Using diversity for optimizing cycle time for a pick-and-place machine
Mark de Berg et al.
What the paper says
Abstract Assigning feeders and nozzles is an important step in the setup of pick-and-place machines. We study this feeder and nozzle assignment problem (FNAP) for a representative machine from Kulicke & Soffa (K&S). The goal is to assign nozzles and feeders so that a given set of printed circuit boards can be produced while minimizing cycle time. The heuristic currently used by K&S reliably finds feasible assignments but performs poorly regarding nozzle exchanges, which are strongly correlated with cycle time. To address this, we propose a novel algorithm that tries to find k feasible assignments that are as diverse as possible, for some parameter $$k>1$$ . By maximizing solution diversity, our approach increases the likelihood of finding initial solutions that can yield shorter cycle times after further optimization. On real-world instances, our algorithm achieves at least half the theoretical maximum diversity in over 70% of cases for $$k \le 10$$ . It also produces assignments with estimated nozzle exchanges roughly half those of the current heuristic in 95% of cases, while reducing the number of instances incorrectly classified as infeasible at only a small cost in runtime.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.