Sequential Resource Allocation with Positional Costs

TL;DR

This paper addresses the challenge of minimizing total costs in sequential task allocation with positional effects, proposing a practical greedy algorithm that is efficient for two-task-type instances and inspired by Bayesian bandit strategies.

Contribution

It introduces a simple, practical $O(k^2 n)$ algorithm for minimizing costs in sequential resource allocation with positional costs, specifically effective for two task types.

Findings

Algorithm runs in $O(k^2 n)$ time.

Effective for problems with two task types.

Inspired by Bayesian bandit optimal stopping strategies.

Abstract

We consider the problem of minimizing the total cost to run a sequence of $n$ tasks in the given order by $k$ agents under the positional cost model. The cost to run a task not only depends on the intrinsic cost of the task itself, but also monotonically related to the position this task is in the working list of the agent assigned. Such a positional effect can naturally arise from the classic sum-of-completion-time minimization problems, and is also well motivated by the varying efficiency when an agent works in reality (such as due to the learning effects or deteriorating effects). Also, it can be seen as a deterministic variant of the classic Baysian sequential decision making problems. This paper presents a simple and practical algorithm that runs in $O(k^2 n)$ time and minimizes the total cost of any problem instance consisting of two task types. The algorithm works by making greedy decision for each task sequentially based on some stopping thresholds in a "greedy-like" allocation simulation -- a working style coinciding with Gittins' optimal-stopping based algorithm for the classic Baysian multi-armed bandit problem.