Name
Abstract | ||
|---|---|---|
In this work, we propose a compromised large-scale neighborhood, which is embedded in simulated annealing to solve a cargo loading planning problem arising in logistics industry. It is "compromised" because it makes a tradeoff between the extensive backward checking work incurred in traditional subset-disjoint restriction and the possible infeasibility resulting from the relaxing the restriction. Extensive experiments have shown the competitive advantages of the heuristic approach. The proposed neighborhood search method is generally applicable. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76928-6_82 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
large-scale neighborhood search heuristic,simulated annealing,proposed neighborhood search method,possible infeasibility,cargo loading planning problem,competitive advantage,traditional subset-disjoint restriction,heuristic approach,logistics industry,large-scale neighborhood,extensive experiment,heuristic | Simulated annealing,Mathematical optimization,Heuristic,Incremental heuristic search,Computer science,Competitive advantage,Neighborhood search | Conference |
Volume | ISSN | ISBN |
4830 | 0302-9743 | 3-540-76926-9 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||