Solving a Five-Objective Preemptive Goal Programming Problem from the Literature

Jsun Yui Wong

The computer program listed below seeks to solve the following multi-objective preemptive (lexicographic) goal program from Baykasoglu, Owen, and Gindy [12, p. 971, Test problem A-2]:

Lexmin

{ z1=(X(3)), z2=(X(10)), z3=(5 * X(5)), z4=(3 * X(7)), z5=(X(4)) }

subject to

X(3) – X(4) + X(1) + X(2)=80
X(5) + X(1)=70
X(7) + X(2)=45
X(9) – X(10)+ X(1) + X(2)=90

X(1), X(2), X(3), …, X(10)>=0.

One notes that line 1302, which is 1302 P = -100000000000000 * X(3) – 10000000000 * X(10) – 10000000000 * 5 * X(5) – 1000000 * 3 * X(7) – X(4), is a use of Hillier and Lieberman’s streamlined procedure [40, pp. 289-291] for preemptive goal programming.
0 DEFDBL A-Z
1 REM DEFINT X, A
2 DIM N(99), A(45222), X(45012), D(111), P(111), PS(33), J44(45202), J(45211), AA(99), STIO(45303)
81 FOR JJJJ = -32000 TO 32000

85 RANDOMIZE JJJJ
87 M = -4E+250
121 FOR J44 = 1 TO 10

122 A(J44) = FIX(RND * 50)
123 NEXT J44

128 FOR I = 0 TO FIX(RND * 800000)
129 FOR KKQQ = 1 TO 10

130 X(KKQQ) = A(KKQQ)
131 NEXT KKQQ

135 FOR IPP = 1 TO FIX(1 + RND * 3.3)

143 j = 1 + FIX(RND * 10)
144 IF RND < .5 THEN GOTO 156 ELSE GOTO 162

145 REM GOTO 162

156 r = (1 – RND * 2) * A(j)
158 X(j) = A(j) + (RND ^ (RND * 15)) * r

161 GOTO 169

162 IF RND < .5 THEN X(j) = A(j) – FIX(1 + RND * 2.3) ELSE X(j) = A(j) + FIX(1 + RND * 2.3)
169 NEXT IPP

291 X(3) = 80 + X(4) – X(1) – X(2)
296 X(5) = 70 – X(1)
298 X(7) = 45 – X(2)
299 X(9) = 90 + X(10) – X(1) – X(2)
331 FOR J44 = 1 TO 10
332 IF X(J44) < 0 THEN 1670

334 NEXT J44

1302 P = -100000000000000 * X(3) – 10000000000 * X(10) – 10000000000 * 5 * X(5) – 1000000 * 3 * X(7) – X(4)

1311 IF P <= M THEN 1670
1420 M = P

1442 FOR KLX = 1 TO 10

1455 A(KLX) = X(KLX)
1456 NEXT KLX

1557 GOTO 128
1670 NEXT I

1677 IF M < -75111111 GOTO 1999

1931 PRINT A(1), A(2), A(3), A(4), A(5)

1939 PRINT A(6), A(7), A(8), A(9), A(10), M, JJJJ
1999 NEXT JJJJ
This BASIC computer program was run with QB64v1000-win [97]. The complete output of a single run through JJJJ= -31062 is shown below:

70          20          0          10          0
10.95891032498612          25          3.489204533175562
0          7.00201049957276D-19          -75000010          -31548

70       20       0       9.9999999999999998
0
41.99957990943607       25       .1944046351790445
0       5.86148583170695D-19       -75000010       -31062
.
.
.

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [97], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31062 was 13 minutes, total, including the time for “Creating .EXE file.” One can compare the computational results above with those in Baykasoglu, Owen, and Gindy [12, p. 971].

Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

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Solving a Nonlinear Goal Programming Problem from the Literature

 

Jsun Yui Wong

The computer program listed below seeks to solve the following nonlinear goal program from Baykasoglu [10, p. 60, Example 4]:

Lexmin (X(4) + X(6) + X(7) + X(8) + X(9) + X(10) + X(11)),

which are 7 deviational variables

subject to

X(4) – X(5) + 5.28 * X(1) ^ 2 + 3.74 * X(2) ^ 2 + 5.28 * X(3) ^ 2 =3.575

X(6) – X(7) + .178 / X(1) ^ 2 =1

X(8) – X(9) + .255 / X(2) ^ 2 =1

X(10) – X(11) + .178 / X(3) ^ 2=1

X(1), X(2), X(3),…, X(11)>=0.
0 DEFDBL A-Z
1 REM DEFINT X, A
2 DIM N(99), A(45222), X(45012), D(111), P(111), PS(33), J44(45202), J(45211), AA(99), STIO(45303)
81 FOR JJJJ = -32000 TO 32000

85 RANDOMIZE JJJJ
87 M = -4E+250
121 FOR J44 = 1 TO 11

122 A(J44) = RND

123 NEXT J44

128 FOR I = 0 TO FIX(RND * 300000)

129 FOR KKQQ = 1 TO 11

130 X(KKQQ) = A(KKQQ)
131 NEXT KKQQ

135 FOR IPP = 1 TO FIX(1 + RND * 3.3)

143 j = 1 + FIX(RND * 11)
156 r = (1 – RND * 2) * A(j)
158 X(j) = A(j) + (RND ^ (RND * 15)) * r

161 GOTO 169

162 IF RND < .5 THEN X(j) = A(j) – FIX(1 + RND * 2.3) ELSE X(j) = A(j) + FIX(1 + RND * 2.3)

169 NEXT IPP

291 X(4) = 3.575 + X(5) – 5.28 * X(1) ^ 2 – 3.74 * X(2) ^ 2 – 5.28 * X(3) ^ 2

293 X(6) = 1 + X(7) – .178 / X(1) ^ 2

311 X(8) = 1 + X(9) – .255 / X(2) ^ 2

322 X(10) = 1 + X(11) – .178 / X(3) ^ 2

331 FOR J44 = 1 TO 11

332 IF X(J44) < 0 THEN 1670

334 NEXT J44

1297 P = -1 * (X(4) + X(6) + X(7) + X(8) + X(9) + X(10) + X(11))

1311 IF P <= M THEN 1670
1420 M = P

1442 FOR KLX = 1 TO 11

1455 A(KLX) = X(KLX)
1456 NEXT KLX

1557 GOTO 128
1670 NEXT I

1677 IF M < -.4376 GOTO 1999

1931 PRINT A(1), A(2), A(3), A(4), A(5)
1933 PRINT A(6), A(7), A(8), A(9), A(10), A(11), M, JJJJ
1999 NEXT JJJJ

This BASIC computer program was run with QB64v1000-win [94]. The complete output of a single run through JJJJ= -31935 is shown below:

.4219004621945798 .673336488003553 .421900462194581
2.763631337665551D-16 3.287775278315277D-04
2.506133321700244D-16 1.291746932102673D-19 .4375604118970625
3.143269644711177D-24 5.51354751784916D-15 1.499886091763481D-19
-.4375604118970685 -31958

.4219004621945798       .6732712066077522       .4219004621945798
1.769417945496343D-16       4.769412653209947D-19
5.136407792150057D-16       2.828791496637557D-27       .4374513366208146
4.552457365592347D-30       5.136407792150077D-16       1.46022525235516D-21
-.4374513366208158       -31935
.
.
.

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [94], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31935 was 30 seconds, not including the time for “Creating .EXE file.” One can compare the computational results above with those in Baykasoglu [10, p. 60].

Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

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[4] Andre R. S. Amaral (2012), The Corridor Allocation Problem. Computers and Operations Research 39 (2012), pp. 3325-3330.

[5 Oscar Augusto, Bennis Fouad, Stephane Caro (2012). A new method for decision making in multi-objective optimization problems. Pesquisa Operacional, Sociedade Brasileira de Pesquisa Operacional, 2012 32 (3), pp.331-369.

[6] Miguel F. Anjos, Anthony Vannelli, Computing Globally Optimal Solutions for Single-Row Layout Problems Using Semidefinite Programming and Cutting Planes. INFORMS Journal on Computing, Vol. 20, No. 4, Fall 2008, pp. 611-617.

