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Objective scaling ensemble approach for integer linear programming

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Abstract

The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space. An empirical analysis on publicly available benchmark problems demonstrate the efficacy of our approach by outperforming standard solution strategies implemented in modern optimization software.

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References

  • Achterberg, T., Berthold, T.: Improving the feasibility pump. Discrete Optim. 4, 77–86 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Bai, L., Hearn, D., Lawphongpanich, S.: A heuristic method for the minimum toll booth problem. J. Glob. Optim. 48, 533–548 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  • Balas, E., Ceria, S., Dawande, M., Margot, F., Pataki, G.: Octane: a new heuristic for pure 0–1 programs. Oper. Res. 49, 207–225 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  • Balas, E., Martin, C.: Pivot and complement-a heuristic for 0–1 programming. Manag. Sci. 26, 86–96 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  • Balas, E., Schmieta, S., Wallace, C.: Pivot and shift-a mixed integer programming heuristic. Discrete Optim. 1, 3–12 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  • Beliën, J.: Exact and heuristic methodologies for scheduling in hospitals: problems, formulations and algorithms. 4OR 5, 157–160 (2007)

    Article  MATH  Google Scholar 

  • Bertsimas, D., Sim, M.: Robust discrete optimization and network flows. Math. Program. 98, 49–71 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Bley, A., Boland, N., Fricke, C., Froyland, G.: A strengthened formulation and cutting planes for the open pit mine production scheduling problem. Comput. Oper. Res. 37, 1641–1647 (2010)

    Article  MATH  Google Scholar 

  • Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35, 268–308 (2003)

    Article  Google Scholar 

  • Bonami, P., Cornuéjols, G., Lodi, A., Margot, F.: A feasibility pump for mixed integer nonlinear programs. Math. Program. 119, 331–352 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  • Breiman, L.: Bagging predictors. Mach. Learn. 26, 123–140 (1996)

    MATH  Google Scholar 

  • Climer, Sharlee, Zhang, Weixiong: Cut-and-solve: an iterative search strategy for combinatorial optimization problems. Artif. Intell. 170, 714–738 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  • Crainic, T., Gendreau, M., Fravolden, J.: A simplex-based tabu search method for capacitated network design. INFORMS J. Comput. 12, 223–236 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Crainic, T., Gendron, B., Hernu, G.: A slope scaling/Lagrangian perturbation heuristic with long-term memory for multicommodity capacitated fixed-charge network design. J. Heuristics 10, 525–545 (2004)

    Article  MATH  Google Scholar 

  • Danna, E., Rothberg, E., Le Pape, C.: Exploring relaxation induced neighborhoods to improve mip solutions. Math. Program. 102, 71–90 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Dolan, E., Moré, J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Eckstein, J., Nediak, M.: Pivot, cut, and dive: a heuristic for 0–1 mixed integer programming. J. Heuristics 13, 471–503 (2007)

    Article  Google Scholar 

  • Fischetti, M., Glover, F., Lodi, A.: The feasibility pump. Math. Program. 104, 91–104 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Fischetti, M., Lodi, A.: Local branching. Math. Program. 98, 23–47 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Gendreau, M., Potvin, J.-Y. (eds.): Handbook of Metaheuristics, 2nd ed. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-1665-5

  • Gendron, B., Potvin, J.-Y., Soriano, P.: A tabu search with slope scaling for the multicommodity capacitated location problem with balancing requirements. Ann. Oper. Res. 122, 193–217 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • Glover, F., Laguna, M.: Tabu Search. Springer (1999)

  • Gulczynski, D., Golden, B., Wasil, E.: The multi-depot split delivery vehicle routing problem: an integer programming-based heuristic, new test problems, and computational results. Comput. Ind. Eng. 61, 794–804 (2011)

    Article  Google Scholar 

  • Gurobi Optimization, Inc. (2012) Gurobi solves the hardest models. http://www.gurobi.com/company/news/gurobi-solves-the-previously-unsolvable. Accessed 20 Nov 2015

  • Gurobi Optimization, Inc. (2014) Features and benefits overview. http://www.gurobi.com/products/gurobi-optimizer/features-and-benefits. Accessed 31 Dec 2014

  • Hoffman, K.L., Padberg, M.: Solving airline crew scheduling problems by branch-and-cut. Manag. Sci. 39, 657–682 (1993)

