Computer and Job-shop Scheduling TheoryIntroduction to deterministic scheduling theory; Algorithms for minimal-length schedulesComplexity of sequencing problems; Enumerative and iterative computationsl approaches. |
From inside the book
Results 1-3 of 38
Page 11
... processors , and seeks to minimize the number of processors required to meet the common deadline d . This problem is referred to as the bin - packing problem in Chapter 5. Interest- ingly , we find that this problem is equivalent to the ...
... processors , and seeks to minimize the number of processors required to meet the common deadline d . This problem is referred to as the bin - packing problem in Chapter 5. Interest- ingly , we find that this problem is equivalent to the ...
Page 29
... processors , and m , { 7 } , and < arbitrary . A number of unexpected anomalies are revealed which show that individual decreases in execution times , increases in the number of processors , and removal of precedence constraints can in ...
... processors , and m , { 7 } , and < arbitrary . A number of unexpected anomalies are revealed which show that individual decreases in execution times , increases in the number of processors , and removal of precedence constraints can in ...
Page 54
... number of processors available , at least 5 time units will be required to execute all the tasks in the system . On a sufficiently large number of processors , an optimal strategy would be to start with the tasks farthest away from the ...
... number of processors available , at least 5 time units will be required to execute all the tasks in the system . On a sufficiently large number of processors , an optimal strategy would be to start with the tasks farthest away from the ...
Common terms and phrases
3-satisfiability a₁ approximate algorithms arbitrary AS/DB assigned assume b₁ b₂ BB₁ BB₂ BFST BFST₁ bound branch-and-bound algorithm branching node branching step Chapter complete solution computational requirements construct D₁ defined denote dynamic programming elements elimination rules example feasible FIFO Figure finishing flow pattern flow-shop problem given graph heuristic input integer job-shop problem job-system knapsack problem labeling Lemma length list schedule lower-bound function mean weighted flow minimal mwft neighborhood nonpreemptive schedule NP-complete number of processors optimal permutation optimal schedule optimal solution p-maximal set P₁ parameters partial solutions polynomial polynomial-time precedence constraint preemptive schedule Proof random variable resource S₁ scheduling problem scheduling rule scheduling theory secondary storage sequence set with respect subset T₁ T₂ task system tasks executed Theorem traveling salesman problem tree U/DBAS U₁ upper-bound solution variables X₁ Y₁ π₂