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Solving a Multi-Objective Integer Nonlinear Goal Programming Problem

Jsun Yui Wong

The computer program listed below seeks to solve the immediately following two-objective integer nonlinear goal programming problem:

Minimize (X(3) + 2 * X(5)), priority 1

minimize (X(4) + 2 * X(6)), priority 2

priority 1>>priority 2

(Or minimize 100000000 * (X(3) + 2 * X(5)) + (X(4) + 2 * X(6)); see Hillier and Lieberman [38, p.291].)

subject to

X(3) – X(4) + (X(1) + 3) / (X(1) + 1) = 2
X(5) – X(6) + (X(2) ^ 2 + 5) / (X(2) + 1) = 3
X(1) ^ 2 + 4 * X(2) <= 4
X(1) + X(2) ^ 2 <= 10

X(1),…, X(6) are integer variables and >=0, X(3)*X(4)=0, X(5)*X(6)=0.

The problem above is based on the problem on pp. 47-48 of Malhotra and Arora [57].

The computer program listed below uses the streamlined procedure of goal programming in Hillier and Liberman [38, p. 291].
0 DEFDBL A-Z
1 REM DEFINT X, A
2 DIM N(99), A(45222), X(45012), D(111), P(111), PS(33), J44(45202), J(45211), AA(99), STIO(45303)
81 FOR JJJJ = -32000 TO 32000

85 RANDOMIZE JJJJ
87 M = -4E+250
121 FOR J44 = 1 TO 6

124 A(J44) = FIX(RND * 10)
125 NEXT J44

128 FOR I = 0 TO FIX(RND * 30000)

129 FOR KKQQ = 1 TO 6

130 X(KKQQ) = A(KKQQ)
131 NEXT KKQQ

135 FOR IPP = 1 TO FIX(1 + RND * 3.3)

143 j = 1 + FIX(RND * 6)
145 GOTO 162
154 REM IF j < .5 THEN GOTO 156 ELSE GOTO 162

156 r = (1 – RND * 2) * A(j)
158 X(j) = A(j) + (RND ^ (RND * 15)) * r

161 GOTO 169

162 IF RND < .5 THEN X(j) = A(j) – FIX(1 + RND * 2.3) ELSE X(j) = A(j) + FIX(1 + RND * 2.3)
169 NEXT IPP

177 FOR J44 = 1 TO 6
178 X(J44) = INT(X(J44))
179 NEXT J44
291 X(3) = 2 + X(4) – (X(1) + 3) / (X(1) + 1)
293 X(5) = 3 + X(6) – (X(2) ^ 2 + 5) / (X(2) + 1)

391 IF X(1) ^ 2 + 4 * X(2) > 4 THEN 1670
393 IF X(1) + X(2) ^ 2 > 10 THEN 1670
1072 FOR J44 = 1 TO 6

1078 IF X(J44) < 0## THEN 1670
1079 NEXT J44

1083 IF X(1) > 22 THEN 1670

1093 IF X(2) > 11 THEN 1670

1297 P = -100000000 * (X(3) + 2 * X(5)) – (X(4) + 2 * X(6))

1311 IF P <= M THEN 1670
1420 M = P

1442 FOR KLX = 1 TO 6

1455 A(KLX) = X(KLX)
1456 NEXT KLX

1557 GOTO 128
1670 NEXT I

1677 IF M < -999999999999 THEN 1999
1931 PRINT A(1), A(2), A(3), A(4), A(5)
1933 PRINT A(6), M, JJJJ
1999 NEXT JJJJ

This BASIC computer program was run with QB64v1000-win [95]. The complete output of a single run through JJJJ= -31957 is shown below:

0          1          0          1          0
0          -1          -31990

0 1 0 1 0
0 -1 -31989

0 1 0 1 0
0 -1 -31973

1 0 0 0 0
2 -4 -31970

1 0 0 0 0
2 -4 -31968

1 0 0 0 0
2 -4 -31967

1 0 0 0 0
2 -4 -31966

0 1 0 1 0
0 -1 -31957

.
.
.
Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [95], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31957 was 3 seconds, not including the time for “Creating .EXE file” (18 seconds, total, including the time for “Creating .EXE file”).

Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

[1] Irfan Ali, Yashpal Singh Raghav, Abdul Bari (2013). Compromise allocation in multivariate stratified surveys with stochastic quadratic cost function, Journal of Statistical Computation and Simulation 2013, Vol. 83, No. 5, pp. 962-976.

[2] Andre R. S. Amaral (2006), On the Exact Solution of a Facility Layout Problem. European Journal of Operational Research 173 (2006), pp. 508-518.

[3] Andre R. S. Amaral (2008), An Exact Approach to the One-Dimensional Facility Layout Problem. Operations Research, Vol. 56, No. 4 (July-August, 2008), pp. 1026-1033.

[4] Andre R. S. Amaral (2011), Optimal Solutions for the Double Row Layout Problem. Optimization Letters, DOI 10.1007/s11590-011-0426-8, published on line 30 November 2011, Springer-Verlag 2011.

[5] Andre R. S. Amaral (2012), The Corridor Allocation Problem. Computers and Operations Research 39 (2012), pp. 3325-3330.

[6] Oscar Augusto, Bennis Fouad, Stephane Caro (2012). A new method for decision making in multi-objective optimization problems. Pesquisa Operacional, Sociedade Brasileira de Pesquisa Operacional, 2012 32 (3), pp.331-369.

[7] Miguel F. Anjos, Anthony Vannelli, Computing Globally Optimal Solutions for Single-Row Layout Problems Using Semidefinite Programming and Cutting Planes. INFORMS Journal on Computing, Vol. 20, No. 4, Fall 2008, pp. 611-617.

[8] Miguel F. Anjos (2012), FLPLIB–Facility Layout Database. Retrieved on September 25 2012 from http://www.gerad.ca/files/Sites/Anjos/indexFR.html

[9] David L. Applegate, Robert E. Bixby, Vasek Chvatal, William J. Cook, The Traveling Salesman Problem: A Computational Study. Princeton and Oxford: Princeton University Press, 2006.

[10] Ritu Arora, S. R. Arora (2015). A cutting plane approach for multi-objective integer indefinite quadratic programming problem: OPSEARCH of the Operational Research Society of India (April-June 2015), 52(2):367-381.

[11] H. Bernau (1990 ). Active constraint strategies in optimization. Geographical data inversion methods and applications. pp. 15-31.

[12] Victor Blanco, Justo Puerto (2011). Some algebraic methods for solving multiobjective polynomial integer programs, Journal of Symbolic Computation, 46 (2011), pp. 511-533.

[13] Jerome Bracken, Garth P. McCormick, Selected Applications of Nonlinear Programming. New York: John Wiley and Sons, Inc., 1968.

[14] Regina S. Burachik, C. Yalcin Kaya, M. Mustafa Rizvi (March 19, 2019), Algorithms for Generating Pareto Fronts of Multi-objective Integer and Mixed-Integer Programming Problems, arXiv: 1903.07041v1 [math.OC] 17 Mar 2019.

[15] R. C. Carlson and G. L. Nemhauser, Scheduling To Minimize Interaction Cost. Operations Research, Vol. 14, No. 1 (Jan. – Feb., 1966), pp. 52-58.

[16] Ta-Cheng Chen (2006). IAs based approach for reliability redundany allocation problems. Applied Mathematics and Computation 182 (2006) 1556-1567.

[17] Leandro dos Santos Coelho (2009), Self-Organizing Migrating Strategies Applied to Reliability-Redundany Optimization of Systems. IEEE Transactions on Reliability, Vol. 58, No. 3, 2009 September, pp. 501-519.

[18] William Conley (1981). Optimization: A Simplified Approach. Published 1981 by Petrocelli Books in New York.

[19] Lino Costa, Pedro (2001). Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems. Computers and Chemical Engineering, Vol. 25, pp. 257-266, 2001.

[20] George B. Dantzig, Discrete-Variable Extremum Problems. Operations Research, Vol. 5, No. 2 (Apr., 1957), pp. 266-277.

