Distributed genetic algorithm for deterministic and stochastic labor scheduling problems

Mathematics

• Distributed Algorithms 90%
• Genetic Algorithm 73%
• Scheduling Problem 69%
• Efficient Frontier 12%
• Genetic Operators 12%
• Costs 12%
• Set Covering Problem 11%
• Tabu Search 11%
• Message Passing 11%
• Set Covering 11%
• Formulation 10%
• Alternatives 10%
• Metaheuristics 10%
• Feasible region 10%
• CPU Time 10%
• Simulated Annealing 10%
• Fitness 9%
• Percentage 9%
• Schedule 8%
• Join 8%
• Test Problems 8%
• Penalty 7%
• Locus 7%
• Economics 7%
• Refinement 7%
• Heuristics 7%
• Minimise 6%
• Optimization Problem 6%
• Performance 5%
• Standards 4%

Business & Economics

• Genetic Algorithm 100%
• Scheduling Problem 61%
• Labor 44%
• Set Covering Problem 13%
• Work Schedules 11%
• Workstation 10%
• Economic Significance 10%
• Tabu Search 10%
• Simulated Annealing 10%
• Metaheuristics 9%
• Efficient Frontier 9%
• Fitness 8%
• Opportunity Cost 8%
• Service Organization 8%
• Join 8%
• Alternatives 8%
• Optimization Problem 7%
• Operator 7%
• Penalty 7%
• Heuristics 6%
• Expenses 6%
• Employees 4%
• Performance 3%
• Costs 3%

Engineering & Materials Science

• Parallel algorithms 63%
• Personnel 51%
• Genetic algorithms 47%
• Scheduling 46%
• Tabu search 9%
• Message passing 9%
• Simulated annealing 8%
• Mathematical operators 7%
• Program processors 7%
• Costs 6%
• Economics 5%