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Total Tardiness (1)

Total Tardiness (1). 1 ||  T j : NP hard in the ordinary sense. There is a pseudo polynomial time algorithm to solve the problem. Properties of the solution: If p j  p k and d j  d k holds then there exists an optimal sequence in which job j is scheduled before job k.

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Total Tardiness (1)

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  1. Total Tardiness (1) 1 ||  Tj : NP hard in the ordinary sense. • There is a pseudo polynomial time algorithm to solve the problem. Properties of the solution: • If pj  pk and dj  dk holds then there exists an optimal sequence in which job j is scheduled before job k. • This is an Elimination criterion or Dominance result.A large number of sequences can be disregarded.  New precedence constraints are introduced.  The problem becomes easier.

  2. Total Tardiness (2) 2 problem instances with processing times p1, ..., pn First instance: d1, ..., dn C’k: latest possible completion time of job k in an optimal sequence (S’) Second instance: d1, ..., dk-1 , max{dk ,C’k} dk+1, ..., dn S’’: an optimal sequence Cj’’: completion time of job j in sequence S’’ • Any sequence that is optimal for the second instance is optimal for the first instance as well.

  3. Total Tardiness (3) Assumption: d1  ... dn and pk = max (p1, ... , pn) • kth smallest due date has the largest processing time. • There is an integer , 0   n – k such that there is an optimal sequence S in which job k is preceded by all other jobs j with j  k+ and followed by all jobs j with j > k+. • An optimal sequence consists of • jobs 1, ..., k-1, k+1, ..., k+ in some order • job k • jobs k+ +1, ... , n in some order The completion time of job k is given by .

  4. Minimizing Total Tardiness (1) • J(j, l, k): all jobs in the set {j, ..., l} with a processing time  pk but job k is not in J(j, l, k). • V(J(j, l, k), t) is the total tardiness of J(j, l, k) in an optimal sequence that starts at time t. Algorithm: Minimizing Total Tardiness Initial conditions: V(, t) = 0 V({j}, t) = max (0, t+ pj –dj) Recursive relation: where k‘ is such that Optimal value functionV({1, ..., n},0)

  5. Minimizing Total Tardiness (2) • At most O(n³) subsets J(j, l, k) and  pj points in t • O(n³∙ pj ) recursive equations • Each recursion takes O(n) time • Running time O(n4∙ pj ) polynomial in n pseudo polynomial Algorithm PTAS Minimizing Total Tardiness

  6. Minimizing Total TardinessExample (1) • k=3 (largest processing time)  0    2 = 5 – 3 V(J(1, 3, 3), 0) + 81 + V(J(4, 5, 3), 347) • V({1, 2, ..., 5}, 0)=min V(J(1, 4, 3), 0) +164 + V(J(5, 5, 3), 430) V(J(1, 5, 3), 0) + 294 + V(, 560) • V(J(1, 3, 3), 0) = 0 for sequences 1, 2 and 2, 1 • V(J(4, 5, 3), 347) = 347 +83 – 336 +347 + 83 +130 – 337 = 317 for sequence 4, 5

  7. Minimizing Total Tardiness Example (2) • V(J(1, 4, 3), 0) = 0 for sequences 1, 2, 4 and 2, 1, 4 • V(J(5, 5, 3), 430) = 430 + 130 – 337 =223 • V(J(1, 5, 3), 0) = 76 for sequences 1, 2, 4, 5 and 2, 1, 4, 5 0 + 81 + 317 • V({1, ..., 5}, 0) = min 0 + 164 + 223 = 370 76 + 294 + 0 1, 2, 4, 5, 3 and 2, 1, 4, 5, 3 are optimal sequences.

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