[21] Pintu Das, Tapan Kumar Roy (2014). Multi-objective geometric programming and its application in gravel box problem. Journal of global research in computer science volume 5. no.7, July 2014. http://www.jgrcs.info

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Solving a Multi-Objective Problem with a Streamlined Procedure of Goal Programming

 

Jsun Yui Wong

The computer program listed below seeks to solve the following problem with the streamlined procedure of goal programming in Hillier and Liebermann [38, p. 291]:

Minimize

5 * X(4) + 2 * 100000000 * X(7) + 4 * X(6) + 3 * 100000000 * X(9)

subject to

X(4) – X(5) + 12 * X(1) + 9 * X(2) + 15 * X(3) =125
X(6) – X(7) + 5 * X(1) + 3 * X(2) + 4 * X(3) =40
X(8) – X(9) + 5 * X(1) + 7 * X(2) + 8 * X(3) =55

X(1),…, X(9)>=0.

0 DEFDBL A-Z
1 REM DEFINT X, A
2 DIM N(99), A(45222), X(45012), D(111), P(111), PS(33), J44(45202), J(45211), AA(99), STIO(45303)
81 FOR JJJJ = -32000 TO 32000

85 RANDOMIZE JJJJ
87 M = -4E+250
121 FOR J44 = 1 TO 9

124 A(J44) = FIX(RND * 5)

125 NEXT J44

128 FOR I = 0 TO FIX(RND * 300000)

129 FOR KKQQ = 1 TO 9

130 X(KKQQ) = A(KKQQ)
131 NEXT KKQQ

135 FOR IPP = 1 TO FIX(1 + RND * 3.3)

143 j = 1 + FIX(RND * 9)

154 IF j < 3.5 THEN GOTO 156 ELSE GOTO 162

156 r = (1 – RND * 2) * A(j)
158 X(j) = A(j) + (RND ^ (RND * 15)) * r

161 GOTO 169

162 IF RND < .5 THEN X(j) = A(j) – FIX(1 + RND * 2.3) ELSE X(j) = A(j) + FIX(1 + RND * 2.3)
169 NEXT IPP

177 FOR J44 = 1 TO 9
178 IF X(J44) < 0 THEN 1670
179 NEXT J44
291 X(4) = 125 + X(5) – 12 * X(1) – 9 * X(2) – 15 * X(3)
293 X(6) = 40 + X(7) – 5 * X(1) – 3 * X(2) – 4 * X(3)
294 X(8) = 55 + X(9) – 5 * X(1) – 7 * X(2) – 8 * X(3)

1077 FOR J44 = 1 TO 9
1078 IF X(J44) < 0## THEN 1670
1079 NEXT J44

1297 P = -5 * X(4) – 2 * 100000000 * X(7) – 4 * X(6) – 3 * 100000000 * X(9)

1311 IF P <= M THEN 1670
1420 M = P

1444 FOR KLX = 1 TO 9

1455 A(KLX) = X(KLX)
1456 NEXT KLX

1557 GOTO 128
1670 NEXT I

1677 IF M < -43.752 THEN 1999
1931 PRINT A(1), A(2), A(3), A(4), A(5)
1933 PRINT A(6), A(7), A(8), A(9), M, JJJJ
1999 NEXT JJJJ

This BASIC computer program was run with QB64v1000-win [95]. The complete output of a single run through JJJJ= -31763 is shown below:

5.000025845890661 0 3.74996769263657
8.750174459763525 0
4.156675004196586D-13 0 1.292294541350714D-04
0 -43.7508722988193 -31933

4.999989737967745 0 3.750006413770127
8.750026937835148 0
2.565508076557421D-05 0 2.557953848736361D-13
0 -43.7502373094988 -31765

4.999996289277822          0          3.750002319201362
8.750009740645712          0
9.27680544648979D-06          0          0          0
-43.75008581045034          -31763

.
.
.
Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [95], the wall-clock time (not CPU time) for obtaining the output through JJJJ = -31763 was 95 seconds, total, including the time for “Creating .EXE file”. One can compare the computational results above with those in Hillier and Lieberman [38, p. 291].

Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

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