1. Approximation Schemes Open Shop Problem

2. O||C max and Om||C max {J 1,..., J n } is set of jobs. {M 1,..., M m } is set of machines. J i : {O i1,..., O im } is set of operations of job J i, O ik must be processed on machine M k, O ik : p ik ≥ 0 (i=1,…, n; k=1,…, m),

3. Instance and Schedule: C max = 16 M1M1 M2M2 M3M3 J1J1 J4J4 J3J3 J2J2 J5J5 J1J1 J2J2 J3J3 J4J4 J2J2 J4J4 J5J5 J5J5 p 11 = 1 p 11 = 1 p 12 = 2 p 12 = 2 p 13 = 2 p 13 = 2 p 21 = 2 p 21 = 2 p 22 = 2 p 22 = 2 p 23 = 2 p 23 = 2 p 31 = 6 p 31 = 6 p 32 = 2 p 32 = 2 p 33 = 1 p 33 = 1 p 41 = 1 p 41 = 1 p 42 = 2 p 42 = 2 p 43 = 3 p 43 = 3 p 51 = 2 p 51 = 2 p 52 = 5 p 52 = 5 p 53 = 5 p 53 = 5 J1J1 0 20 51015 J3J3 J1J1 J2J2 J3J3 J4J4 J5J5

4. Lower bounds Let P j be the length of job J j, P max = max j P j is the maximum job length. Let L k be the load of machine M k. L max = max i L i is the maximum machine load. OPT ≥ max{P max, L max }

5. Greedy Algorithm for O||C max Whenever a machine becomes idle, if there is an unscheduled operation available to be scheduled on that machine, schedule it. An operation is available if it belongs to a job which is currently not undergoing any processing.

6. 2-approximation algorithm Theorem 8.1 (Racsmany [1982]) Greedy algorithm is a 2-approximation algorithm for O||C max.

7. Proof Consider the machine M i that finishes last, and let J j be the job whose operation O k completes last on that machine. We claim that, at every point in time during the schedule, either job J j is undergoing processing on some machine, or machine M i is busy or both. If such were not the case, operation O k would have been scheduled earlier, since if M i and J j were both available earlier, clearly O k would have been processed at that time. Thus the schedule’s makespan is less than P max + L max ≤ 2OPT.

8. Tight instance A simple example shows that the greedy algorithm does not do better than 2 – 1/m in the worst case. Given m machines and m + 1 jobs, each having a unit-length operation on each machine. It is not hard to see that the optimal schedule has length m + 1, since the jobs can simply be rotated through the machines. Suppose that the greedy algorithm assign m jobs in the interval (0, m]. Then the operation J m+1 must undergo processing sequentially, and the overall schedule produced has length 2m,

9. Tight instance J1J1 J2J2 J3J3 J4J4 M1M1 M2M2 M3M3 J1J1 J1J1 J2J2 J2J2 J3J3 J3J3 J4J4 J4J4 J1J1 J2J2 J3J3 J4J4 M1M1 M2M2 M3M3 J1J1 J1J1 J2J2 J2J2 J3J3 J3J3 J4J4 J4J4

10. Inapproximability of O||C max Theorem 8.2 (Williamson et al.) The problem of deciding if there is an open shop schedule of length at most 4 is NP- complete. Corollary 8.3 (Williamson et al.) For any  < 5/4, there does not exist a polynomial-time  -approximation algorithm for the open shop problem, unless P = NP.

11. Monotonne-Not-All-Equal-3Sat Instance: Set U of variables, collection Z of clauses over U such that each clause has size 3 and contains only unnegated variables. Question: Is the truth assignment for U such that each clause in C has at least one true variable and at least one false variable?

12. Instance I of SAT U = {x 1,…, x u }, Z = {z 1,…, z v } Suppose each variable x i appears t i times in Z. For notational convenience, we view the k-th occurrence of x i as the variable x ik. Let  (x ik ) denote the next occurrence of x i, cyclically ordered; that is  (x ik ) = x il, where l = k mod t i + 1. transform an instance I into an inWe stance I O of the O||C max problem.

13. Instance I O 2u machines and 2u + v jobs For each variable x ik we construct two machines M A (x ik ) and M B (x ik ) and 3 types of jobs: An assignment job J ik with operations A(x ik ) and B(x ik ) of length 2, which are to be processed by M A (x ik ) and M B (x ik ), respectively. A consistency job J′ ik has operations A′(x ik ) and B′(x ik ) of length 2 and 1, respectively, which must be processed by M B (x ik ) and M A (  (x ik )). For each clause c = (x ˅ y ˅ z), we construct a clause job J c with three unit-length operations T(x), T(y), and T(z), to be processed on M A (x), M A (y), and M A (z), respectively.

