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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Front. Eng    2018, Vol. 5 Issue (4) : 451-465    https://doi.org/10.15302/J-FEM-2018038
RESEARCH ARTICLE
A simple multi-wave algorithm for the uncapacitated facility location problem
Fred GLOVER1(), Saïd HANAFI2, Oualid GUEMRI2, Igor CREVITS2
1. Leeds School of Business, University of Colorado, Boulder, CO 80309-0419, USA
2. LAMIH, CNRS UMR 8201, Université de Valenciennes, France
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Abstract

The multi-wave algorithm (Glover, 2016) integrates tabu search and strategic oscillation utilizing repeated waves (nested iterations) of constructive search or neighborhood search. We propose a simple multi-wave algorithm for solving the Uncapacitated Facility Location Problem (UFLP) to minimize the combined costs of selecting facilities to be opened and of assigning each customer to an opened facility in order to meet the customers’ demands. The objective is to minimize the overall cost including the costs of opening facilities and the costs of allocations. Our experimental tests on a standard set of benchmarks for this widely-studied class of problems show that our algorithm outperforms all previous methods.

Keywords discrete optimization      UFLP      multi-wave optimization      strategic oscillation      tabu search     
Corresponding Author(s): Fred GLOVER   
Just Accepted Date: 13 August 2018   Online First Date: 02 November 2018    Issue Date: 29 November 2018
 Cite this article:   
Fred GLOVER,Saïd HANAFI,Oualid GUEMRI, et al. A simple multi-wave algorithm for the uncapacitated facility location problem[J]. Front. Eng, 2018, 5(4): 451-465.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2018038
https://academic.hep.com.cn/fem/EN/Y2018/V5/I4/451
Fig.1  Vertical phase r =|AM R|< vk
Fig.2  Horizontal phase r =|AM R|< vk
MultiS-FI MultiS-BI MWA
Instance Avg best Nbr_it Avg best Nbr_it Avg Best CPU
500-10 798577.0 798577* 521.2 798577.0 798577* 514.8 798577.0* 798577* 8.22
500-100 327068.8 327001 1191.7 327017.9 326901 917.2 326798 326790* 20.49
500-1000 99175.8 99170 2767.3 99172.9 99169* 2632.9 99170.9 99169* 47.16
1000-10 1434250.7 1434154* 442.6 1434301.7 1434154* 430.4 1434154* 1434154* 38.75
1000-100 608008.3 607903 1407.0 608028.8 607903 1007.6 607883.9 607878* 134.62
1000-1000 220703 220668 2698.2 220725.3 220680 2322.2 220585.2 220560* 273.47
1500-10 2000839.6 2000801* 452.6 2000839.5 2000801* 433.8 2000828.8 2000801* 111.23
1500-100 867063.7 866829 1021.2 867451.8 867163 764.3 866490.1 866454* 314.24
1500-1000 335579.5 335491 1893.3 335571.8 335487 1297.9 335059.1 335002 702.70
2000-10 2558118.0 2558118* 533.1 2558126.2 2558118* 492.8 2558119.4 2558118* 277.50
2000-100 1123893.3 1123172 1121.6 1124056.8 1123188 765.4 1122854.5 1122805 660.59
2000-1000 438498.7 438295 1216.0 438544.6 438304 503.3 437813.6 437693 1201.25
2500-10 3100172.0 3099907* 550.0 3100439.2 3099907* 570.4 3100155.9 3099907* 531.12
2500-100 1348372.0 1347790 996.7 1348954.5 1348594 695.4 1347566.8 1347516* 1036.96
2500-1000 535364.9 535132 1581.9 535271.4 535091 1186.9 534593.6 534506 2737.16
3000-10 3570904.8 3570766 534.2 3570988.5 3570766* 562.6 3570766* 3570766* 876.77
