Stochastic Integer Programming An Algorithmic Perspective

Transcription

1 Stochastic Integer Programming An Algorithmic Perspective School of Industrial & Systems Engineering

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3 Outline Two-stage SIP Formulation Challenges Simple Integer Recourse Structural results General integer recourse A few Decomposition algorithms Multi-stage SIP Formulation Algorithms Scenario decomposition Polyhedral results 3

4 Two-Stage SIPs Decisions in two stages Stage 1 decision Observe uncertainties Stage 2 decision ( here and now ) ( recourse ) Known distribution The probability distribution of the uncertainties is known Exogenous uncertainties Stage 1 decisions do not affect the distribution Discrete/Combinatorial decisions Goal: Minimize cost of stage 1 decisions + Expected cost of stage 2 decisions 4

5 Examples Resource acquisition (Dempster et al.,1981,1983) Acquire machines Observe processing times Schedule jobs. Location-Routing (Laporte et al., 1994) Locate depots Observe demand Route vehicles. Ground Holding in Airline Operations (Ball et al., 2003) Schedule arrival/departure Observe delays Decide optimal holding pattern. 5

6 General Formulation 6

7 Difficulty 1 Evaluating the second-stage cost for a fixed first-stage decision and a particular realization of the uncertain parameters. Involves solving (possibly) NP-hard integer program E.g. Second stage: schedule jobs after observing processing requirements. Most SIP research assumes away this difficulty. 7

8 Difficulty 2 Evaluating the expected second-stage cost for a fixed first-stage decision. If the uncertain parameters have a continuous distribution: involves integrating the value function of an integer program and is in general impossible. If the uncertain parameters have a discrete distribution: if and each component has 3 independent realizations, then!! involves solving a huge number of similar integer programs. 8

9 Difficulty 3 is the value function of an integer program Is non-convex and discontinuous (lower-semicontinuous) 9

10 Difficulty 3 Optimizing, with respect to. Theorem (Stougie 1985; Schultz 1993,1995) If for all and, and then is real-valued and lower-semicontinuous on. If, in addition, has an absolutely continuous density, then is continuous on. In general, is non-convex and often discontinuous, and therefore, so is. 10

11 Non-convexity and Discontinuity 11

12 Simple Integer Recourse (Stochastic RHS) 12

13 Dealing with the difficulties (SIR) No Difficulty 1 where. Let and then Dealing with Difficulty 2: if we know how to evaluate the univariate functions and, we are done. 13

14 Dealing with Difficulty 2 (SIR) Theorem (Louveaux and van der Vlerk, 1993) and In many cases, the above sums are easy to evaluate. Separability allows for the easy evaluation of. Difficulty 2 resolved. 14

15 Dealing with Difficulty 3 (SIR) Let then In general, is not convex. Theorem (Klein Haneveld et al., 1995) There exists a random variable such that Here denotes the convex hull of a function over its entire domain. 15

16 Example 16

17 Convexification Klein Haneveld et al (1995) give an algorithm for constructing the convex hull in case of discrete distributions. Theorem (Klein Haneveld, 1995) If is of full row rank then In some cases Then, we only need to solve a problem with continuous simple recourse 17

18 Example Klein Haneveld et al.,

19 Approximations Similar results: by perturbing the distribution, convex (continuous simple recourse type) approximations/lower bounding functions for the SIR function can be obtained. Uniform error bound on the approximation Solve SIPs with SIR approximately or obtain lower bounds via solving continuous recourse models. Can we use these within a branch and bound scheme? Can we get convex hull/lower bound restricted to a subset of the domain? 19

20 General Mixed-Integer Recourse Assume that the recourse function is well-defined. Dealing with Difficulty 1 Assume that the second stage MIPs are easily solvable exactly. Some literature on using approximations (e.g. Dempster 1983). Dealing with Difficulty 2 Approximate the distribution by a discrete distribution with a manageable number of realizations, e.g., by sampling. Still need to solve several similar MIPs. 20

21 (One way of) Dealing with Difficulty 2 The Sample Average Approximation Method Need to solve Let be the set of -optimal solutions and be the optimal objective value. Generate i.i.d samples and solve the Sample Average Approximating (SAA) Problem: Let be the set of -optimal solutions (with ) and be the optimal objective value. 21

22 Convergence of the SAA Method Theorem (Kleywegt et al. 2001) If is finite then and. Moreover the convergence is exponentially fast. The sample size needed to obtain an -optimal solution to the true problem with probability is 22