    Article  MATH  Google Scholar 

  • Kim, D., Pardalos, P.: A solution approach to the fixed charge network flow problem using a dynamic slope scaling procedure. Oper. Res. Lett. 24, 195–203 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  • Kim, D., Pardalos, P.: Dynamic slope scaling and trust interval techniques for solving concave piecewise linear network flow problems. Networks 35, 216–222 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  • Koch, T., Achterberg, T., Andersen, E., Bastert, O., Berthold, T., Bixby, R., Danna, E., Gamrath, G., Gleixner, A., Heinz, S., et al.: Miplib 2010. Math. Program. Comput. 3, 103–163 (2011)

    Article  MathSciNet  Google Scholar 

  • Linderoth, J., Lodi, A.: MILP software. In: Cochran, J., Cox, L., Keskinocak, P., Kharoufeh, J., Smith, J. (eds.) Wiley Encyclopedia of Operations Research and Management Science. Wiley, New York (2010)

    Google Scholar 

  • Lodi, A.: Mixed integer programming computation. In: Jünger, M., Liebling, T., Naddef, D., Nemhauser, G., Pulleyblank, W., Reinelt, G., Rinaldi, G., Wolsey, L. (eds.) 50 Years of Integer Programming 1958–2008, pp. 619–645. Springer, Berlin (2010)

    Chapter  MATH  Google Scholar 

  • Nahapetyan, A., Pardalos, P.: Adaptive dynamic cost updating procedure for solving fixed charge network flow problems. Comput. Optim. Appl. 39, 37–50 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Nemhauser, G., Wolsey, L.: Integer and Combinatorial Optimization, vol. 18. Wiley, New York (1988)

    MATH  Google Scholar 

  • Nicholson, C., Zhang, W.: Optimal network flow: a predictive analytics perspective on the fixed-charge network flow problem. Comput. Ind. Eng. 99, 260–268 (2016)

    Article  Google Scholar 

  • Patel, J., Chinneck, J.: Active-constraint variable ordering for faster feasibility of mixed integer linear programs. Math. Program. 110, 445–474 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Raack, C., Koster, A., Orlowski, S., Wessäly, R.: On cut-based inequalities for capacitated network design polyhedra. Networks 57, 141–156 (2011)

    MathSciNet  MATH  Google Scholar 

  • Shiina, T., Xu, C.: Dynamic slope scaling procedure to solve stochastic integer programming problem. J. Comput. Model. 2, 133–148 (2012)

    Google Scholar 

  • van den Akker, J., Hurkens, C., Savelsbergh, M.: Time-indexed formulations for machine scheduling problems: column generation. INFORMS J. Comput. 12, 111–124 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, W., Lin, Pi, Wang, N., Nicholson, C., Xue, X.: Probabilistic prediction of postdisaster functionality loss of community building portfolios considering utility disruptions. J. Struct. Eng. 144, 04018015 (2018)

    Article  Google Scholar 

  • Zhang, W., Nicholson, C.: A multi-objective optimization model for retrofit strategies to mitigate direct economic loss and population dislocation. Sustain. Resilient Infrastruct. 1, 123–136 (2016a)

    Article  Google Scholar 

  • Zhang, W., Nicholson, C.: Prediction-based relaxation solution approach for the fixed charge network flow problem. Comput. Ind. Eng. 99, 106–111 (2016b)

    Article  Google Scholar 

  • Zhang, W., Wang, N.: Resilience-based risk mitigation for road networks. Struct. Saf. 62, 57–65 (2016)

    Article  Google Scholar 

  • Zhang, W., Wang, N.: Bridge network maintenance prioritization under budget constraint. Struct. Saf. 67, 96–104 (2017)

    Article  Google Scholar 

  • Zhang, W., Wang, N., Nicholson, C.: Resilience-based post-disaster recovery strategies for road-bridge networks. Struct. Infrastruct. Eng. 13, 1404–1413 (2017)

    Article  Google Scholar 

  • Zhang, W., Yao, Z.: A reformed lattice gas model and its application in the simulation of evacuation in hospital fire. In: Industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on. IEEE, pp. 1543–1547 (2010)

  • Zhang, W., Zhao, L.: Lattice gas model for simulating pedestrian evacuation in the dormitory fire. J. Saf. Environ. 1, 045 (2010)

    Google Scholar 

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Correspondence to Charles D. Nicholson.

