Name
Title | ||
|---|---|---|
Distributed resource allocation via local choices: A case study of workforce allocation |
Abstract | ||
|---|---|---|
This paper describes a solution to workforce allocation, modelled as a distributed system. The novelty of the solution resides in the fact that it is a natural and versatile formulation that combines an agent-based model with rule-based expressions of allocation strategies and multi-criteria optimisation expressions of allocation objectives. The problem space is structured on disjoint domains. Each domain has two agents: one in charge of its engineers and one in charge of its jobs. In the implemented system, agents have complete decision power over the allocation of their resources/requirements. However, the general solution is readily extendable towards the inclusion of central agents with specialist roles. Workforce allocation is modelled here as an iterative communication process, based on a 4-step communication protocol, between workforce/engineers' agents, on one hand, and jobs agents, on the other. Choices (decisions) regarding the allocation are made locally, within each agent. A global allocation emerges from this interaction/communication. The paper describes the context of this solution (including the problem description), the general model and solution, and specific implementations. The latter illustrate the emergence of good global solutions from the distributed algorithm. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.3233/KES-2005-9405 | KES Journal |
Keywords | Field | DocType |
iterative communication process,solution resides,resource allocation,agent-based model,general solution,allocation strategy,allocation objective,4-step communication protocol,workforce allocation,global allocation,local choice,case study,good global solution | Disjoint sets,Expression (mathematics),Computer science,Implementation,Artificial intelligence,Management science,Communications protocol,Max-min fairness,Workforce,Operations research,Distributed algorithm,Resource allocation,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1327-2314 |
Citations | PageRank | References |
6 | 0.59 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marian F. Ursu | 1 | 163 | 15.52 |
| Botond Virginas | 2 | 192 | 11.29 |
| Gilbert Owusu | 3 | 102 | 22.66 |
| Chris Voudouris | 4 | 14 | 3.30 |