A collaborative ant colony metaheuristic for distributed multi-level lot-sizing
This addresses coordination challenges for self-interested decision makers in supply chain management, offering a significant improvement over existing methods.
The paper tackles the distributed multi-level lot-sizing problem by proposing an ant colony optimization metaheuristic with collaborative planning, reducing the average deviation from best known solutions from 46% to 5% on large instances.
The paper presents an ant colony optimization metaheuristic for collaborative planning. Collaborative planning is used to coordinate individual plans of self-interested decision makers with private information in order to increase the overall benefit of the coalition. The method consists of a new search graph based on encoded solutions. Distributed and private information is integrated via voting mechanisms and via a simple but effective collaborative local search procedure. The approach is applied to a distributed variant of the multi-level lot-sizing problem and evaluated by means of 352 benchmark instances from the literature. The proposed approach clearly outperforms existing approaches on the sets of medium and large sized instances. While the best method in the literature so far achieves an average deviation from the best known non-distributed solutions of 46 percent for the set of the largest instances, for example, the presented approach reduces the average deviation to only 5 percent.