3000-100 1604408.9 1604011 1006.8 1605408.1 1604748 709.7 1602512.2 1602345 1711.03
3000-1000 644839.1 644718 1133.1 644746.6 644574 909.4 643885.3 643797 3389.68
AVG 1170.47 928.72
Tab.1  MWA results versus multi-start results
?Instance ?TS ?MDAS ?GTS ?CLM ?HYB ?MTS ?US ?MWA
?250-sym-A ?257805.0 ?257804.0 ?257832.6 ?257895.2 ?257806.2 ?257804.0 ?257804.0 ?257804.0
?250-sym-B ?276035.2 ?276035.2 ?276185.2 ?276352.2 ?276035.2 ?276035.2 ?276035.2 ?276035.2
?250-sym-C ?333671.6 ?333671.6 ?333820.0 ?333671.6 ?333671.6 ?333671.6 ?333671.6 ?333671.6
?250-asym-A ?257917.8 ?257917.8 ?257978.4 ?258032.6 ?257923.4 ?257917.8 ?257917.8 ?257917.8
?250-asym-B ?276053.2 ?276053.2 ?276467.2 ?276184.2 ?276053.2 ?276053.2 ?276053.2 ?276053.2
?250-asym-C ?332897.2 ?333897.2 ?333237.6 ?333058.4 ?332897.2 ?333897.2 ?332897.2 ?332897.2
?500-sym-A ?511180.4 ?511181.2 ?511383.6 ?511487.2 ?511196.4 ?511188.8 ?511180.4 ?511180.4
?500-sym-B ?537912.0 ?537912.0 ?538480.4 ?538685.8 ?537912.0 ?537912.0 ?537912.0 ?537912.0
?500-sym-C ?621059.2 ?621059.2 ?621107.2 ?621172.8 ?621059.2 ?621059.2 ?621059.2 ?621059.2
?500-asym-A ?511140.0 ?511136.4 ?511251.6 ?511393.4 ?511145.0 ?511137.8 ?511137.4 ?511136.4
?500-asym-B ?537847.6 ?537847.6 ?538144.0 ?538421.0 ?537863.4 ?537847.6 ?537847.6 ?537847.6
?500-asym-C ?621463.8 ?621463.8 ?621811.8 ?621990.8 ?621463.8 ?621463.8 ?621463.8 ?621463.8
?750-sym-A ?763693.4 ?763684.8 ?763830.8 ?763978.0 ?763706.6 ?763708.8 ?763684.8 ?763684.8
?750-sym-B ?796571.8 ?796571.8 ?796919.0 ?797173.4 ?796632.2 ?796571.8 ?796576.8 ?796571.8
?750-sym-C ?900158.6 ?900158.6 ?901158.4 ?900785.2 ?900272.0 ?900158.6 ?900158.6 ?900158.6
?750-asym-A ?763717.0 ?763716.4 ?763836.6 ?764019.4 ?763731.2 ?763735.8 ?763712.4 ?763711.6*
?750-asym-B ?796374.4 ?796374.4 ?796859.0 ?796754.2 ?796396.8 ?796374.4 ?796374.4 ?796374.4
?750-asym-C ?900193.2 ?900193.2 ?900514.2 ?900349.8 ?900193.2 ?900193.2 ?900193.2 ?900193.2
?#BKS ?14/18 ?16/18 ?0/18 ?1/18 ?8/18 ?14/18 ?15/18 ?18/18
Tab.2  MWA results versus literature results on GHOSH benchmark instances
Instance TS MDAS CLM HYB MTS US MWA
All Find
250-sym-A 2.828 7.36 86.842 4.328 5.59 0.843 1.94 0.18
250-sym-B 5.628 5.88 34.634 7.774 7.73 0.618 0.75 0.1
250-sym-C 9.878 5.73 69.458 8.702 8.49 0.63 0.435 0.05
250-asym-A 2.618 7.32 86.506 4.636 5.62 0.993 1.84 0.78
250-asym-B 5.79 5.92 33.688 8.082 7.33 0.6 0.75 0.08
250-asym-C 9.196 5.67 89.99 7.776 8.51 0.586 0.39 0.04
500-sym-A 15.616 35.87 946.028 27.644 27.4 8.201 14.72 7.98
500-sym-B 31.432 26.72 294.656 34.196 32.7 4.619 4.29 0.95
500-sym-C 71.106 24.18 437.462 40.376 36.72 3.46 2.27 0.47
500-asym-A 13.76 35.97 921.208 20.232 27.43 7.736 11.81 7.28
500-asym-B 34.748 26.73 311.344 31.3 32.91 4.751 3.95 0.53
500-asym-C 72.064 26.60 388.21 47.79 36.61 3.601 2.31 0.45
750-sym-A 39.812 93.68 3650.662 49.214 67.63 25.903 34.41 20.31
750-sym-B 93.352 68.83 1583.17 92.886 77.67 14.601 11.37 4.1
750-sym-C 229.914 56.31 1194.534 113.64 88.17 10.888 7.117 1.31
750-asym-A 39.65 92.60 3658.588 59.13 67.91 26.914 40.58 20.49
750-asym-B 95.43 64.71 1606.778 73.322 78.06 14.542 11.17 5.03
750-asym-C 236.902 56.95 1325.812 112.994 87.17 10.827 7.12 1.76
AVG CPU 56.10 35.95 928.87 41.33 39.09 7.80 8.73 3.99
Tab.3  MWA CPU-times versus literature CPU-times on GHOSH benchmark instances
?Instance? ?Gurobi? ?ILS? ?MDAS? ?HYB? ?MWA?
