. Next-Generation Operational Engine is built on a different premise: a plan should be modeled the way the company actually operates, not forced into a generic template. Operations teams define the steps in their flow, the valid ways to move between those steps, and the resources, speeds, capacities, and costs for each path. The result is a dynamic operational twin of the warehouse, one that reasons across every valid combination and continuously coordinates even the most complex automation and multi-step flows, and selects the best plan every time.
“Most warehouse software still treats the WMS as the foundation for decision making, but as operations have gotten more complex, they find that it can tell you what happened, but not what to do next,” said Keith Moore, CEO of AutoScheduler.AI. “AutoScheduler.AI doesn’t just execute a plan someone else designed for a generic warehouse; it reasons through your actual operation, coordinating every piece of equipment and every flow, and builds the best plan for it, even as conditions change.”
What’s New in Next-Generation Optimization Engine
Orchestrating complex automation and multi-step flows. The Next-Generation Optimization Engine models the real flow of a site, not a generic one, so operations teams get plans that reflect how the floor actually runs, including coordination across AGVs, ASRS, shuttle systems, automated loading devices, and multi-step P&D transitions. For technical teams, new site types and customers can be onboarded through configuration rather than custom engineering.
New capabilities out of the box. Modeling the real flow unlocks a set of capabilities that used to require custom builds, including prestaging and partial staging, replenishment, multi-step and divergent flows such as short-term versus long-term storage, and non-standard units of measure, such as raw materials measured in tons or catch-weight. Each of these opens a previously out-of-reach use case and widens the range of warehouses AutoScheduler can run on.
Built to scale, fast enough to matter. Tested against live data from one of the world’s largest CPG manufacturers, the Next-Generation Optimization Engine reproduced the trusted results of AutoScheduler’s established engine in a fraction of the time, fast enough to re-optimize a plan not once a shift, but continuously as conditions change.
A sharper Warehouse Decision Agent. The Warehouse Decision Agent has always understood site- and shift-level performance. With the Next-Generation Optimization Engine, it can now reason down to individual flows and the steps and sub-steps within them, giving the agent a materially deeper layer of intelligence to work from.
One Engine, More Kinds of Warehouses
Because the Next-Generation Optimization Engine models a site’s actual flow and lets each step carry its own rules, the same engine can now stretch across industries and operations that previously required custom builds, including retail and grocery distribution, raw materials manufacturing, value-added services, sortation-driven distribution, bulk and raw-materials handling, finished-goods facilities attached to production, multi-step and split storage, and micro-fulfillment and each-picking sites.
The same flexibility extends to operations AutoScheduler.AI is now bringing onto the platform, including cold chain and temperature-zoned facilities, 3PLs managing multiple clients under one roof, regulated hazmat and pharma operations, sites running mixed automation such as ASRS, AMRs, and conveyor systems alongside manual labor, value-added services like kitting and light assembly, cross-dock and flow-through facilities, returns and reverse logistics, retail store-sequence loading, and import and port-adjacent operations.
“The Next-Generation Optimization Engine reflects how we build at AutoScheduler.AI: real customer operations shape the roadmap, and innovation shows up as capability you can actually use to drive better decisions,” Moore said. “This is one part of a larger investment we’re making in product development, guided directly by customer feedback and where the market is heading. The Next-Generation Optimization Engine is the next step in that momentum, and we’ll continue announcing releases as that work lands.”
About AutoScheduler.AI
AutoScheduler.AI is a Warehouse Decision Intelligence Platform that layers on top of existing WMS, LMS, YMS, and automation systems to orchestrate labor, dock, and inventory decisions in real time. Powered by a continuously running optimization engine, it harmonizes data across every connected system and intelligently allocates the workforce, sequences wave releases, assigns dock doors, and flags at-risk shipments, adapting instantly as conditions change to improve service, increase throughput, and lower operating costs. Its Warehouse Decision Agent puts this intelligence directly in users’ hands, always on call to surface the plan and the reasoning behind every recommendation. For more information, visit http://www.AutoScheduler.AI.
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