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Appendices

Appendices

1.1 A. Experimental results of Easy instances

Name

\(\gamma \) (%)

\(G_{\text {OSEA}}\) (%)

\(T_{\text {OSEA}}\)

\(G_{\text {Standard}}\) (%)

\(T_{\text {Standard}}\)

30_70_45_095_100

11.82

0.00

2.21

0.00

2.79

30n20b8

99.09

0.00

0.38

0.00

2.58

50v-10

88.78

0.12

0.92

0.32

60.00

aflow40b

87.20

0.00

0.94

0.00

60.00

air04

94.12

0.00

11.34

0.00

9.22

app1-2

90.18

0.00

1.34

0.00

47.06

beasleyC3

64.36

0.00

0.24

0.00

10.53

berlin_5_8_0

64.23

0.00

0.03

0.00

60.00

biella1

93.09

1.41

11.60

5.87

60.00

binkar10_1

42.65

0.00

0.40

0.00

8.32

co-100

96.65

0.01

25.07

45.55

60.02

core2536-691

90.62

0.00

16.56

0.00

31.48

cov1075

0.00

0.00

5.97

0.00

3.32

dfn-gwin-UUM

58.95

0.00

2.00

0.00

60.00

eil33-2

97.87

5.43

5.56

0.00

36.46

eilB101

90.74

3.13

2.27

3.02

60.00

enlight13

35.80

0.00

0.01

0.00

3.44

enlight15

53.78

0.00

0.01

0.00

14.42

gmu-35-40

92.92

0.02

0.02

0.03

60.00

gmu-35-50

95.98

0.01

0.03

0.03

60.00

go19

16.99

0.00

60.00

1.18

60.00

harp2

94.85

0.00

0.11

0.00

25.31

ic97_potential

2.63

0.18

60.00

0.08

60.00

iis-100-0-cov

0.00

0.00

60.00

0.00

60.01

iis-bupa-cov

14.97

0.00

60.00

0.00

60.00

iis-pima-cov

55.14

2.94

60.00

2.94

60.00

k16x240

87.95

0.00

0.02

0.00

60.00

lectsched-4-obj

43.26

0.00

0.21

0.00

0.65

m100n500k4r1

95.20

4.17

0.01

4.17

60.00

macrophage

53.41

0.27

0.06

0.00

60.00

mc11

48.53

0.00

1.33

0.00

9.79

mine-166-5

41.08

0.00

0.30

0.00

19.57

mine-90-10

40.11

0.07

0.14

0.07

60.00

mzzv11

96.57

3.91

0.24

0.00

14.93

n3div36

99.69

0.00

24.96

0.15

60.01

n3seq24

98.91

2.25

33.61

2.61

60.04

n4-3

24.14

0.85

60.00

0.19

60.00

n9-3

43.65

3.17

60.00

9.84

60.00

neos-1109824

91.78

0.00

2.08

0.00

26.80

neos-1112782

88.94

0.00

0.15

0.00

8.67

neos-1112787

89.29

0.00

0.13

0.00

1.83

neos-1224597

66.51

0.00

0.25

0.00

0.25

neos-1225589

81.48

0.00

0.08

0.00

0.14

neos-1337307

59.33

0.00

24.75

0.00

25.67

neos-1396125

10.49

0.00

25.63

0.00

10.39

Name

\(\gamma \)

\(G_{\text {OSEA}}\)

\(T_{\text {OSEA}}\)

\(G_{\text {Standard}}\)

\(T_{\text {Standard}}\)

