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Abstract:Many cities have developed bicycle sharing systems. This project points out that a key element in their
success and sustainability is an understanding of the spatial patterns of supply and demand. This project
tackles the re‐balancing issue where bikes must be removed from full stations and replenished at empty
ones. Taking inspiration from the lot size problem, and capacitated vehicle routing, the project devises a
rolling window approach to this problem, in essence representing the amount of foresight that the
system planner can use to determine solutions. The submitted paper shows that a problem which might
be unsolvable with a more myopic approach can actually be solved satisfactorily when a longer window
range is used. The penalty for this added window (look‐ahead) is of course added computational effort
but the paper shows that this effort is manageable for the relatively small sample system analyzed here
(Columbus, Ohio). The paper reporting these efforts is in review. The paper is illustrated with results
from 54 problem instances derived from variants of the base data. The results are derived on a standard
computer with widely available tools such as Python and CPLEX.
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