?OPT? ?AVG? ?CPU? ?AVG? ?CPU? ?AVG? ?CPU? ?AVG? ?CPU (Find )? ?CPU(All)?
500-10? 798577? 799077.1? 46.89? 798577.0*? 28.0? 798577*? 33.2? 798577.0*? 1.488? 8.22?
500-100? 326790? 327007.6? 18.54? 326922.375? 25.6? 326805.4? 32.9? 326798? 12.402? 20.49?
500-1000? 99169? 99172.6? 14.57? 99196.0? 22.4? 99169*? 23.6? 99170.9? 18.13? 47.16?
1000-10? 1434154? 1435918.6? 54.94? 1434171.0? 130.7? 1434185.4? 173.9? 1434154*? 15.41? 38.75?
1000-100? 607878? 608202.3? 160.76? 607992.563? 106.5? 607880.4? 148.8? 607883.9? 73.21? 134.62?
1000-1000? 220560? 221260.9? 175.71? 220626.563? 84.4? 220560.9? 141.7? 220585.2? 159.42? 273.47?
1500-10? 2000801? 2002874.5? 178.66? 2000854.14? 331.1? 2001121.7? 347.8? 2000828.8? 70.89? 111.23?
1500-100? 866454? 867654.7? 385.46? 867149.69? 293.6? 866493.2? 378.7? 866490.1? 196.11? 314.24?
1500-1000? 334962? 338046.1? 381.83? 335400.813? 218.7? 334973.2? 387.2? 335059.1? 361.28? 702.70?
2000-10? 2558118? 2559611.3? 224.77? 2558121.5? 687.4? 2558120.8? 717.5? 2558119.4? 93.39? 277.50?
2000-100? 1122748? 1125471.9? 980.89? 1123936.5? 562.3? 1122861.9? 650.8? 1122854.5? 311.26? 660.59?
2000-1000? 437686? 443025.7? 997.64? 438263.0? 425.9? 437690.7? 760? 437813.6? 527.60? 1201.25?
2500-10? 3099907? 3107032.5? 710.01? 3100174.5? 1116.7? 3100224.7? 1419.5? 3100155.9? 259.50? 531.12?
2500-100? 1347516? 1350446.6? 1903.79? 1348713.25? 870.5? 1347577.6? 1128.2? 1347566.8? 711.44? 1036.96?
2500-1000? 534405? 540365.2? 1940.58? 535134.938? 675.3? 534426.6? 1309.4? 534593.6? 1723.54? 2737.16?
3000-10? 3570766? 3579295.7? 2605.87? 3570820.75? 1667.0? 3570818.8? 1621.1? 3570766*? 287.43? 876.77?
3000-100? 1602154? 1607502.6? 2987.58? 1605083.63? 1349.5? 1602530.9? 1977.6? 1602512.2? 999.13? 1711.03?
3000-1000? 643463? 652092.7? 2996.92? 644376.25? 1017.3? 643541.8? 2081.4? 643885.3? 2022.41? 3389.68?
Tab.4  MWA results versus literature results on MED test problems?
