City Research Online

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most intractable combinatorial optimisation problems that combines a set of constraints and objectives met in a vast variety of applications and industries. Its solution raises major theoretical challenges due to its complexity, yet presenting numerous practical dimensions. Adaptive memory programming (AMP) is one of the most successful frameworks for solving hard combinatorial optimisation problems (e.g. vehicle routing and scheduling). Its success stems from the use of learning mechanisms that capture favourable solution elements found in high-quality solutions. This paper challenges the efficiency of AMP for solving the RCPSP, to our knowledge, for the first time in the literature. Computational experiments on well-known benchmark RCPSP instances show that the proposed AMP consistently produces high-quality solutions in reasonable computational times.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 21 March 2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1145814.
Publisher Keywords:	adaptive memory programming, project scheduling, resource constraints
Subjects:	H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Departments:	Bayes Business School > Management
SWORD Depositor:	Symplectic Administrator

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