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MethodAdvanced02.12
Optimization Core

Heuristics & Metaheuristics

Good answers when exact optimization is too slow or brittle.

Overview

Heuristics & Metaheuristics focuses on good answers when exact optimization is too slow or brittle. In the map of OR, it connects Local search, Tabu search, Simulated annealing to decisions that must be modeled, solved, explained, and revised as evidence changes.

Local search, tabu search, simulated annealing, genetic algorithms, large neighborhood search, and matheuristics trade guarantees for speed and scale. The practical use case is clearest in Routing, Scheduling, Packing, Network design, where the method helps turn constraints and tradeoffs into a decision artifact someone can inspect.

Core ideas

Local search

Moves through neighboring solutions and depends on neighborhood design more than slogans about heuristics.

Tabu search

Tabu search is a core checkpoint for Heuristics & Metaheuristics: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Simulated annealing

Simulated annealing is a core checkpoint for Heuristics & Metaheuristics: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

LNS

LNS is a core checkpoint for Heuristics & Metaheuristics: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Matheuristics

Matheuristics is a core checkpoint for Heuristics & Metaheuristics: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

How to use it

  1. 1Start with Routing: write the decision, time horizon, actors, and objective in operational language.
  2. 2Translate the problem into Local search, Tabu search, and Simulated annealing; define units and data sources for each one.
  3. 3Build a small instance of Heuristics & Metaheuristics that can be solved or simulated by hand inspection before using full production data.
  4. 4Compare the recommendation against a baseline policy, not just against mathematical optimality.
  5. 5Document assumptions, sensitivity results, and the conditions under which the recommendation should be revisited.

Applications

RoutingSchedulingPackingNetwork design
  • Routing: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Scheduling: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Packing: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Network design: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.

Common pitfalls

  • Applying Heuristics & Metaheuristics because the label sounds appropriate while leaving the actual decision boundary vague.
  • Treating Local search as a technical detail instead of a modeling choice that affects the recommendation.
  • Reporting one answer without showing sensitivity to demand, capacity, costs, or behavioral assumptions.
  • Ignoring implementation details such as data quality, explainability, ownership, and how users will override bad recommendations.

Resources