?Instance ?Gurobi ?ILS ?MWA
?OPT ?Best ?Best
?500-10 ?798577 ?798577* ?798577*
?500-100 ?326790 ?326894 ?326790*
?500-1000 ?99169 ?99169* ?99169*
?1000-10 ?1434154 ?1434154* ?1434154*
?1000-100 ?607878 ?607939 ?607878*
?1000-1000 ?220560 ?220959 ?220560*
?1500-10 ?2000801 ?2001920 ?2000801*
?1500-100 ?866454 ?866769 ?866454*
?1500-1000 ?334962 ?337452 ?335002
?2000-10 ?2558118 ?2558125 ?2558118*
?2000-100 ?1122748 ?1124572 ?1122805
?2000-1000 ?437686 ?442136 ?437693
?2500-10 ?3099907 ?3102719 ?3099907*
?2500-100 ?1347516 ?1349510 ?1347516*
?2500-1000 ?534405 ?539485 ?534506
?3000-10 ?3570766 ?3570930 ?3570766*
?3000-100 ?1602154 ?1606424 ?1602345
?3000-1000 ?643463 ?650777 ?643797
Tab.5  MWA best solutions versus ILS best solutions on MED test problems
?Instance ?TS ?LAG ?US ?HYB ?MWA
?Cost ?CPU ?Cost ?CPU ?Cost ?CPU ?Cost ?CPU ?Cost ?CPU
?All
?Find
?MO1 ?1305.95 ?0.68 ?1305.95 ?0.69 ?1305.95 ?0.07 ?1305.95 ?0.988 ?1305.95 ?0.24 ?0.08
?MO2 ?1432.26 ?0.76 ?1445.70 ?0.73 ?1432.26 ?0.07 ?1432.26 ?1.030 ?1432.26 ?0.27 ?0.10
?MO3 ?1516.77 ?0.80 ?1535.77 ?0.79 ?1516.77 ?0.07 ?1516.77 ?0.960 ?1516.77 ?0.30 ?0.12
?MO4 ?1442.24 ?0.77 ?1442.24 ?0.79 ?1442.24 ?0.06 ?1442.24 ?0.892 ?1442.24 ?0.27 ?0.09
?MO5 ?1408.77 ?0.80 ?1408.77 ?0.78 ?1408.77 ?0.06 ?1408.77 ?0.815 ?1408.77 ?0.34 ?0.09
?MP1 ?2686.48 ?4.97 ?2722.66 ?3.40 ?2686.48 ?0.24 ?2686.48 ?3.695 ?2686.48 ?0.72 ?0.33
?MP2 ?2904.86 ?5.00 ?2914.42 ?2.81 ?2904.86 ?0.28 ?2904.86 ?4.125 ?2904.86 ?0.78 ?0.47
?MP3 ?2623.71 ?4.95 ?2623.71 ?3.06 ?2623.71 ?0.30 ?2623.71 ?3.500 ?2623.71 ?0.61 ?0.33
?MP4 ?2938.75 ?5.82 ?2958.80 ?3.23 ?2938.75 ?0.32 ?2938.75 ?3.887 ?2938.75 ?0.70 ?0.34
?MP5 ?2932.33 ?5.49 ?2946.03 ?3.06 ?2932.33 ?0.25 ?2932.33 ?4.169 ?2932.33 ?0.75 ?0.42
?MQ1 ?4091.01 ?20.84 ?4091.01 ?3.96 ?4091.01 ?0.84 ?4091.01 ?8.919 ?4091.01 ?1.64 ?0.91
?MQ2 ?4028.33 ?17.82 ?4096.13 ?4.34 ?4028.33 ?0.79 ?4028.33 ?7.802 ?4028.33 ?1.65 ?1.15
?MQ3 ?4275.43 ?15.46 ?4373.08 ?3.78 ?4275.43 ?0.84 ?4275.43 ?9.508 ?4275.43 ?1.63 ?0.97
?MQ4 ?4235.15 ?16.24 ?4274.68 ?4.06 ?4235.15 ?0.74 ?4235.15 ?9.834 ?4235.15 ?1.84 ?0.92
?MQ5 ?4080.74 ?17.66 ?4138.50 ?3.87 ?4080.74 ?0.90 ?4080.74 ?10.813 ?4080.74 ?1.87 ?0.98
?MR1 ?2608.15 ?71.33 ?2634.71 ?11.67 ?2608.15 ?3.04 ?2608.15 ?27.221 ?2608.15 ?8.05 ?4.03
?MR2 ?2654.73 ?74.02 ?2704.66 ?11.37 ?2654.73 ?2.98 ?2654.73 ?27.646 ?2654.73 ?6.02 ?3.86
?MR3 ?2788.25 ?83.13 ?2874.46 ?11.62 ?2788.25 ?3.20 ?2788.25 ?26.417 ?2788.25 ?4.74 ?4.10
?MR4 ?2756.04 ?72.84 ?2774.38 ?12.54 ?2756.04 ?2.91 ?2756.04 ?27.595 ?2756.04 ?7.70 ?3.96
?MR5 ?2505.05 ?75.91 ?2526.32 ?11.57 ?2505.05 ?3.08 ?2505.05 ?26.989 ?2505.05 ?7.80 ?4.54
?MS1 ?5283.76 ?629.31 ?5434.11 ?50.19 ?5283.76 ?10.56 ?5283.76 ?113.395 ?5283.76 ?31.34 ?11.12
?MT1 ?10069.80 ?6173.04 ?10172.55 ?189.69 ?10069.80 ?120.58 ?10069.80 ?701.167 ?10069.80 ?180.33 ?78.47
?#BKS ?22/22 ?5/22 ?22/22 ?22/22 ?22/22
Tab.6  MWA results versus literature results on M* test problems
Instance Optimal TS LAG HYB MWA
All
Find
Cap71 932 615.75 0.05 0.07 0.034 0.060 0.020
Cap72 977 799.40 0.05 0.08 0.039 0.060 0.025
Cap73 1 010 641.45 0.05 0.08 0.053 0.045 0.045
Cap74 1 034 976.97 0.04 0.07 0.049 0.050 0.030
Cap101 796 648.44 0.07 0.07 0.055 0.095 0.055
Cap102 854 704.20 0.06 0.12 0.056 0.085 0.030
Cap103 893 782.11 0.05 0.10 0.072 0.080 0.050
Cap104 928 941.75 0.06 0.06 0.077 0.075 0.055
Cap131 793 439.56 0.11 0.20 0.105 0.145 0.055
Cap132 851 495.32 0.12 0.19 0.097 0.115 0.055
Cap133 893 076.71 0.13 0.28 0.131 0.130 0.040
