Affiliation:
1. School of Economics and Management, Chang’an University, Xi’an, China
2. LERIA, Université d’Angers, Angers, France
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.
Funder
The Natural Science Basic Research Program of Shaanxi
The Special Foundation for Philosophy and Social Science Research of Shaanxi
Scientific Research Plan Project of Shaanxi Provincial Department of Education
The Fundamental Research Funds for the Central Universities
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