Skip to main content

Schedule Optimization

Kanoa MES Accelerator

Schedule Optimization

Created using: Kanoa MES 1.14.3 Last updated: February 18, 2026

Coming Soon
Schedule Optimization Accelerator

On this page

Overview​

The Schedule Optimization Accelerator is a deterministic, fully transparent reference implementation that demonstrates how production scheduling logic can be built directly on top of Kanoa MES data structures using Ignition Perspective and Jython scripting.

This accelerator is not intended to be a black-box optimization engine. Instead, it provides clear, explainable scheduling heuristics that operate using core MES data that is already available in Kanoa:

  • Production Orders
  • Asset–Item rate mappings
  • Existing production/downtime events
  • Shift availability windows

The goal is to show how structured MES data can drive algorithmic scheduling outputs that are immediately usable in the Kanoa Ops Scheduler.

What this accelerator is​

  • A practical “how-to” reference for pulling scheduling inputs from Kanoa, running a scheduling heuristic, and committing events back into Kanoa.
  • A framework designed for clarity and customization.

What this accelerator is not​

  • A one-size-fits-all scheduling optimizer.
  • A replacement for enterprise-grade optimization solvers (OR tools, MILP, etc.).

How It Works​

This accelerator implements a complete scheduling loop:

1) Gather schedule inputs

  • Pull production orders to schedule.
  • Pull asset–item rate capabilities (what can run where, and at what rate).

2) Gather calendar constraints

  • Pull existing scheduled events (maintenance, downtime, scheduled production).
  • Pull shift calendars to determine asset availability windows.

3) Run a deterministic scheduling heuristic

  • Apply one of the included algorithms (below).
  • Generate a list of proposed schedule events (including optional explicit changeover blocks, when needed).

4) Commit results back into Kanoa

  • Convert proposed events into Kanoa schedule blocks.
  • Optionally clear existing schedule blocks first (for repeatable demo runs / iteration).
  • Update affected orders to Scheduled after they are placed.

Kanoa MES Features Used​

This accelerator is intentionally built on “standard primitives” that exist in most Kanoa deployments:

  • Production Orders
  • Items + Asset–Item relationships (rates)
  • Scheduling / production calendar events
  • Shift calendars (availability windows)

Input Data Model​

The accelerator collects structured datasets in JSON-like form from Kanoa System Function calls:

1) Orders​

Using system.kanoa.order.getProductionOrders(...)

Each order includes:

  • workOrderId
  • itemId
  • reqdQty
  • dueDate

2) Asset Item Rates​

Using system.kanoa.item.getAssetItems(...)

Defines production capability per asset/item:

  • assetId
  • itemId
  • scheduleRate (units/hour)

3) Existing Events​

Using system.kanoa.schedule.getCalendarEvents(...)

Represents pre-blocked time (maintenance, downtime, previously scheduled production).

4) Shift Events​

Using system.kanoa.schedule.getCalendarEvents(...)

Defines when assets are available for production.

Core Scheduling Rules​

All included algorithms respect these hard constraints:

  • An order may only run on assets configured for its item.
  • Production must occur within defined shift windows.
  • Proposed events may not overlap existing blocked time.
  • Runs are contiguous within a single window (no automatic splitting).

Implemented Scheduling Algorithms​

This accelerator currently includes four deterministic heuristics.

1) Baseline EDD (Simple First-Fit)​

Purpose: Fast, minimal-complexity baseline.
Behavior: Sort by Earliest Due Date, schedule into the first eligible asset where the order fits.

2) On-Time EDD (Best Slot per Order)​

Purpose: Improve due-date performance while maintaining EDD ordering.

Behavior: For each order (EDD sequence), evaluate all eligible assets and choose the earliest feasible completion slot; prioritize on-time completion.

3) Global Least-Slack Priority​

Purpose: Maximize the number of orders completed on time.
Behavior: Globally greedy selection based on tightest slack (urgency) rather than strict EDD ordering.

4) Item Batch Dispatch (Changeover-Friendly Scheduling)​

Purpose: Reduce changeovers while maintaining due-date sensitivity.
Behavior: Builds same-item batches within a lookahead window, simulates candidate schedules on all eligible assets, and selects the best plan using deterministic scoring.

