October  2018, 14(4): 1367-1380. doi: 10.3934/jimo.2018011

Disaster relief routing in limited capacity road networks with heterogeneous flows

Department of Industrial and Systems Engineering, Yeditepe University, 26 Agustos Yerlesimi, Kayisdagi Cad., 34755 Atasehir, Istanbul, Turkey

* Corresponding author: Dilek Tuzun Aksu

Received  September 2016 Revised  October 2017 Published  October 2018 Early access  January 2018

In the aftermath of a major earthquake, delivery of essential services to survivors is of utmost importance and in urban areas it is conducted using road networks that are already stressed by road damages, other urban traffic and evacuation. Relief distribution efforts should be planned carefully in order to create minimal additional traffic congestion. We propose a dynamic relief distribution model where relief trucks share limited capacity road networks with counterflows resulting from car traffic. We develop a MIP model for this problem and solve it by decomposing the road network geographically and solving each subnetwork iteratively using the Relax and Fix method.

Citation: Linet Ozdamar, Dilek Tuzun Aksu, Elifcan Yasa, Biket Ergunes. Disaster relief routing in limited capacity road networks with heterogeneous flows. Journal of Industrial and Management Optimization, 2018, 14 (4) : 1367-1380. doi: 10.3934/jimo.2018011
References:
[1]

A. Afshar and A. Haghani, Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Planning Sciences, 46 (2012), 327-338.  doi: 10.1016/j.seps.2011.12.003.

[2]

V. Akbari and F. S. Salman, Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity, European Journal of Operational Research, 257 (2017), 625-640.  doi: 10.1016/j.ejor.2016.07.043.

[3]

M. Albareda-SambolaA. Alonso-AyusoL. F. EscuderoE. Fernandez and C. Pizarro, Fix-and-relax-coordination for a multi-period location-allocation problem under uncertainty, Computers & Operations Research, 40 (2013), 2878-2892.  doi: 10.1016/j.cor.2013.07.004.

[4]

A. M. Anaya-ArenasJ. Renaud and A. Ruiz, Relief distribution networks: A systematic review, Annals of Operations Research, 223 (2014), 53-79.  doi: 10.1007/s10479-014-1581-y.

[5]

J. AraozE. Fernandez and C. Zoltan, Privatized rural postman problems, Computers & Operations Research, 33 (2006), 3432-3449.  doi: 10.1016/j.cor.2005.02.013.

[6]

J. AraozE. Fernandez and O. Meza, Solving the prize-collecting rural postman problem, European Journal of Operational Research, 196 (2009), 886-896.  doi: 10.1016/j.ejor.2008.04.037.

[7]

C. ArchettiD. FeilletA. Hertz and M. G. Speranza, The undirected capacitated arc routing problem with profits, Computers & Operations Research, 37 (2010), 1860-1869.  doi: 10.1016/j.cor.2009.05.005.

[8]

B. BalcikB. M. Beamon and K. Smilowitz, Last mile distribution in humanitarian relief, Journal of Intelligent Transportation Systems, 12 (2008), 51-63.  doi: 10.1080/15472450802023329.

[9]

T. A. BaldoM. O. SantosB. Almada-Lobo and R. Morabito, An optimization approach for the lot sizing and scheduling problem in the brewery industry, Computers & Industrial Engineering, 72 (2014), 58-71.  doi: 10.1016/j.cie.2014.02.008.

[10]

P. BeraldiG. GhianiE. Guerriero and A. Grieco, Scenario-based planning for lot-sizing and scheduling with uncertain processing times, International Journal of Production Economics, 101 (2006), 140-149.  doi: 10.1016/j.ijpe.2005.05.018.

[11]

D. BerkouneJ. RenaudM. Rekik and A. Ruiz, Transportation in disaster response operations, Socio-Economic Planning Sciences, 46 (2012), 23-32.  doi: 10.1016/j.seps.2011.05.002.

[12]

V. CamposR. Bandeira and A. Bandeira, A method for evacuation route planning in disaster situations, Procedia-Social and Behavioral Sciences, 54 (2012), 503-512.  doi: 10.1016/j.sbspro.2012.09.768.

[13]

A. M. CaunhyeX. Nie and S. Pokharel, Optimization models in emergency logistics: A literature review, Socio-Economic Planning Sciences, 46 (2012), 4-13.  doi: 10.1016/j.seps.2011.04.004.

