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A genetic algorithm for resource-constrained scheduling · Study

ThesisA genetic algorithm for resource-constrained scheduling

Mark J. Jakiela1996

Thesis1996

A genetic algorithm for resource-constrained scheduling

Mark J. Jakiela · Woodie C. Flowers

162 citations·0/0 files stored·DSpace@MIT (Massachusetts Institute of Technology)

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Resource-Constrained Project Scheduling Scheduling and Optimization Algorithms Scheduling and Timetabling Solutions

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.

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References

1992A Branch-and-Bound Procedure for the Multiple Resource-Constrained Project Scheduling Problem 1975A Comparison of Heuristic and Optimum Solutions in Resource-Constrained Project Scheduling 1984A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem 1990Some efficient multi-heuristic procedures for resource-constrained project scheduling 1978Multiple Resource–Constrained Scheduling Using Branch and Bound 1989Advances in Project Scheduling 1997A genetic algorithm for multi-mode resource constrained project scheduling problem 1995Project Scheduling under Resource Constraints

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2001Genetic Algorithms for Project Management 2015Automation in construction scheduling: a review of the literature 2008Time-line based model for software project scheduling with genetic algorithms 2002Application of genetic algorithms to construction scheduling with or without resource constraints 2011An autonomous multi-agent approach to supply chain event management

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Year1996

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Published1996-01-01

VenueDSpace@MIT (Massachusetts Institute of Technology)

Languageen

Citations162

Files0 public / 0

ShelfResource-Constrained Project Scheduling

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2008Finding the shortest path with honey-bee mating optimization algorithm in project management problems with constrained/unconstrained resources

2016A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem (UGA)

2014A Comparative Study of Three Artificial Intelligence Techniques: Genetic Algorithm, Neural Network, and Fuzzy Logic, on Scheduling Problem