Name
Abstract | ||
|---|---|---|
This paper proposed a state-of-the-art local search for solving Flexible Demand Assignment problem (FDA) which considers the balance between revenue and cost in demand assignment. Different than the published studies, our research splits the FDA problem into three core subproblems as operators for neighborhood construction. The three specified subproblems One Bin Repack, Two Bins Repack and Unpack are proposed completely based on mathematical modelling, computational complexity, executive conditions and greedy solving methods. Benchmark experimental results have shown that the proposed local search improved to the best published heuristics by 2.34%. |
Year | Venue | Keywords |
|---|---|---|
2004 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | assignment problem |
Field | DocType | Volume |
Revenue,Mathematical optimization,Bin,Computer science,Generalized assignment problem,Heuristics,Operator (computer programming),Local search (optimization),Demand assignment,Computational complexity theory | Conference | 110 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Wang | 1 | 142 | 9.55 |
| Andrew Lim | 2 | 937 | 89.78 |
| Hong Chen | 3 | 99 | 23.20 |