Computers in Industry - Special issue: Application of genetics algorithms in industry
Computers and Operations Research
Stochastic rollout and justification to solve the resource-constrained project scheduling problem
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Hybrid Heuristics for Multi-mode Resource-Constrained Project Scheduling
Learning and Intelligent Optimization
Differential evolution for solving multi-mode resource-constrained project scheduling problems
Computers and Operations Research
An Artificial Immune System for the Multi-Mode Resource-Constrained Project Scheduling Problem
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
IEEE Transactions on Evolutionary Computation
Computers in Industry - Special issue: Application of genetics algorithms in industry
Using genetic algorithms for planning of ASIC chip-design project flows
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Independent tasks scheduling based on genetic algorithm in cloud computing
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Optimizing discounted cash flows in project scheduling: an ant colony optimization approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A genetic algorithm for project scheduling with multi-modes and renewable resources
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Solving software project scheduling problems with ant colony optimization
Computers and Operations Research
Advanced Engineering Informatics
Using meta-heuristics for project scheduling under mode identity constraints
Applied Soft Computing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A genetic algorithm (GA) approach is proposed for the general resource-constrained project scheduling model, in which activities may be executed in more than one operating mode, and renewable as well as nonrenewable resource constraints exist. Each activity's operation mode has a different duration and requires different amounts of renewable and nonrenewable resources. The objective is the minimization of the project duration or makespan. The problem under consideration is known to be one of the most difficult scheduling problems, and it is hard to find a feasible solution for such a problem, let alone the optimal one. The GA approach described in this paper incorporates problem-specific scheduling knowledge by an indirect chromosome encoding that consists of selected activity operating modes and an ordered set of scheduling rules. The scheduling rules in the chromosome are used in an iterative scheduling algorithm that constructs the schedule resulting from the chromosome. The proposed GA is denoted a hybrid GA (HGA) approach, since it is integrated with traditional scheduling tools and expertise specifically developed for the general resource-constrained project scheduling problem. The results demonstrate that HGA approach produces near-optimal solutions within a reasonable amount of computation time