Annals of Operations Research - Special issue on Tabu search
Large-scale controlled rounding using tabu search with strategic oscillation
Annals of Operations Research - Special issue on Tabu search
Tabu Search
A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
Scheduling and constraint propagation
Discrete Applied Mathematics
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
A study of project scheduling optimization using Tabu Search algorithm
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
A Neurogenetic approach for the resource-constrained project scheduling problem
Computers and Operations Research
Tri-directional Scheduling Scheme: Theory and Computation
Journal of Mathematical Modelling and Algorithms
Expert Systems with Applications: An International Journal
On the performance of bee algorithms for resource-constrained project scheduling problem
Applied Soft Computing
Expert Systems with Applications: An International Journal
A dynamic clonal selection algorithm for project optimization scheduling
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A genetic algorithm for solving resource-constrained project scheduling problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Computers and Operations Research
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An appropriate tabu search implementation is designed to solvethe resource constrained project scheduling problem. Thisapproach uses well defined move strategies and a structuredneighbourhood, defines appropriate tabu status and tenure andtakes account of objective function approximation to speed upthe search process. A sound understanding of the problem hashelped in many ways in designing and enhancing the tabu searchmethodology. The method uses diversification, intensification andhandles infeasibility via strategic oscillation.The above methodology is tested on existing problems from theliterature and also on parametrically generated problems withencouraging results. For comparison of results, optimalsolutions are used in the former and lower bounds obtained byLagrangian heuristics are used in the latter.