[14]

A. Y. ChenF. Pena-Mora and Y. Ouyang, A collaborative GIS framework to support equipment distribution for civil engineering disaster response operations, Automation in Construction, 20 (2011), 637-648.  doi: 10.1016/j.autcon.2010.12.007.

[15]

T. Cova and J. Johnson, A network flow model for lane-based evacuation routing, Transportation Research Part A: Policy and Practice, 37 (2003), 579-604.  doi: 10.1016/S0965-8564(03)00007-7.

[16]

J. Cui, S. An and M. Zhao, A generalized minimum cost flow model for multiple emergency flow routing Mathematical Problems in Engineering 2014 (2014), Article ID 832053, 12 pages. doi: 10.1155/2014/832053.

[17]

C. F. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B: Methodological, 28 (1994), 269-287.  doi: 10.1016/0191-2615(94)90002-7.

[18]

L. E. de la TorreI. S. Dolinskaya and K. R. Smilowitz, Disaster relief routing: Integrating research and practice, Socio-Economic Planning Sciences, 46 (2012), 88-97. 

[19]

L. EscuderoA. GarinM. Merino and G. Perez, BFC-MSMIP: An exact branch-and-fix coordination approach for solving multistage stochastic mixed 0--1 problems, TOP, 17 (2009), 96-122.  doi: 10.1007/s11750-009-0083-6.

[20]

D. FeilletP. Dejax and M. Gendreau, The profitable arc tour problem: Solution with a branch-and-price algorithm, Transportation Science, 39 (2005), 539-552.  doi: 10.1287/trsc.1040.0106.

[21]

C. M. Feng and C. C. Wen, A fuzzy bi-level and multi-objective model to control traffic flow into disaster area post earthquake, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 4253-4268. 

[22]

D. FerreiraR. Morabito and S. Rangel, Relax and fix heuristics to solve one-stage one machine lot-scheduling models for small-scale soft drink plants, Computers & Operations Research, 37 (2010), 684-691.  doi: 10.1016/j.cor.2009.06.007.

[23]

S. GuptaM. StarrR. Zanjirani Farahani and N. Matinrad, Disaster management from a POM perspective: Mapping a new domain, Production and Operations Management, 25 (2016), 1611-1637.  doi: 10.1111/poms.12591.

[24]

A. Haghani and S. C. Oh, Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations, Transportation Research Part A: Policy and Practice, 30 (1996), 231-250.  doi: 10.1016/0965-8564(95)00020-8.

[25]

F. LiberatoreM. T. OrtunoG. TiradoB. Vitoriano and M. P. Scaparra, A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics, Computers & Operations Research, 42 (2014), 3-13.  doi: 10.1016/j.cor.2012.03.019.

[26]

G. J. LimS. ZangenehM. R. Baharnemati and T. Assavapokee, A capacitated network flow optimization approach for short notice evacuation planning, European Journal of Operational Research, 223 (2012), 234-245.  doi: 10.1016/j.ejor.2012.06.004.

[27]

Y. H. LinR. BattaP. A. RogersonA. Blatt and M. Flanigan, A logistics model for emergency supply of critical items in the aftermath of a disaster, Socio-Economic Planning Sciences, 45 (2011), 132-145.  doi: 10.1016/j.seps.2011.04.003.

[28]

Y. LiuX. R. Lai and G. L. Chang, Two-level integrated optimization system for planning emergency evacuation, J. Of Transportation Engineering, 132 (2006), 800-807.  doi: 10.1061/(ASCE)0733-947X(2006)132:10(800).

[29]

E. Miller-Hooks and S. S. Patterson, On solving quickest time problems in time-dependent, dynamic networks, Journal of Mathematical Modelling and Algorithms, 3 (2004), 39-71.  doi: 10.1023/B:JMMA.0000026708.57419.6d.

[30]

M. NajafiK. Eshghi and W. Dullaert, A multi-objective robust optimization model for logistics planning in the earthquake response phase, Transportation Research Part E: Logistics and Transportation Review, 49 (2013), 217-249.  doi: 10.1016/j.tre.2012.09.001.

[31]

L. Ozdamar and O. Demir, A hierarchical clustering and routing procedure for large scale disaster relief logistics planning, Transportation Research Part E: Logistics and Transportation Review, 48 (2012), 591-602.  doi: 10.1016/j.tre.2011.11.003.