neos-1440225

67.21

0.00

0.76

0.00

23.18

neos-1616732

0.00

0.62

60.00

0.62

60.00

neos-1620770

0.00

0.00

60.00

0.00

60.01

neos-506428

94.65

77.78

7.77

77.78

60.01

neos-520729

99.44

0.00

14.53

0.00

60.02

neos-555424

49.78

0.00

0.31

0.00

5.06

neos-631710

92.67

46.30

32.32

63.49

60.03

neos-686190

90.61

0.59

2.46

10.98

60.00

neos-693347

49.96

1.68

60.00

1.27

60.05

neos-777800

94.89

0.00

7.04

0.00

1.17

neos-824661

92.85

0.00

3.73

0.00

9.13

neos-824695

91.61

0.00

1.09

0.00

2.70

neos-826650

85.79

0.00

0.25

0.00

60.00

neos-826694

86.73

0.00

1.02

0.00

1.97

neos-826812

86.81

0.00

0.64

0.00

0.87

neos-826841

84.78

0.00

0.26

0.00

40.41

neos-885524

99.78

0.00

1.55

0.00

0.58

neos-932816

93.66

0.00

0.88

0.00

0.91

neos-933638

83.64

0.36

60.00

0.00

33.14

neos-933966

89.00

0.00

31.99

0.00

6.68

neos-934278

77.74

8.45

60.00

0.00

42.49

neos-935627

47.56

10.51

60.00

0.19

60.00

neos-935769

41.23

1.95

60.00

0.00

25.78

neos-937511

58.84

0.00

60.00

0.00

10.30

neos-941262

46.16

1.10

60.02

1.10

60.00

neos-941313

79.80

0.00

32.07

0.00

39.15

neos-957389

91.98

0.00

0.39

0.00

1.16

neos15

0.00

0.90

60.00

0.90

60.00

neos16

32.10

0.22

60.00

0.22

60.00

neos18

4.83

0.00

0.44

0.00

41.12

net12

58.07

0.00

0.75

16.08

60.00

nobel-eu-DBE

93.91

2.05

45.11

0.82

60.00

noswot

78.00

0.00

0.01

0.00

42.38

nu60-pr9

90.84

1.58

0.78

3.18

60.35

p80x400b

76.01

0.17

0.07

0.00

60.00

pg

0.00

0.25

60.00

0.25

60.00

pg5_34

0.00

0.03

60.00

0.03

60.00

pigeon-10

82.00

0.00

0.04

0.00

60.00

pigeon-11

82.03

0.00

0.02

0.00

60.00

pw-myciel4

33.24

0.00

60.00

0.00

60.00

pw-myciel4

33.24

0.00

60.00

0.00

60.00

rail507

98.24

0.00

48.07

0.00

60.01

ran14x18

74.42

0.64

0.16

0.64

60.00

ran16x16

72.83

0.10

0.06

0.00

48.51

reblock166

45.54

0.03

0.83

0.06

60.00

reblock67

38.36

0.21

0.06

0.59

60.00

rmatr100-p10

0.00

0.00

28.41

0.00

47.48

rmatr100-p5

0.00

6.69

60.00

6.96

60.00

rmine6

31.66

0.01

0.24

0.00

60.00

rococoB10-011000

87.43

2.54

0.15

7.00

60.00

rococoC10-001000

85.95

0.04

0.08

0.50

60.00

satellites1-25

85.80

0.00

20.80

117.24

60.00

Name

\(\gamma \)

\(G_{\text {OSEA}}\)

\(T_{\text {OSEA}}\)

\(G_{\text {Standard}}\)

\(T_{\text {Standard}}\)

satellites2-60-fs

82.49

144.19

37.60

139.58

60.50

sp97ar

97.61

0.26

22.35

0.88

60.13

sp98ic

98.70

0.19

25.07

0.65

60.00

sp98ir

92.81

0.13

1.40

0.00

30.46

tanglegram1

84.22

0.00

0.87

0.00

4.48

tanglegram2

87.36

0.00

0.11

0.00

0.55

toll-like

55.22

0.33

0.08

1.61

60.00

uct-subprob

5.85

1.57

60.00

0.95

60.37

umts

85.21

0.12

1.77

0.13

60.00

wachplan

86.88

0.00

6.99

0.00

60.00

zib54-UUE

0.00

2.23

60.00

2.23

60.00

1.2 B. Experimental results of Hard instances

Name

\(\gamma \) (%)

\(G_{\text {OSEA}}\) (%)

\(T_{\text {OSEA}}\)

\(G_{\text {Standard}}\) (%)

