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Integrated Operations Planning & Optimization

A constraint optimization framework for production and resource planning, designed to resolve conflicts across maintenance, production, and quality activities.

Semiconductor fab transport system representing integrated operations planning and optimization.
Solve Time
<1 sec
Activities Planned
400+
Dynamic Opt.
Level 4

Client context

A flexible optimization framework developed to solve complex scheduling and resource allocation problems in high-volume manufacturing.

The approach uses mathematical programming to balance competing objectives - maximizing critical activities, optimizing timing, and respecting capacity constraints - while ensuring production targets are met.

Problem

  • +Siloed planning: Different functions (maintenance, production, quality) planned independently, creating conflicts.
  • +Capacity bottlenecks: Critical activities compete for limited resources.
  • +Complex constraints: Time windows and qualification rules make manual planning infeasible at scale.

Approach

  • +Multi-objective optimization: Balances throughput, delays, and costs.
  • +Constraint integration: Handles qualifications, capacity limits, and sequencing rules simultaneously.
  • +Progressive model levels: Scales from basic scheduling to dynamic performance modeling (time-varying capabilities).

Measured result

  • +Efficiency: Sub-second solve times for 80-400+ activities across 20+ resources.
  • +Performance-Aware: Strategic timing creates additional capacity through performance restoration.
  • +Strategic Benefits: Proactive conflict identification weeks ahead implies system-level optimization.

Next step

Have a similar operational constraint?

Bring the current baseline, target, and available data. PrismaWerk will help define the first measurable pilot.

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