[32]

L. OzdamarE. Ekinci and B. Kucukyazici, Emergency logistics planning in natural disasters, Annals of Operations Research, 129 (2004), 217-245.  doi: 10.1023/B:ANOR.0000030690.27939.39.

[33]

L. Ozdamar and M. A. Ertem, Models, solutions and enabling technologies in humanitarian logistics, European Journal of Operational Research, 244 (2015), 55-65.  doi: 10.1016/j.ejor.2014.11.030.

[34]

B. VitorianoM. T. OrtunoG. Tirado and J. Montero, A multi-criteria optimization model for humanitarian aid distribution, Journal of Global Optimization, 51 (2011), 189-208.  doi: 10.1007/s10898-010-9603-z.

[35]

B. VitorianoT. Ortuno and G. Tirado, HADS, a goal programming-based humanitarian aid distribution system, Journal of Multi-Criteria Decision Analysis, 16 (2009), 55-64.  doi: 10.1002/mcda.439.

[36]

S. Yan and Y. L. Shih, Optimal scheduling of emergency roadway repair and subsequent relief distribution, Computers & Operations Research, 36 (2009), 2049-2065.  doi: 10.1016/j.cor.2008.07.002.

show all references

References:
[1]

A. Afshar and A. Haghani, Modeling integrated supply chain logistics in real-time large-scale disaster relief operations, Socio-Economic Planning Sciences, 46 (2012), 327-338.  doi: 10.1016/j.seps.2011.12.003.

[2]

V. Akbari and F. S. Salman, Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity, European Journal of Operational Research, 257 (2017), 625-640.  doi: 10.1016/j.ejor.2016.07.043.

[3]

M. Albareda-SambolaA. Alonso-AyusoL. F. EscuderoE. Fernandez and C. Pizarro, Fix-and-relax-coordination for a multi-period location-allocation problem under uncertainty, Computers & Operations Research, 40 (2013), 2878-2892.  doi: 10.1016/j.cor.2013.07.004.

[4]

A. M. Anaya-ArenasJ. Renaud and A. Ruiz, Relief distribution networks: A systematic review, Annals of Operations Research, 223 (2014), 53-79.  doi: 10.1007/s10479-014-1581-y.

[5]

J. AraozE. Fernandez and C. Zoltan, Privatized rural postman problems, Computers & Operations Research, 33 (2006), 3432-3449.  doi: 10.1016/j.cor.2005.02.013.

[6]

J. AraozE. Fernandez and O. Meza, Solving the prize-collecting rural postman problem, European Journal of Operational Research, 196 (2009), 886-896.  doi: 10.1016/j.ejor.2008.04.037.

[7]

C. ArchettiD. FeilletA. Hertz and M. G. Speranza, The undirected capacitated arc routing problem with profits, Computers & Operations Research, 37 (2010), 1860-1869.  doi: 10.1016/j.cor.2009.05.005.

[8]

B. BalcikB. M. Beamon and K. Smilowitz, Last mile distribution in humanitarian relief, Journal of Intelligent Transportation Systems, 12 (2008), 51-63.  doi: 10.1080/15472450802023329.

[9]

T. A. BaldoM. O. SantosB. Almada-Lobo and R. Morabito, An optimization approach for the lot sizing and scheduling problem in the brewery industry, Computers & Industrial Engineering, 72 (2014), 58-71.  doi: 10.1016/j.cie.2014.02.008.

[10]

P. BeraldiG. GhianiE. Guerriero and A. Grieco, Scenario-based planning for lot-sizing and scheduling with uncertain processing times, International Journal of Production Economics, 101 (2006), 140-149.  doi: 10.1016/j.ijpe.2005.05.018.

[11]

D. BerkouneJ. RenaudM. Rekik and A. Ruiz, Transportation in disaster response operations, Socio-Economic Planning Sciences, 46 (2012), 23-32.  doi: 10.1016/j.seps.2011.05.002.

[12]

V. CamposR. Bandeira and A. Bandeira, A method for evacuation route planning in disaster situations, Procedia-Social and Behavioral Sciences, 54 (2012), 503-512.  doi: 10.1016/j.sbspro.2012.09.768.

