Work

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

Year: 2023

Type: article

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 h, and outperforms state of the art exact algorithms.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.1597

Authors: Zhen Shang, Jin-Kao Hao, Fei Ma

Institutions: Chang'an University, Université d'Angers

Language: English

Topic: Resource-Constrained Project Scheduling

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,395

Funders: Education Department of Shaanxi Province, Fundamental Research Funds for the Central Universities