Problem
Develop scalable optimization models that integrate multiple planning domains, maximize operational objectives under realistic constraints, and provide decision-ready schedules.
- +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).
Delivered
- +Validated optimization models (MIP).
- +Scenario analysis tools for 'what-if' evaluation.
- +Integration with production systems for rolling horizon implementation.
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.
Where it ran
- +Production & Capacity Planning.
- +Maintenance & Engineering Operations.
- +Workforce & Resource Management (identifying skill gaps).
- +Supply Chain & Logistics synchronization.
Stack
Mathematical Optimization · Mixed-Integer Programming · Constraint Satisfaction · Capacity Planning
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Photo: Inside Bright Advanced Semiconductor Production Fab Cleanroom with Working Overhead Wafer Transfer System, by IM Imagery, Adobe Stock 498638756