A double-decomposition based parallel exact algorithm for the feedback length minimization problem

TL;DR

This paper introduces a novel parallel branch-and-prune algorithm based on double decomposition to optimally sequence activities in project scheduling, significantly improving solution efficiency for the feedback length minimization problem.

Contribution

It presents a new double-decomposition based parallel algorithm with result-compression and hash strategies, outperforming existing exact methods in solving FLMP.

Findings

Optimal sequences for up to 27 activities within 1 hour

Outperforms state-of-the-art exact algorithms

Effective use of parallel computing resources

Abstract

Product development projects usually contain many interrelated activities with complex information dependences, which induce activity rework, project delay and cost overrun. To reduce negative impacts, scheduling interrelated activities in an appropriate sequence is an important issue for project managers. This study develops a double-decomposition based parallel branch-and-prune algorithm, to determine the optimal activity sequence that minimizes the total feedback length (FLMP). This algorithm decomposes FLMP from two perspectives, which enables the use of all available computing resources to solve subproblems concurrently. In addition, we propose a result-compression strategy and a hash-address strategy to enhance this algorithm. Experimental results indicate that our algorithm can find the optimal sequence for FLMP up to 27 activities within 1 hour, and outperforms state of the art exact algorithms.