Cap134 928 941.75 0.13 0.20 0.140 0.125 0.085
Capa 17 156 454.48 13.02 7.97 7.380 1.545 1.190
Capb 12 979 071.58 10.38 8.69 6.245 1.687 1.492
Capc 11 505 594.33 9.23 9.19 6.148 1.805 1.190
Avg - 2.24 1.82 1.38 0.41 0.29
Tab.7  MWA results versus literature results on ORLIB test problems
1 Ahn S, Cooper C, Cornuéjols G, Frieze A (1988). Probabilistic analysis of a relaxation for the k-median problem. Mathematics of Operations Research, 13(1): 1–31
https://doi.org/10.1287/moor.13.1.1
2 Akbaripour H, Masehian E, Roostaei A (2017). Landscape analysis and scatter search metaheuristic for solving the uncapacitated single allocation hub location problem. International Journal of Industrial and Systems Engineering, 26(4): 425–459
https://doi.org/10.1504/IJISE.2017.085207
3 Al-Sultan K S, Al-Fawzan M A (1999). A tabu search approach to the uncapacitated facility location problem. Annals of Operations Research, 86: 91–103
https://doi.org/10.1023/A:1018956213524
4 Albareda-Sambola M, Fernández E, Saldanha-da-Gama F (2017). Heuristic solutions to the facility location problem with general bernoulli demands. INFORMS Journal on Computing, 29(4): 737–753
https://doi.org/10.1287/ijoc.2017.0755
5 Amin S H, Zhang G (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6): 4165–4176
https://doi.org/10.1016/j.apm.2012.09.039
6 An H C, Svensson O (2017). Recent developments in approximation algorithms for facility location and clustering problems. In: Fukunaga T, Kawarabayashi K, eds. Combinatorial Optimization and Graph Algorithms, 1–19. Singapore: Springer
7 Ardjmand E, Park N, Weckman G, Amin-Naseri M R (2014). The discrete unconscious search and its application to uncapacitated facility location problem. Computers & Industrial Engineering, 73: 32–40
https://doi.org/10.1016/j.cie.2014.04.010
8 Arostegui M A Jr, Kadipasaoglu S N, Khumawala B M (2006). An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. International Journal of Production Economics, 103(2): 742–754
https://doi.org/10.1016/j.ijpe.2005.08.010
9 Atta S, Mahapatra P R S, Mukhopadhyay A (2018). Deterministic and randomized heuristic algorithms for uncapacitated facility location problem. Information and Decision Sciences, 205–216. Singapore: Springer
10 Barahona F, Chudak F (1999). Near-optimal Solutions to Large Scale Facility Location Problems. Technical Report RC21606, IBM, USA
11 Basu S, Sharma M, Ghosh P S (2015). Metaheuristic applications on discrete facility location problems: A survey. OPSEARCH, 52(3): 530–561
https://doi.org/10.1007/s12597-014-0190-5
12 Beasley J E (1990). OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11): 1069–1072
https://doi.org/10.1057/jors.1990.166
13 Beasley J E (1993). Lagrangean heuristics for location problems. European Journal of Operational Research, 65(3): 383–399
https://doi.org/10.1016/0377-2217(93)90118-7
14 Beltran C, Tadonki C, Vial J Ph (2006). Solving the p-median problem with a semi-Lagrangian relaxation. Computational Optimization and Applications, 35(2): 239–260
https://doi.org/10.1007/s10589-006-6513-6
15 Beltran-Royo C, Vial J P, Alonso-Ayuso A (2012). Semi-lagrangian relaxation applied to the uncapacitated facility location problem. Computational Optimization and Applications, 51(1): 387–409
https://doi.org/10.1007/s10589-010-9338-2
16 Blum C, Roli A (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3): 268–308
https://doi.org/10.1145/937503.937505
