ILP Formulations for the Lazy Bureaucrat Problem

Transcription

1 the the PSL, Université Paris-Dauphine, Paris Cedex 16, France, CNRS, LAMSADE UMR 7243 Department of Statistics and Operations Research, University of Vienna, Vienna, Austria EURO 2015, July, Glasgow

2 Outline the

3 the The lazy bureaucrat problem (LBP) is a scheduling problem (common arrivals and deadlines) in which a set of jobs has to be scheduled in the most inefficient way! Definition The lazy bureaucrat needs to choose a subset of jobs to execute in a single day, in a such a way that: no other job fits in his/her working hours the total profit of selected jobs (e.g., their duration) is minimized

4 the The lazy bureaucrat problem (LBP) is a scheduling problem (common arrivals and deadlines) in which a set of jobs has to be scheduled in the most inefficient way! Definition The lazy bureaucrat needs to choose a subset of jobs to execute in a single day, in a such a way that: no other job fits in his/her working hours the total profit of selected jobs (e.g., their duration) is minimized

5 Applications of the the Practical point of view how to distribute the available budget so that: (i) the minimal amount of money is allocated to funding requests and (ii) while having a good excuse that no additional funds can be granted without violating the available budget? Theoretical point of view new interesting insights as the optimization is driven in the opposite direction when compared to their standard (non-lazy) counterparts

6 Applications of the the Practical point of view how to distribute the available budget so that: (i) the minimal amount of money is allocated to funding requests and (ii) while having a good excuse that no additional funds can be granted without violating the available budget? Theoretical point of view new interesting insights as the optimization is driven in the opposite direction when compared to their standard (non-lazy) counterparts

7 Mathematical definition the Definition (LBP with common arrivals and deadlines) We are given: a set of jobs I = {1,..., n} each job i I has a duration w i N and a profit p i N all jobs arrive at the same time all jobs have a common deadline C N The goal is to find a least profitable subset of jobs S I such that: S = arg min { p i w i C and w i + w j > C, j S} S I i S i S i S time-spent objective (p i = w i ). min-number-of-jobs objective (p i = 1). general objective function weighted-sum

8 Mathematical definition the Definition (LBP with common arrivals and deadlines) We are given: a set of jobs I = {1,..., n} each job i I has a duration w i N and a profit p i N all jobs arrive at the same time all jobs have a common deadline C N The goal is to find a least profitable subset of jobs S I such that: S = arg min { p i w i C and w i + w j > C, j S} S I i S i S i S time-spent objective (p i = w i ). min-number-of-jobs objective (p i = 1). general objective function weighted-sum

9 Mathematical definition the Definition (LBP with common arrivals and deadlines) We are given: a set of jobs I = {1,..., n} each job i I has a duration w i N and a profit p i N all jobs arrive at the same time all jobs have a common deadline C N The goal is to find a least profitable subset of jobs S I such that: S = arg min { p i w i C and w i + w j > C, j S} S I i S i S i S time-spent objective (p i = w i ). min-number-of-jobs objective (p i = 1). general objective function weighted-sum

10 Complexity issues the The problem has been introduced in [1] where it was shown that a more general problem variant with individual arrival times and deadlines is NP-hard For the problem variant with common arrival times and deadlines, [3] show that the problem is weakly NP-hard the min-number-of-jobs objective by reduction from subset-sum. Thus, the problem studied in this paper (with the more general weighted-sum objective) is also at least weakly NP-hard. In [5] a FPTAS have been proposed the time-spent objective with common arrival and deadlines. Property The weighted-sum lazy bureaucrat problem with common arrivals and deadlines is weakly NP-hard

11 Relationship with the Knapsack the the LBP can be seen as the problem of packing a set of items in a knapsack in a most inefficient but feasible way jobs of I are the items job durations w i are the item weights job profits p i are the item profits the deadline C is the budget or capacity. Notation and preprocessing items sorted in non-decreasing lexicographic order: non-decreasing weights w 1 w 2 w n non-decreasing profits p i C i := C w i, C 1 C 2... C n. w max := max i I w i (= w n) w min := min i I w i (= w 1 ) W := i I w i P := i I p i

12 the SOLUTION PROPERTIES

13 Solution Properties the Definition (Maximal Knapsack Packing) A packing P is a set of items, with the property i P w i C, i.e., a subset of items, which does not exceed the capacity C. Property A packing P is called maximal, iff P {i} is not a packing any i P. Each feasible LBP solution corresponds to a maximal feasible packing of the knapsack with capacity C. Property The capacity used by an arbitrary feasible solution S is bounded from below by C w max + 1.

