Refine
Year of publication
- 2011 (3) (remove)
Document Type
Conference Type
- Konferenzartikel (2)
- Sonstiges (1)
Language
- English (3)
Has Fulltext
- no (3) (remove)
Is part of the Bibliography
- yes (3) (remove)
Keywords
- RoboCup (1)
Open Access
- Bronze (2)
- Open Access (2)
- Closed Access (1)
This paper describes the magmaOffenburg 3D simulation team trying to qualify for RoboCup 2011. While last year’s TDP focused on the tool set created for 3D simulation in this year we describe the further improvement in this tools as well as some new features we implemented focusing on heterogeneous robot models which seem to be used in RoboCup 2012.
An additional tool was written to simply generate situation-dependent strategies. Furthermore some tools, described last year, are now integrated in one single GUI to easy things up.
Existing approaches solving multi-vehicle pickup and delivery problems with soft time windows typically use common benchmark sets to verify their performance. However, there is a gap from these benchmark sets to real world problems with respect to instance size and problem complexity. In this paper we show that a combination of existing approaches together with improved heuristics is able to deal with the instance sizes and complexity of real world problems. The cost savings potential of the heuristics is compared to human dispatching plans generated from the data of a European carrier.
In previous work we [1] and other authors (e.g. [2]) have shown that agent-based systems are successful in optimizing delivery plans of single logistics companies and are meanwhile successfully productive in industry. In this paper we show that agent-based systems are particularly useful to also optimize transport across logistics companies. In intercompany optimization, privacy is of major importance between the otherwise competing companies. Some data has to be treated strictly private like the cost model or the constraint model. Other data like order information has to be shared. However, typically the amount of orders released to other companies has also to be limited. We show that our agent-based approach can be easily fine tuned to trade off privacy against the benefit of cooperation.