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Given: $n$ tasks with accomplishment times $t_1, t_2, \ldots , t_n$. There is no such task that its accomplishment time is greater than the overall accomplishment time of other tasks.

Question: How to distribute these tasks between two workers in a such way that $|T_1 - T_2|$ is minimum ($T_i$ - the overall accompl. time for the $i$-th worker accordingly).

We will use the local search algorithm, starting with a random distribution and redistributing one task on every step only if it leads to reducing $|T_1 - T_2|$ as long as it's possible.

Does this algorithm find the optimal distribution or is there any counterexample?

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We can consider the more general problem of makespan, in which we wish to partition the tasks into $m$ sets as evenly as possible (your case is $m=2$). We measure the quality of our solution using the maximum total sum of a set, which we wish to minimize. Finn and Horowitz showed that the locality gap for this problem is $2-2/(m+1)$. This means that a locally optimal solution has objective value larger by a factor of at most $2-2/(m+1)$, and moreover there are examples in which the ratio approaches $2-2/(m+1)$. In particular, local search doesn't always give the optimal solution.

For other local search algorithms for makespan, consult the survey of Schuurman and Vredeveld.

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