Changeover Handling​

Item Batch Dispatch will auomatically insert changeovers between produciton runs of different items.

  • Changeover duration is configurable (uniform duration for the demo).
  • Changeovers are explicitly inserted as schedule events (for traceability and accurate utilization modeling).

Implementation & Configuration​

Included Resources​

This accelerator package contains:

Perspective View​

  • accelerators/scheduleOptimizer/main — planner view used to run the optimizer and commit schedule events.

Script Module​

  • accelerators/autoSchedule — scheduling helpers + the four algorithm implementations described above.

Step 1 — Import the View and Scripts​

Import the provided Perspective view and the accelerators/autoSchedule script module into your Ignition project.

The scripts are Jython 2.7 compatible and do not rely on external libraries.

Step 2 — Ensure You Have Schedulable Assets and Shifts​

This demo expects:

  • A set of assets (typically under an “Area” asset) that are intended to be scheduled
  • Shift events defined so the optimizer can compute availability windows

Step 3 — Ensure Asset–Item Rates Are Defined​

The optimizer requires each scheduled item to have at least one eligible asset with a defined schedule rate (units/hour).

Step 4 — Run the Optimizer and Commit Schedule Blocks​

Open the Schedule Optimization Accelerator planner view and:

  1. Select the target Area you want to schedule.
  2. Choose an optimization algorithm:
    • Baseline EDD
    • On-Time EDD
    • Global Least-Slack Priority
    • Item Batch Dispatch (changeover-friendly)
  3. Select your date range / horizon window.
  4. (Optional) Enable “Clear Schedule First” if you want repeatable runs for demos and testing.
  5. Run the optimization.
  6. Review logs/output and confirm that schedule blocks were created.

If the optimizer cannot schedule all orders, the view reports which orders were unscheduled (typically due to capacity or eligibility constraints).

Detailed Walkthrough​

accelerators/scheduleOptimizer/main​

Purpose​

Planner-facing view used to:

  • Gather input data from Kanoa
  • Run a selected scheduling heuristic
  • Write resulting schedule blocks to the production calendar

What the view does​

At a high level, the view:

  • Loads the selected asset scope (usually an Area and its children)
  • Pulls:
    • Orders to schedule
    • Asset–item rates
    • Existing schedule conflicts
    • Shift availability windows
  • Runs the selected algorithm and produces proposed schedule events
  • Inserts changeover events (for the batch dispatch approach)
  • Commits the resulting events as schedule blocks in Kanoa

Operator / Planner controls​

  • Select Area: defines which assets will be considered
  • Select Algorithm: chooses the scheduling heuristic
  • Date Range: scheduling horizon
  • Changeover Duration: used by changeover-aware scheduling
  • Clear Schedule First: optional reset for demo/testing

accelerators/autoSchedule​

Purpose​

Script module containing:

  • Helper utilities (interval merging/subtraction, slot finding, eligibility filtering, duration math)
  • The four deterministic scheduling heuristics

Design principles​

This module is explicitly designed to prioritize:

  • Determinism (same inputs → same outputs)
  • Transparency (no black-box solver)
  • Educational value (easy to read, modify, and extend)
  • No external dependencies (Ignition-native, Jython 2.7 compatible)

Conclusion​

Common Use Cases​

This accelerator is useful for:

  • Demonstrating how to pull scheduling inputs from Kanoa MES and produce actionable schedules
  • Showing the tradeoffs between “simple and fast” vs “better on-time performance” heuristics
  • Providing a starting point for implementation teams to design plant-specific scheduling rules

Expansion Opportunities​

Common next steps for real-world deployments:

  • Add constraint modeling (sequence-dependent changeovers, tooling, labor, material availability)
  • Allow run splitting across multiple windows (optional partial scheduling is a first step)
  • Add “what-if” comparisons (multiple scenarios without committing schedule blocks)
  • Add scoring outputs (tardiness, total changeovers, utilization, schedule stability)

Summary​

The Schedule Optimization Accelerator demonstrates a practical pattern for:

  • Programmatically accessing orders, asset capability, schedule conflicts, and shift windows in Kanoa MES
  • Running deterministic optimization heuristics (clear, explainable logic)
  • Committing recommended schedules back into the Kanoa production calendar for immediate use