Scheduling of project networks by job assignment
Management Science
Resource-constrained project scheduling: a survey of recent developments
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
An evolutionary algorithm for resource-constrained projectscheduling
IEEE Transactions on Evolutionary Computation
On the performance of bee algorithms for resource-constrained project scheduling problem
Applied Soft Computing
An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid evolutionary algorithm for the resource-constrained project scheduling problem
Artificial Life and Robotics
Discrete-event simulation for design of enhanced project schedules
Proceedings of the 2013 Summer Computer Simulation Conference
Computers and Operations Research
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A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.