2012/04/02 by Tobias Buer, Jörg Homberger, Hermann Gehring · 20 citations
Computer Science · Economics, Econometrics and Finance · Engineering · #Advanced Multi-Objective Optimization Algorithms #Ant colony #Ant colony optimization algorithms #Benchmark (surveying) #Game Theory and Voting Systems #Graph #Metaheuristic #Parallel metaheuristic #Set (abstract data type) #Vehicle Routing Optimization Methods #Voting #acm:68T20 #acm:68W15 #acm:90B30 #acm:91B10 #acm:91B12 #acm:91B14 #cs.AI #cs.DC #msc:68T20 #msc:68W15 #msc:90B30 #msc:91B10 #msc:91B12 #msc:91B14
paper · pdf · doi:10.1080/00207543.2013.802822
published in International Journal of Production Research 51(17), 5253-5270 (Taylor & Francis)
arxiv created 2012/04/02 · openalex publication_date 2013/07/25 · arxiv updated 2014/06/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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 are 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 75% for the set of the largest instances, for example, the presented approach reduces the average deviation to 7%.