14. (x 1 ˅ x 2 ˅ x 4 ) ˄ (x 2 ˅ x 3 ˅ x 4 ) M A (x 11 ) A(x 11 ) M B (x 11 ) M A (x 21 ) M B (x 21 ) M A (x 22 ) M B (x 22 ) M A (x 31 ) M B (x 31 ) M A (x 41 ) A(x 41 ) M B (x 41 ) A′(x 41 ) M A (x 42 ) A(x 42 ) M B (x 42 ) A′(x 42 ) B(x 11 ) B′(x 22 ) A′(x 21 ) A′(x 22 ) B′(x 21 ) B(x 41 ) B(x 42 ) B′(x 42 ) B′(x 41 ) assignment job x 1 assignment job x 2 assignment job x 3 assignment job x 4 clause job for x 1 ˅ x 2 ˅ x 4 clause job for x 2 ˅ x 3 ˅ x 4 consistency jobs

15. Consistency jobs M A (x 21 ) M B (x 21 ) M A (x 22 ) M B (x 22 ) B′(x 22 ) A′(x 21 ) A′(x 22 ) B′(x 21 ) assignment job x 2 consistency jobs A consistency job ensures that the value of variable x ik is equal to the value of its next occurrence.

16. (x 1 ˅ x 2 ˅ x 4 ) ˄ (x 2 ˅ x 3 ˅ x 4 ) M A (x 11 ) A(x 11 ) M B (x 11 ) M A (x 21 ) M B (x 21 ) M A (x 22 ) M B (x 22 ) M A (x 31 ) M B (x 31 ) M A (x 41 ) A(x 41 ) M B (x 41 ) A′(x 41 ) M A (x 42 ) A(x 42 ) M B (x 42 ) A′(x 42 ) B(x 11 ) B′(x 22 ) A′(x 21 ) A′(x 22 ) B′(x 21 ) B(x 41 ) B(x 42 ) B′(x 42 ) B′(x 41 ) assignment job x 1 assignment job x 2 assignment job x 3 assignment job x 4 clause job for x 1 ˅ x 2 ˅ x 4 clause job for x 2 ˅ x 3 ˅ x 4 consistency jobs

17. Properties of instance I O Suppose that there is a schedule of length 4. In any such schedule, either every machine M A (x ik ) (k = 1,…, t i ) process its assignment operation from time 0 to 2, or every machine M A (x ik ) process its assignment operation from time 2 to 4. If this is not a case, then the exists i and k such that M A (x ik ) processes its assignment operation from time 0 to 2, and M A (  (x ik )) processes its assignment operation from time 2 to 4. But M B (x ik ) processes its assignment operation from time 2 to 4. The consistency job for x ik must be processed on both M B (x ik ) and M A (  (x ik )) from time 0 to 2, which is a contradiction. M A (x ik ) M B (x 21 ) M A (  (x ik )) M B (x 22 ) A(  (x ik )) A(x ik ) A′(x ik ) B′(x ik ) assignment job x i consistency jobs

18. Satisfying assignment For each variable x i, set x i to be true if the assignment operation for M A (x ik ) runs from 0 to 2, and false otherwise. We know that a clause operation has been scheduled between time 2 and 4 in case the variable corresponding to that operation has been set true and sometime between time 0 and 2 in case the variable has been set false. Because each clause job has three unit length operations wich have been scheduled in nonoverlapping time periods, not all of its operations can correspond to true variables and not all of its operations can correspond to false variables. Hence at least one variable of each clause must be true and at least one variable must be false.

19. (x 1 ˅ x 2 ˅ x 4 ) ˄ (x 2 ˅ x 3 ˅ x 4 ) M A (x 11 ) A(x 11 ) M B (x 11 ) M A (x 21 ) M B (x 21 ) M A (x 22 ) M B (x 22 ) M A (x 31 ) M B (x 31 ) M A (x 41 ) A(x 41 ) M B (x 41 ) A′(x 41 ) M A (x 42 ) A(x 42 ) M B (x 42 ) A′(x 42 ) B(x 11 ) B′(x 22 ) A′(x 21 ) A′(x 22 ) B′(x 21 ) B(x 41 ) B(x 42 ) B′(x 42 ) B′(x 41 ) операции назначения x 1 операции назначения x 2 операции назначения x 3 операции назначения x 4 операции x 1 ˅ x 2 ˅ x 4 операции x 2 ˅ x 3 ˅ x 4 операции согласования x 1 := 1 x 2 := 0 x 3 := 0 x 4 := 1

20. Om||C max Let 0 < ε < 1 be some small number such that 1/ ε is an integer. Let m ≥ 3 be an integer LB = max{P max, L max } LB ≤ OPT(C max ) ≤ 2LB

21. Partition of jobs (0 < ε′ < ε/(m 2 +1) < 1) We define three set of jobs. For a rational number  with ε m/ε ≤  ≤ ε set Big = {J j  J| P j ≥  LB}, Small = {J j  J|  ε′LB < P j <  LB}, Tiny = {J j  J| P j ≤  ε′LB}. The number of big jobs is bounded by m/  ≤ mε′ -m/ε′. The total length of the small jobs is at most ε′LB.