17 Cerrone C, Cerulli R, Golden B (2017). Carousel greedy: A generalized greedy algorithm with applications in optimization and statistics. Computers & Operations Research, 85: 97–112
18 Chalupa D, Nielsen P (2018). Instance scale, numerical properties and design of metaheuristics: A study for the facility location problem. arXiv preprint arXiv:1801.03419
19 Chen L, Olhager J, Tang O (2014). Manufacturing facility location and sustainability: A literature review and research agenda. International Journal of Production Economics, 149: 154–163
https://doi.org/10.1016/j.ijpe.2013.05.013
20 Cheung B S, Langevin A, Villeneuve B (2001). High performing evolutionary techniques for solving complex location problems in industrial system design. Journal of Intelligent Manufacturing, 12(5): 455–466
https://doi.org/10.1023/A:1012248319870
21 Cura T (2010). A parallel local search approach to solving the uncapacitated warehouse location problem. Computers & Industrial Engineering, 59(4): 1000–1009
https://doi.org/10.1016/j.cie.2010.09.012
22 De Armas J, Juan A A, Marques J M, Pedroso J P (2017). Solving the deterministic and stochastic uncapacitated facility location problem: From a heuristic to a simheuristic. Journal of the Operational Research Society, 68(10): 1161–1176
https://doi.org/10.1057/s41274-016-0155-6
23 De Corte A, Sörensen K (2015). An iterated local search algorithm for water distribution network design optimization. Networks, 67(3): 187–198
https://doi.org/10.1002/net.21673
24 Erlenkotter D (1978). A dual-based procedure for uncapacitated facility location: General solution procedures and computational experience. Operations Research, 26(6): 992–1009
https://doi.org/10.1287/opre.26.6.992
25 Galli L, Letchford A N, Miller S J (2018). New valid inequalities and facets for the simple plant location problem. European Journal of Operational Research, 269(3): 824–833
https://doi.org/10.1016/j.ejor.2018.03.009
26 Galvão R D, Raggi L A (1989). A method for solving to optimality uncapacitated location problems. Annals of Operations Research, 18(1): 225–244
https://doi.org/10.1007/BF02097805
27 Ghosh D (2003). Neighborhood search heuristics for the uncapacitated facility location problem. European Journal of Operational Research, 150(1): 150–162
https://doi.org/10.1016/S0377-2217(02)00504-0
28 Glover F (1989). Tabu search—Part I. ORSA Journal on Computing, 1(3): 190–206
https://doi.org/10.1287/ijoc.1.3.190
29 Glover F (1995). Tabu thresholding: Improved search by nonmonotonic trajectories. ORSA Journal on Computing, 7(4): 426–442
https://doi.org/10.1287/ijoc.7.4.426
30 Glover F (2000). Multi-start and strategic oscillation methods—principles to exploit adaptive memory. In: Laguna M, Gonzales-Velarde J L, eds. Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research, 1–24. Berlin: Kluwer Academic Publishers
31 Glover F (2016). The multi-wave algorithm for metaheuristic optimization. Journal of Heuristics, 22(3): 331–358
https://doi.org/10.1007/s10732-016-9312-y
32 Glover F, Laguna M (1997). Tabu Search. Boston: Kluwer Academic Publishers
33 Goldengorin B, Ghosh D, Sierksma G (2003). Branch and peg algorithms for the simple plant location problem. Computers & Operations Research, 30(7): 967–981
https://doi.org/10.1016/S0305-0548(02)00049-7
34 Grasas A, Juan A A, Faulin J, de Armas J, Ramalhinho H (2017). Biased randomization of heuristics using skewed probability distributions: A survey and some applications. Computers & Industrial Engineering, 110: 216–228
