Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 56, Issue 5, Pages 2054-2075Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2017.1355120
Keywords
project scheduling; resource levelling; generalised precedence relations; make-to-order production; metaheuristics
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Funding
- National Science Foundation of China [71602106, 71602109]
- Humanities and Social Sciences Foundation of the Ministry of Education of China [15YJCZH077]
- China Postdoctoral Science Foundation [2015M571542]
- Support Plan for Young Teachers of Shanghai [ZZSD16025, ZZSD15095]
- Shanghai Pujiang Programme [16PJC038]
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Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.
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