A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A well-scalable metaheuristic for the fleet size and mix vehicle routing problem with time windows
Expert Systems with Applications: An International Journal
INFORMS Journal on Computing
Expert Systems with Applications: An International Journal
An efficient hybrid algorithm for resource-constrained project scheduling
Information Sciences: an International Journal
Hybrid local search techniques for the resource-constrained project scheduling problem
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Event-based MILP models for resource-constrained project scheduling problems
Computers and Operations Research
A Neurogenetic approach for the resource-constrained project scheduling problem
Computers and Operations Research
Chaos-based improved immune algorithm (CBIIA) for resource-constrained project scheduling problems
Expert Systems with Applications: An International Journal
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
There are various scheduling problems with resource limitations and constraints in the literature that can be modeled as variations of the Resource Constrained Project Scheduling Problem (RCPSP). This paper proposes a new solution representation and an evolutionary algorithm for solving the RCPSP. The representation scheme is based on an ordered list of events, that are sets of activities that start (or finish) at the same time. The proposed solution methodology, namely SAILS, operates on the event list and relies on a scatter search framework. The latter incorporates an Adaptive Iterated Local Search (AILS), as an improvement method, and integrates an event-list based solution combination method. AILS utilizes new enriched neighborhoods, guides the search via a long term memory and applies an efficient perturbation strategy. Computational results on benchmark instances of the literature indicate that both AILS and SAILS produce consistently high quality solutions, while the best results are derived for most problem data sets.