https://doi.org/10.1016/j.cie.2017.06.019
35 Greistorfer P, Rego C (2006). A simple filter-and-fan approach to the facility location problem. Computers & Operations Research, 33(9): 2590–2601
https://doi.org/10.1016/j.cor.2005.07.006
36 Hale T S, Moberg C R (2003). Location science research: A review. Annals of Operations Research, 123(1/4): 21–35
https://doi.org/10.1023/A:1026110926707
37 Han L, Xu D, Du D, Zhang D (2018). A local search approximation algorithm for the uniform capacitated k-facility location problem. Journal of Combinatorial Optimization, 35(2): 409–423
https://doi.org/10.1007/s10878-017-0179-0
38 Homberger J, Gehring H (2008). A two-level parallel genetic algorithm for the uncapacitated warehouse location problem. In: Proceedings of Hawaii international conference on system sciences
39 Jiang Y, Xu D, Du D, Wu C, Zhang D (2018). An approximation algorithm for soft capacitated k-facility location problem. Journal of Combinatorial Optimization, 35(2): 493–511
https://doi.org/10.1007/s10878-017-0192-3
40 Jörnsten K, Klose A (2016). An improved Lagrangian relaxation and dual ascent approach to facility location problems. Computational Management Science, 13(3): 317–348
https://doi.org/10.1007/s10287-015-0244-z
41 Khumawala B M (1972). An efficient branch and bound algorithm for the warehouse location problem. Management Science, 18(12): 718–731
https://doi.org/10.1287/mnsc.18.12.B718
42 Klose A, Drexl A (2005). Facility location models for distribution system design. European Journal of Operational Research, 162(1): 4–29
https://doi.org/10.1016/j.ejor.2003.10.031
43 Körkel M (1989). On the exact solution of large-scale simple plant location problems. European Journal of Operational Research, 39(2): 157–173
https://doi.org/10.1016/0377-2217(89)90189-6
44 Kratica J, Tošic D, Filipović V, Ljubić I (2001). Solving the simple plant location problem by genetic algorithm. Operations Research, 35(1): 127–142
https://doi.org/10.1051/ro:2001107
45 Melo M T, Nickel S, Saldanha-Da-Gama F (2009). Facility location and supply chain management–A review. European Journal of Operational Research, 196(2): 401–412
https://doi.org/10.1016/j.ejor.2008.05.007
46 Michel L, Van Hentenryck P (2004). A simple tabu search for warehouse location. European Journal of Operational Research, 157(3): 576–591
https://doi.org/10.1016/S0377-2217(03)00247-9
47 Ortiz-Astorquiza C, Contreras I, Laporte G (2017a). Formulations and approximation algorithms for multilevel uncapacitated facility location. INFORMS Journal on Computing, 29(4): 767–779
https://doi.org/10.1287/ijoc.2017.0757
48 Ortiz-Astorquiza C, Contrerasn I, Laporte G (2017b). Multi-level facility location problems. European Journal of Operational Research, 267(3): 791–805
https://doi.org/10.1016/j.ejor.2017.10.019
49 Pearce R H, Forbes M (2018). Disaggregated Benders decomposition and branch-and-cut for solving the budget-constrained dynamic uncapacitated facility location and network design problem. European Journal of Operational Research, 270(1): 78–88
https://doi.org/10.1016/j.ejor.2018.03.021
50 Posta M, Ferland J A, Michelon P (2014). An exact cooperative method for the uncapacitated facility location problem. Mathematical Programming Computation, 6(3): 199–231
https://doi.org/10.1007/s12532-014-0065-z
51 Resende M G, Werneck R F (2004). A hybrid heuristic for the p-median problem. Journal of Heuristics, 10(1): 59–88
https://doi.org/10.1023/B:HEUR.0000019986.96257.50