22. How to choose such  Small = {J j  J|  ε′LB < P j <  LB} Define a sequence of real numbers  l = (  ) l, l ≥ 0. Consider the sets S l of small operations with respect to  l. For i ≠ j the sets S i and are S j disjoint ε′ 7 ε′ 5 ε′ 3 ε′ ε′ 6 ε′ 4 ε′ 2  k = ε k S1S1 S2S2

23. How to choose such  Since the total length of all operations is at most mLB, the exists a number k ≤ m/ε′ for which S k is as desired. We set  =  k. Note that the value of  depends on the input, but it is bounded by constants independent on the input. ε′ 7 ε′ 5 ε′ 3 ε′ ε′ 6 ε′ 4 ε′ 2  k = ε k S1S1 S2S2

24. Algorithm OpenShop 1.Find an optimal schedule  1 for big jobs. –We fix an order of the big jobs for each machine and fix an order of the operations for each big job. On any machine M i, the schedule big jobs induces a sequence of gaps. 2.Schedule tiny operations into gaps of  1. Denote the obtained schedule as  2. 3.Add the small jobs at the end of  2 in a greedy way.

25. Step 1 The number of big jobs is bounded by The number of big jobs is bounded by a constant that only depends on ε and m. We enumerate all schedules of big jobs and take the best one. We note that OPT(Big) ≤ OPT. There are at most m 2 /  gaps in the schedule  1. M1M1 M2M2 M3M3 J1J1 J2J2 J3J3 J2J2 J1J1 J2J2 J5J5 J5J5 0 16 51015 J3J3

26. Steps 2 and 3 Starting at time t = 0, the algorithm tries to schedule one of the available unscheduled tiny operations at every time t where one of the machines M i becomes idle. Let at time t an available operation O ik be considered and the remaining part of the gap is less than the length of operation O ik. The reason is that some big operation O starts at time τ and p ik > τ − t. In this case shift operation O and every operation, which starts after the completion of O, to the right by p ik − τ + t time units. Let C max (  2 ) be the length of the obtained schedule. Starting at timeC max (  2 ) schedule the small jobs in a greedy way.

27. Step 2 M1M1 M2M2 M3M3 J1J1 J2J2 J3J3 J2J2 J1J1 J2J2 J5J5 J5J5 0 16 510 J3J3 M1M1 M2M2 M3M3 J1J1 J2J2 J3J3 J2J2 J1J1 J2J2 J5J5 J5J5 0 16 510 J3J3

28. Analysis of the algorithm Let C max (  ) be the makespan of the schedule by Algorithm OpenShop. Since the total length of small jobs is at most ε′LB, it follows that C max (  ) ≤ C max (  2 ) + ε′LB. Let us estimate C max (  2 ). Let O jk be an operation, which completes last in  2, i.e. С jk = C max (  2 ). Let be the sum of the lengths of all shifts produced by the algorithm. Let  be the total idle times on machine M k.

29. O jk is an operation of a big job C max (  2 ) ≤ C max (  1 ) +  ≤ (m 2 /  )  ε′LB = m 2 ε′LB C max (  2 ) ≤ C max (  1 ) + m 2 ε′LB

30. O jk is an operation of a tiny job C max (  2 ) ≤ L k +    ≤ p k ≤  ε′LB C max (  2 ) ≤ C max (  1 ) +  ε′LB ≤ ≤ C max (  1 ) + m 2 ε′LB C max (  ) ≤ C max (  2 ) + ε′LB ≤ ≤ C max (  1 ) + (m 2 +1)ε′LB ≤ (1 +  ) OPT.

31. PTAS Theorem 8.4 (Sevastianov, Woeginger 1996) For every fixed ε > 0 and any fixed m ≥ 2, there exists a polynomial-time (1+ ε)-approximation algorithm for the Om||C max problem.

32. Exercise Let  be the schedule obtained by the greedy algorithm. Let H be the set of intervals in  such that no machine is idle during these intervals. Let W be the total length of the intervals from H. Suppose that W ≥ 3P max. Obtain a good estimate of the ratio of C max (  ) to OPT.