A Time-Triggered Dimension Reduction Algorithm for the Task Assignment Problem

TL;DR

This paper introduces a novel time-triggered dimension reduction algorithm for the task assignment problem, improving computational efficiency by convexifying the problem and accelerating solution times.

Contribution

It proposes a new TTDRA method with convexification approaches and a time-triggered scheme to enhance speed and optimality in solving the task assignment problem.

Findings

Algorithm accelerates solution times in benchmark tests.

Convexification improves problem tractability.

Numerical simulations verify optimality and efficiency.

Abstract

The task assignment problem is fundamental in combinatorial optimisation, aiming at allocating one or more tasks to a number of agents while minimizing the total cost or maximizing the overall assignment benefit. This problem is known to be computationally hard since it is usually formulated as a mixed-integer programming problem. In this paper, we consider a novel time-triggered dimension reduction algorithm (TTDRA). We propose convexification approaches to convexify both the constraints and the cost function for the general non-convex assignment problem. The computational speed is accelerated via our time-triggered dimension reduction scheme, where the triggering condition is designed based on the optimality tolerance and the convexity of the cost function. Optimality and computational efficiency are verified via numerical simulations on benchmark examples.