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An effective hybrid evolutionary searchmethod is presentedwhich integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversi?es the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid schedule generation scheme for solving the resource-constrained project scheduling problem. The scheme, which is called the Polarized Adaptive Scheduling Scheme (PASS), can operate in a spectrum between two poles, namely the parallel and serial schedule generation schemes. A polarizer parameter in the range between zero and one indicates how similarly the PASS behaves like each of its two poles. The presented hybrid is incorporated into a novel genetic algorithm that never degenerates, resulting in an effective self-adaptive procedure. The key point of this genetic algorithm is the embedding of the polarizer parameter as a gene in the genomes used. Through this embedding, the procedure learns via monitoring its own performance and incorporates this knowledge in conducting the search process. The computational experiments indicate that the procedure can produce optimal solutions for a large percentage of benchmark instances. [ABSTRACT FROM AUTHOR]

This paper presents an effective parallel complete anytime procedure for the resource-constrained project scheduling problem. The procedure finds a sequence of solutions in which every solution improves the previous one. To accelerate the convergence of the sequence to the optimal solution, the procedure simultaneously works in the forward and backward directions. While in the forward direction, it solves the problem at hand; in the backward direction, it solves its mirror problem. Performing simultaneously in the forward and backward directions exploits the power of double processors that are now available on most personal computers. The results of computational experiments on 2,040 benchmark instances are promising and indicate that, for many instances, a fast convergence to optimal solutions is obtained. [ABSTRACT FROM AUTHOR]

This paper describes an efficient procedure for resource-constrained project scheduling problems. It starts with a simulated annealing technique to find a base schedule, and then improves the result by a time-windowing process. Every time-window, which is part of the base schedule, is a basis for a small subproject with two sets of specific constraints. Associated subprojects with time-windows are scheduled to optimality and based on their results the base schedule is updated. The overlapping feature of time-windows makes the displacement of an activity possible within the range of the entire project. The process of creating time-windows, scheduling their associated subprojects to optimality and improving the base schedule is controlled by a feed-back based mechanism that realises a trade-off between computational effort and the improvement made. The computational results indicate that the procedure is promising and yields better solutions than several heuristic algorithms presented in the literature. [ABSTRACT FROM AUTHOR]

A correction to the article "A parallel complete anytime procedure for project scheduling under multiple resource constraints" which was published in the March 2, 2010 issue is presented.