link to the series: https://www.youtube.com/watch?v=XFB8rn2 ... ObizXMXBPo.
The requirements are:
- must be exact
- you can change the items you want loaded into the train on a combinator
- can remove garbage items/ if some item's amount is to high
- can handle multiple wagons
Fooluaintblack's best design:
features:
- combinators: 17 decider combinators, 7+2l arithmetic combinator, and 3+l constant combinators where l is the number of wagons
- height: 12 blocks
- speed: 834 ticks
- test setup blueprint: 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
features:
- combinators: 17+32l decider combinators, 7+25l arithmetic combinator, and 2+l constant combinators where l is the number of wagons
- height: 26 blocks
- speed: 777 ticks
- test setup blueprint: 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
If anyone is interested then I will try to explain how it works.
I also want to challenge you, the reader, to come up with a faster design.
ps: when you open the blueprint, send the train first to the other station.
ps: English isn't my native langue so sorry for grammar/spelling mistakes.
edit:
I managed to improve my new design even more
Currently my best design:
features:
- combinators: 16+37l decider combinators, 8+18l arithmetic combinator, and 4+l constant combinators where l is the number of wagons
- height: 24 blocks
- speed: 658 ticks
- test setup blueprint: 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