Suppose that I'm a Fedex driver and I have N houses to visit, but only exactly 1 house every day (i.e., this is NOT a route planning problem). Since I'm an unimaginative Fedex driver, it's simply easier to go to the houses in the same order every cycle of days, whether or not I have something to deliver. I'd like to come up with a *simple* house visit scheduling algorithm with the following properties: * For each N, the output is a *fixed* *cyclic* schedule; i.e., after some # of days the entire schedule repeats; we'd like the cycle length to be relatively short and will accept some amount of deviation from Zipf's Law in return * The % of visits to house #i in each cycle is very roughly proportional to 1/i * Ideally, the deliveries to house #i should be spread out as much as possible -- i.e., not all bunched together on successive days * The schedule for N+1 houses is simply computed from the schedule for N houses (alternatively, the schedule for N is computed from an a priori fixed number of schedules from smaller N's) Here's an example of what NOT to do for Zipf's Law: Suppose we have 4 houses, with visits scheduled like so: 1 1 2 1 2 3 1 2 3 4 Thus, we have 10 total visits each cycle, but the ratios are 40%, 30%, 20%, 10% -- i.e., this is a linear "tail", rather than a Zipf's Law tail. Is there such an algorithm which approximates a Zipf's Law distribution? What about other power laws/long-tail distributions -- e.g., i^(-2) ? Other common distributions?