52 Resende M G C, Ribeiro C C (2003). Greedy randomized adaptive search procedures. In: Glover F, Kochenberger G, eds. Handbook of Metaheuristics, 219–249. Berlin: Kluwer Academic Publishers
53 Resende M G C, Werneck R F (2006). A hybrid multistart heuristic for the uncapacitated facility location problem. European Journal of Operational Research, 174(1): 54–68
https://doi.org/10.1016/j.ejor.2005.02.046
54 Resende M G C, Werneck R F (2007). A fast swap-based local search procedure for location problems. Annals of Operations Research, 150(1): 205–230
https://doi.org/10.1007/s10479-006-0154-0
55 ReVelle C S, Eiselt H A, ReVelle C S (2005). Location analysis: A synthesis and survey. European Journal of Operational Research, 165(1): 1–19
https://doi.org/10.1016/j.ejor.2003.11.032
56 Sahman M A, Altun A A, Dündar A O (2017). The binary differential search algorithm approach for solving uncapacitated facility location problems. Journal of Computational and Theoretical Nanoscience, 14(1): 670–684
https://doi.org/10.1166/jctn.2017.6258
57 Sun M (2005). A tabu search heuristic procedure for the uncapacitated facility location problem. In: Rego C, Alidaee B, eds. Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search, 191–211. Boston: Kluwer Academic Publishers
58 Sun M (2006). Solving the uncapacitated facility location problem using tabu search. Computers & Operations Research, 33(9): 2563–2589
https://doi.org/10.1016/j.cor.2005.07.014
59 Todosijević R, Urošević D, Mladenović N, Hanafi S (2017). A general variable neighborhood search for solving the uncapacitated r-allocation p-hub median problem. Optimization Letters, 11(6): 1109–1121
https://doi.org/10.1007/s11590-015-0867-6
60 Tohyama H, Ida K, Matsueda J (2011). A genetic algorithm for the uncapacitated facility location problem. Electronics and Communications in Japan, 94(5): 47–54
https://doi.org/10.1002/ecj.10180
61 Tseng L Y, Wu C S (2009a). Multiple trajectory search for uncapacitated facility location problems. In: Proceedings of the Second International Joint Conference on Computational Sciences and Optimization, Sanya, China. 2: 965–968
62 Tseng L Y, Wu C S (2009b). The multistart drop-add-swap heuristic for the uncapacitated facility location problem. In: Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization, Milan, Italy. 21–28
63 Tsuya K, Takaya M, Yamamura A (2017). Application of the firefly algorithm to the uncapacitated facility location problem. Journal of Intelligent & Fuzzy Systems, 32(4): 3201–3208
https://doi.org/10.3233/JIFS-169263
64 Wang D, Wu C H, Ip A, Wang D, Yan Y (2008). Parallel multipopulation particle swarm optimization algorithm for the uncapacitated facility location problem using OpenMP. In: Proceedings of 2008 IEEE Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence. 1214–1218
65 Wang Y, Lu Z, Glover F, Hao J K (2013). Probabilistic GRASP-tabu search algorithms for the UBQP problem. Computers & Operations Research, 40(12): 3100–3107
https://doi.org/10.1016/j.cor.2011.12.006
66 Xu J, Chiu S Y, Glover F (1997). Probabilistic tabu search for telecommunications network design. Combinatorial Optimization: Theory and Practice, 1(1): 69–94
67 Yigit V, Aydin M E, Turkbey O (2006). Solving large-scale uncapacitated facility location problems with evolutionary simulated annealing. International Journal of Production Research, 44(22): 4773–4791
https://doi.org/10.1080/00207540600621003
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