23 Convergence of the SAA Method Theorem (Ahmed and Shapiro, 2002) If is bounded (not necessarily finite) then the sample size needed to obtain an -optimal solution to the true problem with probability is For pure integer recourse, only right hand-side uncertainty, and discrete distribution with scenarios, the sample size needed to obtain an -optimal solution to the true problem with probability is 23

24 Practical SAA Method Select a sample size, solve independent SAA problems. Let and be the optimal value and optimal solution of the I-th SAA problem. A point estimate of a lower bound on is given by (Mak et al, 1999) A point estimate of the objective value of a candidate solution is where is a large sample. Then a point estimate of the optimality gap of the candidate solution is obtained. Variability in the point estimates can be used to obtain confidence intervals. 24

25 Sample SAA Computation Two-stage SIP with 10 8 scenarios. 25

26 Solving Similar IPs Still need to solve many similar MIPs. In case of stochastic linear programming, this problem is tackled using various warm-start strategies arising from exploiting LP duality. Unfortunately, a computationally useful IP duality theory is not yet mature. Two of the approaches in the SIP literature for pure integer secondstage: Gröbner Basis (Schultz et al., 1998) Value function construction (Kong et al., 2004) 26

27 Dealing with Difficulty 3 Assume a finite ( manageable ) number of scenarios. where and 27

28 Deterministic Equivalent Large-scale MIP. If not too many scenarios, use, e.g., CPLEX. Otherwise decompose 28

29 Two-Stage Decomposition Solve is computationally tractable lower bounding approximation of Evaluate Evaluation is via decomposition Refinement No Yes STOP 29

30 Binary First-stage Integer L-Shaped Method (Laporte & Louveaux, 1993) Here Denote Let Let Note Then and 30

31 Integer L-Shaped Method Master Problem Cuts Evaluate Evaluation is via decomposition Refinement No Yes STOP 31

32 Integer L-Shaped (Remarks) Master problem is a 0-1 MIP solve by branch & bound. (Decomposed) MIP subproblems in the evaluation steps. Implementation: Do not B&B to optimality branch-and-cut. Add cuts whenever a binary solution is encountered in the B&B search. Cut quality depends on the quality of the lower bound. Can be improved if more information is available on the value function. Other types of valid cuts can also be added. Standard LP Benders cuts are valid, but weak. Finite termination guaranteed. Application: Stochastic vehicle routing (Laporte et al. 2002) 32

33 Disjunctive Decomposition Sen and Higle (2000) Binary first-stage, Mixed-binary second-stage and fixed recourse. Goal Avoid solving MIP subproblems during evaluation. Exploit similarity of subproblems. Given, for each solve the LP-relaxation. If solution is fractional, find a valid inequality for the LP. Only valid for current subproblem 33

34 The C3 Theorem Sen and Higle (2000) There exists a function such that the cut can be translated to be valid for the subproblem corresponding to any scenario and any first-stage solution. The cut-coefficients do not change ( common ). However, is piece-wise linear and concave in. 34

35 Convexification Since only binary first-stage solutions are relevant, convexify (linearize) the concave function. RHS linear in first-stage variables. Pass Benders cuts to master. 35

36 The D2 Algorithm (Remarks) As long as we have a separation scheme for finding proper valid inequalities for the subproblems, the algorithm terminates in a finite number of steps with the optimum. Application: Server location (Ntaimo and Sen, 2003) Sen and Sherali (2004) extend the approach to when the second-stage problems are partially solved by Branch-andcut. 36

37 Mixed-Integer First Stage Binary first-stage Need to evaluate/approximate value function only at binary solutions (finiteness inherent). Mixed-integer first-stage Optimize a non-convex discontinuous objective over a (semi)-continuous domain. If Pure integer Second-stage: Theorem (Schultz et al.1998) The expected second-stage value function is piece-wise constant (over polyhedral regions), and an optimal solution to the problem lies at an extreme point of one of these polyhedra. 37

38 Pure Integer Second Stage Inherent finiteness. Algorithm: Check all these extreme points. Difficulties Polyhedra not easy to characterize. May be too many. Alternative: Use continuous branch and bound. 38

39 Continuous Branch & Bound Objective P Objective P R R U L L Variable Variable (a) Lower bounding (b) Upper bounding Objective P R U L R1 R2 R R1 R2 fathom Variable subdivide (c) Domain subdivision (d) Search tree 39

