Optimizing Repair Shop Station Costs
Reducing Idle and Overtime Costs with Smart Scheduling
This post explains how our scheduling model minimizes idle and overtime costs in multi-station automotive repair shops. Using a Mixed-Integer Linear Programming approach powered by Google OR-Tools, we optimize the sequence and allocation of repair tasks so stations are used efficiently throughout the day. Tested on real repair shop data, our method reduces station utilization costs by up to 47% compared to manual scheduling. An interactive demo lets you see the results for a sample repair day, showing exactly how better scheduling translates into lower operational costs.
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1. Introduction: The Cost of Inefficient Scheduling
Explain why idle and overtime costs matter in repair shops:
- Idle stations mean wasted labor and equipment capacity.
- Overtime means paying more for the same output.
- Manual scheduling often leads to both problems.
Goal: Show that smart scheduling can keep stations working at the right pace throughout the day.
- Idle stations mean wasted labor and equipment capacity.
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2. Scheduling Model
What it does:
- Assigns and sequences repair tasks across stations to minimize total idle + overtime costs.
Key constraints:
- Each task must be done at an eligible station.
- A station can work on only one task at a time.
- A car can be at only one station at a time.
Inputs needed:
- Repair durations per car-task
- Eligible stations for each repair
- Shift length + max overtime
- Cost per minute for idle and overtime
- Assigns and sequences repair tasks across stations to minimize total idle + overtime costs.
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3. The Mathematical Model
Here’s a simplified form of the model for the blog:
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4. How We Solve It (Exact Approach)
- For small to medium problem sizes, we solve the Mixed-Integer Linear Programming (MILP) model directly using Google OR-Tools with a MILP solver.
- This guarantees the optimal schedule that minimizes station costs.
- For small to medium problem sizes, we solve the Mixed-Integer Linear Programming (MILP) model directly using Google OR-Tools with a MILP solver.
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5. Metaheuristics as an Option (For Larger Problems)
- For larger repair shops (many cars & stations), solving exactly can take too long.
- We use metaheuristics like the Non-dominated Sorting Genetic Algorithm II (NSGA-II) or customized genetic algorithms.
- These produce near-optimal solutions fast, making them practical for daily scheduling.
- Metaheuristics allow flexibility — we can also consider other objectives like flow time.
- For larger repair shops (many cars & stations), solving exactly can take too long.