\(T_{\text {Standard}}\)

a1c1s1

9.90

0.26

60.00

0.02

60.00

b2c1s1

3.91

5.84

60.00

6.75

60.03

bg512142

5.67

5.44

60.00

8.75

60.00

dg012142

7.68

20.70

60.00

25.81

60.00

dolom1

81.32

92.38

60.00

97.80

60.02

germany50-DBM

1.33

2.13

60.00

1.46

60.00

d10200

79.27

0.10

23.79

0.10

60.00

dc1c

95.24

5.95

19.50

91.22

60.00

janos-us-DDM

25.04

0.05

60.00

0.08

60.00

lotsize

44.77

0.47

60.00

1.22

60.00

eilA101-2

99.54

7.68

23.50

7.68

60.14

n3-3

46.72

3.71

60.00

10.18

60.00

neos-948126

33.25

4.22

60.00

4.22

60.01

neos-984165

39.81

23.97

60.00

23.97

60.01

mkc

96.67

0.02

0.11

0.00

60.00

rmatr200-p10

24.30

2.04

60.00

10.36

60.01

nu120-pr3

92.10

3.99

1.12

4.38

60.43

p100x588b

75.65

0.65

0.29

1.71

60.00

p6b

86.36

0.00

0.02

0.00

60.00

pigeon-12

82.07

0.00

0.03

0.00

60.00

pigeon-13

82.10

0.00

0.03

0.00

60.00

protfold

83.98

34.78

0.07

40.91

60.00

queens-30

95.89

8.11

0.03

8.11

60.07

r80x800

84.42

0.06

0.30

0.06

60.00

reblock354

39.29

0.01

0.28

0.04

60.02

reblock420

45.93

15.28

10.03

15.68

60.06

rmatr200-p20

20.87

0.12

60.01

4.67

60.00

rmatr200-p5

18.83

6.84

60.01

9.24

60.01

seymour

29.68

0.24

60.00

0.24

60.10

rococoC11-011100

90.29

3.14

0.18

6.97

60.15

tw-myciel4

38.03

0.00

0.14

0.00

60.00

wnq-n100-mw99-14

90.58

14.24

22.73

9.44

60.03

1.3 C. Experimental results of Open instances

Name

\(\gamma \) (%)

\(G_{\text {OSEA}}\) (%)

\(T_{\text {OSEA}}\)

\(G_{\text {Standard}}\) (%)

\(T_{\text {Standard}}\)

core4872-1529

67.32

3.96

60.00

3.59

60.01

bab1

98.70

31.16

22.35

31.16

60.01

cdma

83.99

32138.99

20.17

32138.99

60.01

dc1l

95.68

8.00

60.06

10.30

61.98

d20200

85.20

0.30

5.37

0.31

60.00

ex1010-pi

86.93

9.93

60.00

11.02

60.00

ger50_17_trans

97.54

10.82

60.00

15.56

60.00

momentum3

68.69

76.50

60.02

76.50

60.03

methanosarcina

56.53

100.00

0.28

100.00

60.00

n3700

92.85

23.33

60.00

24.64

60.00

n3705

92.32

24.13

60.00

24.22

60.00

n370a

91.65

23.71

60.00

25.09

60.00

neos-937815

48.89

0.56

60.00

0.56

60.00

ns4-pr3

0.99

0.20

60.00

0.19

60.00

ns4-pr9

0.00

0.16

60.00

0.16

60.00

pigeon-19

82.33

5.56

0.07

5.56

60.00

ramos3

18.69

44.77

60.13

48.30

60.11

rmine10

34.92

2.45

14.60

2.46

60.01

rmine14

96.73

2426.06

1.54

2426.06

60.08

rococoC12-111000

93.73

25.93

0.28

25.54

60.00

rvb-sub

99.39

94.70

22.05

93.47

60.01

siena1

79.86

92.48

60.00

97.73

60.12

sing2

47.56

2.30

60.00

9.22

60.01

stockholm

55.71

99.50

60.01

99.49

60.05

sts405

0.00

60.98

60.04

60.98

60.01

sts729

0.00

62.44

60.01

62.44

60.12

t1717

97.80

43.80

60.02

43.80

60.01

t1722

97.21

35.94

60.00

32.35

60.00

usAbbrv.8.25_70

69.84

21.49

0.08

22.13

60.00

van

0.00

95.17

60.01

95.17

60.01

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Zhang, W., Nicholson, C.D. Objective scaling ensemble approach for integer linear programming. J Heuristics 26, 1–19 (2020). https://doi.org/10.1007/s10732-019-09418-9

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  • DOI: https://doi.org/10.1007/s10732-019-09418-9

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