40 Finiteness Issue The most common branching scheme is rectangular partitioning - branching along a variable axis. The resulting partitions are rectangular. Finite sequences A potentially infinite sequence The polyhedral shaped discontinuous pieces cannot be isolated by a finite number of rectangular partitions. There will be some partitions containing discontinuities. These will require infinite partitioning for bounds to converge. 40

41 Pure Integer Second Stage B&B in the tender space (Ahmed et al., 2004) Fixed technology matrix. Solve the problem in the space of the tender variables. Discontinuous are orthogonal to the tender axes. 41

42 The B&B Algorithm Lower bounding: Second-stage value function is non-decreasing and lsc. Upper bounding: Function evaluation. L χ B χ U χ Branching: Partition along the discontinuities. Maintains rectangular partitions. Finite convergence to global optima. L χ B χ B χ U χ Improved lower bounding methods. 42

43 Sample Computation Test set: Capacity acquisition-assignment problems. Ahmed and Garcia,

44 More on Pure Integer Second Stage Hemmecke and Schultz, 2003 Rhs uncertainty. Pure integer first stage. IP Test Sets (Computational Algebra). van der Vlerk, 2004 Rhs uncertainty. Convex lower bounds by changing the distribution. Kong et al., 2004 Rhs uncertainty. Pure integer first stage. Construct and optimize value function. Kong et al Conditions for total unimodularity. Benders: MIP master may be better than LP master. 44

45 Multi-Stage SIPs Decisions in multiple (but, a finite number of) stages Stage 1 decision Observe uncertainties Stage 2 decision Observe uncertainties Stage 3 decision.. Example Applications: Unit commitment (Takriti et al. 1996, Caroe & Schultz, 1999). Capacity/Production planning (Ahmed et al. 2001, Lulli and Sen, 2002 ). Asset liability management (Drijver et al., 2000). Challenges: Same as before but now in multiple-folds! 45

46 The Scenario Tree Assuming finite support, the evolution of the uncertain parameters can be modeled as a scenario tree. 46

47 A Formulation Deterministic equivalent formulation. Solution at a node depends on the solutions in ancestor nodes. Index according to node a tree formulation. 47

48 Scenario Decomposition 48

49 The Scenario Formulation Non-anticipativity constraints 49

50 Lagrangian Relaxation Any feasible solution to the Lagrangian dual provides a lower bound to the true optimal value. Evaluating the dual function requires solving one deterministic problem per scenario (Decomposition). Dual involves maximizing a concave non-smooth function can be solved using non-smooth optimization techniques. Difficulty: Many dual multipliers. Difficulty: Duality Gap 50

51 Caroe and Schultz (1999) Dual Decomposition Use Lagrangian dual as the lower bounding scheme within a branch and bound algorithm. Branch to enforce non-anticipativity. Finite termination in case of pure integer solutions. Applicable to two-stage stochastic integer programs. Application: Unit commitment problem. 51

52 Polyhedral Results Given valid inequalities for a deterministic MIP, can we find a valid inequality for the stochastic counterpart? Generating tree inequalities from path inequalities. Branch and cut schemes for deterministic equivalent. Tighten subproblems within decomposition based branch and cut schemes. 52

53 Example: Uncapacitated Lot-sizing Common substructure in many production planning problems (l,s) inequalities sufficient to describe convex hull. Exponential family polynomially separable. 53

54 Stochastic Uncapacitated Lot-sizing Guan et al. (2004) Given any subset of the nodes, the corresponding (l,s) inequalities are valid. These inequalities can be combined to generate a new family of inequalities. Necessary and sufficient conditions for the inequalities to be facetdefining. Excellent performance within branch & cut. The combining idea is quite general and can be applied to other SIPs. 54

55 Concluding Remarks Would have liked to talk about Approximation Algorithms for SIP. Survey articles on SIP: Klein Haneveld and van der Vlerk (1998) Louveaux and Schultz (2003) Schultz et al. (1995) Sen (2004) WWW Resources: SP Community Page: Bibliography (2003): Test Problems: 55

