A collaborative ant colony metaheuristic for distributed multi-level lot-sizing

TL;DR

This paper introduces a novel ant colony optimization metaheuristic designed for collaborative planning in distributed multi-level lot-sizing, effectively coordinating private information among decision makers to improve overall solutions.

Contribution

It develops a new search graph and voting-based mechanisms for distributed optimization, outperforming existing methods on benchmark instances.

Findings

Significantly reduces deviation from best solutions, especially on large instances.

Outperforms existing approaches with an average deviation of only 5%.

Effective integration of private information via voting and local search.

Abstract

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.