14 Solution Properties the Definition (Maximal Knapsack Packing) A packing P is a set of items, with the property i P w i C, i.e., a subset of items, which does not exceed the capacity C. Property A packing P is called maximal, iff P {i} is not a packing any i P. Each feasible LBP solution corresponds to a maximal feasible packing of the knapsack with capacity C. Property The capacity used by an arbitrary feasible solution S is bounded from below by C w max + 1.

15 Solution Properties the Definition (Maximal Knapsack Packing) A packing P is a set of items, with the property i P w i C, i.e., a subset of items, which does not exceed the capacity C. Property A packing P is called maximal, iff P {i} is not a packing any i P. Each feasible LBP solution corresponds to a maximal feasible packing of the knapsack with capacity C. Property The capacity used by an arbitrary feasible solution S is bounded from below by C w max + 1.

16 Solution Properties the Definition (Maximal Knapsack Packing) A packing P is a set of items, with the property i P w i C, i.e., a subset of items, which does not exceed the capacity C. Property A packing P is called maximal, iff P {i} is not a packing any i P. Each feasible LBP solution corresponds to a maximal feasible packing of the knapsack with capacity C. Property The capacity used by an arbitrary feasible solution S is bounded from below by C w max + 1.

17 Critical Item (i) the We can only consider the minimum weight item outside the knapsack! Definition (Critical Weight and Critical Item.) i c = arg min{i I j i w j > C} the index of a critical item is the index of the first item that exceeds the capacity, assuming all i i c will be taken as well. The critical weight (w c) is the weight of the critical item (w c = w ic ). Items after the critical cannot be minimum weight item outside!

18 Critical Item (i) the We can only consider the minimum weight item outside the knapsack! Definition (Critical Weight and Critical Item.) i c = arg min{i I j i w j > C} the index of a critical item is the index of the first item that exceeds the capacity, assuming all i i c will be taken as well. The critical weight (w c) is the weight of the critical item (w c = w ic ). Items after the critical cannot be minimum weight item outside!

19 Critical Item (ii) the Proposition The weight of the smallest item left out of any feasible LBP solution is bounded above by the critical weight w c, i.e.: S is feasible min i S w i w c. Consequently, the size of the knapsack can be bounded from below as: w(s) C w c + 1. Example Weights(=profits) {1, 2, 2, 3, 3, 3, 4, 5} and C = 6; i c = 4 and w c = 3

20 Critical Item (ii) the Proposition The weight of the smallest item left out of any feasible LBP solution is bounded above by the critical weight w c, i.e.: S is feasible min i S w i w c. Consequently, the size of the knapsack can be bounded from below as: w(s) C w c + 1. Example Weights(=profits) {1, 2, 2, 3, 3, 3, 4, 5} and C = 6; i c = 4 and w c = 3

21 Main Observation the Observation Once, the smallest item left out of the solution is known, the problem reduces to solving the knapsack problem with a lower and upper bound on its capacity (LU-KP) If items are sorted in non-increasing order acc. to w i and i is the smallest item outside: (KP i ) J = arg min { p j + P i C w i W i + 1 w j C W i } J {i+1,...,n} j J j J Based on it: FPTAS [5] Dynamic Programming We have a new O(nC) approach! ILP Models Subject of this talk! CP Models Combinatorial lower bounds

22 Main Observation the Observation Once, the smallest item left out of the solution is known, the problem reduces to solving the knapsack problem with a lower and upper bound on its capacity (LU-KP) If items are sorted in non-increasing order acc. to w i and i is the smallest item outside: (KP i ) J = arg min { p j + P i C w i W i + 1 w j C W i } J {i+1,...,n} j J j J Based on it: FPTAS [5] Dynamic Programming We have a new O(nC) approach! ILP Models Subject of this talk! CP Models Combinatorial lower bounds

23 the ILP FORMULATIONS

24 ILP Formulation 1 the (ILP 1 ) j I,j i min p i x i i I w i x i C (0.1) i I w j x j + w i (1 x i ) (C + 1)(1 x i ) i I : i i c (0.2) x i {0, 1} i I Constraint (0.2) can be rewritten as w j x j (C i + 1)(1 x i ) i I : i i c. j I,j i where C i = C w i.