56 References S. Ahmed and A. Shapiro. The sample average approximation method for stochastic programs with integer recourse. Optimization Online, S. Ahmed and R. Garcia. Dynamic Capacity Acquisition and Assignment under Uncertainty. Annals of Operations Research, 124: , 2003 S. Ahmed, M. Tawarmalani, and N. V. Sahinidis. A finite branch and bound algorithm for two-stage stochastic integer programs. Mathematical Programming, 100: , M. O. Ball, R. Hoffman, A. R. Odoni, and R. Rifkin. A stochastic integer program with dual network structure and its application to the ground-holding problem. Operations Research, 51(1): , C. C. Carøe and R. Schultz. Dual decomposition in stochastic integer programming. Operations Research Letters, 24(1-2):37-45, M.A.H. Dempster, M.L. Fisher, L. Jansen, B.J. Lageweg, J.K. Lenstra, and A.H.G. Rinnooy Kan. Analytical evaluation of hierarchical planning systems. Operations Research, 29: , M.A.H. Dempster, M.L. Fisher, L. Jansen, B.J. Lageweg, J.K. Lenstra, and A.H.G. Rinnooy Kan. Analysis of heuristics for stochastic programming: results for hierarchical scheduling problems. Mathematics of Operations Research, 8: , R. Hemmecke and R. Schultz. Decomposition of test sets in stochastic integer programming. Mathematical Programming, 94: , Y. Guan, S. Ahmed and G.L. Nemhauser. A branch-and-cut algorithm for the stochastic uncapacitated lot-sizing problem. Stochastic Programming E-Print Series, W.K. Klein Haneveld, L. Stougie and M.H. van der Vlerk. On the convex hull of the simple integer recourse objective function, Annals of Operations Research, 56: , W.K. Klein Haneveld and M. H. van der Vlerk. Stochastic integer programming: general models and algorithms. Ann. Oper. Res., 85:39-57, A.J. Kleywegt, A. Shapiro, and T. Homem-de-Mello. The Sample Average Approximation Method for Stochastic Discrete Optimization. SIAM Journal on Optimization, 12: , N. Kong, A.J. Schaefer and B. Hunsaker. Two-Stage Integer Programs with Stochastic Right-Hand Sides: A Superadditive Dual Approach, Technical Report, University of Pittsburgh, F.V. Louveaux and R. Schultz. Stochastic Integer Programming. Chapter 4 in Handbooks in Operations Research and Management Science, Vol. 10, Stochastic Programming (Ruszczynski and Shapiro etd.), Elsevier,

57 References G. Laporte and F.V. Louveaux. The integer L-shaped method for stochastic integer programs with complete recourse. Operations Research Letters, 13: , G. Laporte, F.V. Louveaux, and L. van Hamme. Exact solution of a stochastic location problem by an integer L-shaped algorithm. Transportation Science, 28(2):95-103, G. Laporte, F.V. Louveaux, and L. van Hamme. An integer L-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Operations Research, 50: , F. Louveaux and M.H. van der Vlerk. Stochastic programming with simple integer recourse, Mathematical Programming, 61(3): , W.K. Mak, D.P. Morton and R.K. Wood. Monte Carlo Bounding Techniques for Determining Solution Quality in Stochastic Programs. Operations Research Letters, 24:47-56, L. Ntaimo and S. Sen. The Million-Variable March for Stochastic Combinatorial Optimization. To appear in the Journal of Global Optimization, R. Schultz. Continuity properties of expectation functions in stochastic integer programming. Mathematics of Operations Research, 18(3): , R. Schultz. On structure and stability in stochastic programs with random technology matrix and complete integer recourse. Mathematical Programming, 70:73-89, R. Schultz, L. Stougie, and M.H. van der Vlerk. Two-stage stochastic integer programming: a survey. Statistica Neerlandica, 50(3): , R. Schultz, L. Stougie and M.H. van der Vlerk. Solving stochastic programs with complete integer recourse: a framework using Groebner Bases, Mathematical Programming, 83(2): , S. Sen and J. Higle. The C3 Theorem and a D2 Algorithm for Large Scale Stochastic Mixed-Integer Programming: Set Convexification. Technical Report, University of AZ S. Sen and H. D. Sherali. Decomposition with Branch-and-Cut Approaches for Two Stage Stochastic Mixed-Integer Programming. Technical Report, University of AZ S. Sen. Algorithms for Stochastic Mixed-Integer Programming Models. Technical Report, University of AZ L. Stougie. Design and analysis of algorithms for stochastic integer programming, PhD Thesis, Center for Mathematics and Computer Science, Amsterdam, M. H. van der Vlerk. Convex approximations for complete integer recourse models. Mathematical Programming, 99(2): ,