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We have $n$ tasks, each has its own price: $a_i$. We also have $n$ workers, each can do task $i$ with a probability $b_i^k$. Each worker should do only 1 task. I am trying to find out what will be the best algorithm to maximize the expectation of money that workers can earn (this will be a sum: $\sum\limits_i a_ib_i^{k_i},$where $k_i$ are some permutation of $1,..n$).

My idea was to sort probabilities of success for each of the tasks and compare products of its prices and difference of the top 2 probabilities. When I will get the maximum of such function, I will assign this task to a worker with the best probability to successfully finish it and after that do the whole procedure again. Unfortunately, I was stuck trying to prove it, also I am not even sure that this is the best possible algorithm. I will really appreciate any help or a hint.

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  • $\begingroup$ You can always rephrase it as an IP problem if nothing else, however, I doubt that this is the solution you need. If the defining matrix is totally unimodular, it is not that bad, otherwise it may be worth finding a faster algorithm. $\endgroup$ – rss Apr 30 at 15:06
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    $\begingroup$ Hint: matching. $\endgroup$ – Marcus Ritt Apr 30 at 19:06
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What you are describing is a "greedy heuristic", and no, it is not guaranteed to find an optimal assignment. To expand on the comment by Marcus Ritt, see this description of the assignment problem.

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  • $\begingroup$ Thank you very much! This is exactly what I was looking for. $\endgroup$ – AO1992 May 3 at 7:24

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