25 ILP Formulation 1 the (ILP 1 ) j I,j i min p i x i i I w i x i C (0.1) i I w j x j + w i (1 x i ) (C + 1)(1 x i ) i I : i i c (0.2) x i {0, 1} i I Constraint (0.2) can be rewritten as w j x j (C i + 1)(1 x i ) i I : i i c. j I,j i where C i = C w i.

26 Strengthening Covering Inequalities the Let ( a fixed i I) C = { C i + 1, C c + 1, i i c otherwise Proposition For a given i I, coefficients of the associated covering inequalities w j x j + (C i + 1)x i C i + 1 j I,j i can be down-lifted to k I α kx k C where { min{w c, α k := C}, k = i min{w k, C}, otherwise k I Global covering constraint: w i x i C c + 1 (C c = C w c.) (0.3) j I

27 (ILP 1 ): s the (ILP 1 ) Basic (ILP l 1) Lifted Variant In both cases: Branch-and-Cut approach - capacity constraints are separated on the fly

28 ILP Formulation 2 the (ILP 2 ) min p i x i i I w i x i C (0.4) i I w i x i + z C + 1 (0.5) i I z w c (w c w i )(1 x i ) i I, i i c (0.6) x {0, 1}, z N i I a single additional variable, but significantly simplified structure.

29 Formulations structures the Cap 9 x 1 x 2 x 3 x 4 x 5 z p i w i sol OPT LP ILP ILP l ILP Model I Model II

30 the IS THERE A CONNECTION BETWEEN ILP 1 and ILP 2?

31 A (not so) surprising result! the Proposition Benderization of ILP 2 : Projecting out z variables from ILP 2 results in the mulation ILP 1 in which constraints (0.2) are down-lifted as follows: w j x j + w cx i C + 1 w i Corollary j I,j i If min{w k, C + 1 w i } = w k, all k, i I, then the LP-relaxations of ILP l 1 and ILP 2 are the same. Observation If i, k s.t. w k + w i > C + 1 ILP l 1 can still be stronger than ILP 2 : coefficients next to x j, j i are not down-lifted!

32 A (not so) surprising result! the Proposition Benderization of ILP 2 : Projecting out z variables from ILP 2 results in the mulation ILP 1 in which constraints (0.2) are down-lifted as follows: w j x j + w cx i C + 1 w i Corollary j I,j i If min{w k, C + 1 w i } = w k, all k, i I, then the LP-relaxations of ILP l 1 and ILP 2 are the same. Observation If i, k s.t. w k + w i > C + 1 ILP l 1 can still be stronger than ILP 2 : coefficients next to x j, j i are not down-lifted!

33 A (not so) surprising result! the Proposition Benderization of ILP 2 : Projecting out z variables from ILP 2 results in the mulation ILP 1 in which constraints (0.2) are down-lifted as follows: w j x j + w cx i C + 1 w i Corollary j I,j i If min{w k, C + 1 w i } = w k, all k, i I, then the LP-relaxations of ILP l 1 and ILP 2 are the same. Observation If i, k s.t. w k + w i > C + 1 ILP l 1 can still be stronger than ILP 2 : coefficients next to x j, j i are not down-lifted!

34 the COMPUTATIONAL STUDY

35 Instances the classical instance generator 0/1 KP described in [6] two values: R {1000, 10000} small: n {10, 20, 30, 40, 50, 100, 500, 1000, 2000}, large: n {7500, 10000, 15000, 20000} C {25%, 50%, 75%} of total weight 9 different groups, each with 54 instances in total

36 Instances the 1 Uncorrelated: w j u.r. in [1, R], p j u.r. in [1, R]. 2 Weakly correlated: w j u.r. in [1, R], p j u.r. in [w j R/10, w j + R/10] so that p j 1. 3 Strongly correlated: w j u.r. in [1, R], p j = w j + R/10. 4 Inverse strongly correlated: p j u.r. in [1, R], w j = p j + R/10. 5 Almost strongly correlated: w j u.r. in[1, R], p j u.r. in [w j +R/10 R/500, w j +R/10+R/500]. 6 Subset-sum: w j u.r. in [1, R], p j = w j. 7 Even-odd subset-sum: w j even value u.r. in [1, R], p j = w j, c odd. 8 Even-odd strongly correlated: w j even value u.r. in [1, R], p j = w j + R/10, c odd. 9 Uncorrelated with similar weights: w j u.r. in [100 R, 100 R + R/10], p j u.r. in [1, R].

