Resource-constrained project scheduling: Notation, classification, models, and methods

An ant colony optimization approach (ACO) for the resource-constrained project scheduling problem (RCPSP) is presented. Combinations of two pheromone evaluation methods are used by the ants to find new solutions. We tested our ACO algorithm on a set of large benchmark problems from the PSPLIB. Compared to several other heuristics for the RCPSP including genetic algorithms, simulated annealing, tabu search, and different sampling methods our algorithm performed best on the average. For some test instances the algorithm was able to find new best solutions.

Ant colony optimization for resource-constrained project scheduling

In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998

A competitive genetic algorithm for resource-constrained project scheduling

A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7765&context=lkcsb_research

Arno Schoo

Papers

A branch and bound algorithm for the resource-constrained project scheduling problem