37 Greedy Heuristics the Table : Average percentage gaps from best known solutions by greedy heuristics. Class Algorithm avg Greedy heuristics greedy[1/p j ] greedy[1/w j ] greedy[w j /p j ] greedy[1/(p j w j )] greedy[1/(p j + w j )] greedy[p j /w j ] greedy-comb Observe: sorting according to w j /p j perms the best, on average, which is also intuitive (we prefer items with low profit and high weight).

38 Formulation relative strength the Table : Average percentage gaps of the different linear programming relaxations. Class Algorithm avg Linear Relaxation ILP ILP l ILP Lifting significantly reduces the LP-gap of (ILP 1 ) (ILP 2 ) provides very strong lower bounds, comparable to those obtained after lifting (ILP 1 ). The first class of instances (uncorrelated weights and profits) is by far the most difficult one, with average LP-gaps of more than 13% all ILP mulations.

39 Formulation Comparison: Over all classes (small) the ILP 1 ILP 2 DP Items ILP 1 ILP l 1 B&C Avg t[sec.s] AVG # of TL

40 Formulation Comparison the Avg t[sec.s] Table : Class # of TL Items ILP l 1 ILP 2 DP ILP l 1 ILP 2 DP

41 Formulation Comparison the Avg t[sec.s] Table : Class # of TL Items ILP l 1 ILP 2 DP ILP l 1 ILP 2 DP

42 Formulation Comparison the Avg t[sec.s] Table : Class # of TL Items ILP l 1 ILP 2 DP ILP l 1 ILP 2 DP

43 the

44 Recap and future developments the We studied ILP and DP approaches solving the LBP problem to optimality. ILP l 1 can be obtained by Benderization of ILP 2 Our computational study showed that the LBP is more difficult than the KP: the latter, instances with several thousands of items can be easily solved, while the LBP, a few thousands items make the problem difficult. Greedy Boss: Find a most profitable subset of jobs S to be executed so that the schedule exceeds the deadline, but after removing any of the scheduled jobs, the deadline is respected. solution properties, valid inequalities, solution approaches vs Greedy Boss Games!

45 Recap and future developments the We studied ILP and DP approaches solving the LBP problem to optimality. ILP l 1 can be obtained by Benderization of ILP 2 Our computational study showed that the LBP is more difficult than the KP: the latter, instances with several thousands of items can be easily solved, while the LBP, a few thousands items make the problem difficult. Greedy Boss: Find a most profitable subset of jobs S to be executed so that the schedule exceeds the deadline, but after removing any of the scheduled jobs, the deadline is respected. solution properties, valid inequalities, solution approaches vs Greedy Boss Games!

46 References the E. M. Arkin, M. A. Bender, J. S. Mitchell, and S. S. Skiena. The lazy bureaucrat scheduling problem. Inmation and, 184(1): , J. Boyar, L. Epstein, L. M. Favrholdt, J. S. Kohrt, K. S. Larsen, M. M. Pedersen, and S. Wøhlk. The maximum resource bin packing problem. Theoretical Computer Science, 362(1 3): , L. Gai and G. Zhang. On lazy bureaucrat scheduling with common deadlines. Journal of Combinatorial Optimization, 15(2): , D. Pisinger. David Pisinger s optimization codes, L. Gourvés, J. Monnot, and A. T. Pagourtzis. The lazy bureaucrat problem with common arrivals and deadlines: Approximation and mechanism design. In L. Gasieniec and F. Wolter, editors, Fundamentals of Theory, volume 8070 of Lecture Notes in Computer Science, pages Springer Berlin Heidelberg, S. Martello, D. Pisinger, and P. Toth. Dynamic programming and strong bounds the 0-1 knapsack problem. Management Science, 45: , 1999.