[13]

A. M. CaunhyeX. Nie and S. Pokharel, Optimization models in emergency logistics: A literature review, Socio-Economic Planning Sciences, 46 (2012), 4-13.  doi: 10.1016/j.seps.2011.04.004.

[14]

A. Y. ChenF. Pena-Mora and Y. Ouyang, A collaborative GIS framework to support equipment distribution for civil engineering disaster response operations, Automation in Construction, 20 (2011), 637-648.  doi: 10.1016/j.autcon.2010.12.007.

[15]

T. Cova and J. Johnson, A network flow model for lane-based evacuation routing, Transportation Research Part A: Policy and Practice, 37 (2003), 579-604.  doi: 10.1016/S0965-8564(03)00007-7.

[16]

J. Cui, S. An and M. Zhao, A generalized minimum cost flow model for multiple emergency flow routing Mathematical Problems in Engineering 2014 (2014), Article ID 832053, 12 pages. doi: 10.1155/2014/832053.

[17]

C. F. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B: Methodological, 28 (1994), 269-287.  doi: 10.1016/0191-2615(94)90002-7.

[18]

L. E. de la TorreI. S. Dolinskaya and K. R. Smilowitz, Disaster relief routing: Integrating research and practice, Socio-Economic Planning Sciences, 46 (2012), 88-97. 

[19]

L. EscuderoA. GarinM. Merino and G. Perez, BFC-MSMIP: An exact branch-and-fix coordination approach for solving multistage stochastic mixed 0--1 problems, TOP, 17 (2009), 96-122.  doi: 10.1007/s11750-009-0083-6.

[20]

D. FeilletP. Dejax and M. Gendreau, The profitable arc tour problem: Solution with a branch-and-price algorithm, Transportation Science, 39 (2005), 539-552.  doi: 10.1287/trsc.1040.0106.

[21]

C. M. Feng and C. C. Wen, A fuzzy bi-level and multi-objective model to control traffic flow into disaster area post earthquake, Journal of the Eastern Asia Society for Transportation Studies, 6 (2005), 4253-4268. 

[22]

D. FerreiraR. Morabito and S. Rangel, Relax and fix heuristics to solve one-stage one machine lot-scheduling models for small-scale soft drink plants, Computers & Operations Research, 37 (2010), 684-691.  doi: 10.1016/j.cor.2009.06.007.

[23]

S. GuptaM. StarrR. Zanjirani Farahani and N. Matinrad, Disaster management from a POM perspective: Mapping a new domain, Production and Operations Management, 25 (2016), 1611-1637.  doi: 10.1111/poms.12591.

[24]

A. Haghani and S. C. Oh, Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations, Transportation Research Part A: Policy and Practice, 30 (1996), 231-250.  doi: 10.1016/0965-8564(95)00020-8.

[25]

F. LiberatoreM. T. OrtunoG. TiradoB. Vitoriano and M. P. Scaparra, A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics, Computers & Operations Research, 42 (2014), 3-13.  doi: 10.1016/j.cor.2012.03.019.

[26]

G. J. LimS. ZangenehM. R. Baharnemati and T. Assavapokee, A capacitated network flow optimization approach for short notice evacuation planning, European Journal of Operational Research, 223 (2012), 234-245.  doi: 10.1016/j.ejor.2012.06.004.

[27]

Y. H. LinR. BattaP. A. RogersonA. Blatt and M. Flanigan, A logistics model for emergency supply of critical items in the aftermath of a disaster, Socio-Economic Planning Sciences, 45 (2011), 132-145.  doi: 10.1016/j.seps.2011.04.003.

[28]

Y. LiuX. R. Lai and G. L. Chang, Two-level integrated optimization system for planning emergency evacuation, J. Of Transportation Engineering, 132 (2006), 800-807.  doi: 10.1061/(ASCE)0733-947X(2006)132:10(800).

[29]

E. Miller-Hooks and S. S. Patterson, On solving quickest time problems in time-dependent, dynamic networks, Journal of Mathematical Modelling and Algorithms, 3 (2004), 39-71.  doi: 10.1023/B:JMMA.0000026708.57419.6d.

[30]

M. NajafiK. Eshghi and W. Dullaert, A multi-objective robust optimization model for logistics planning in the earthquake response phase, Transportation Research Part E: Logistics and Transportation Review, 49 (2013), 217-249.  doi: 10.1016/j.tre.2012.09.001.

[31]

L. Ozdamar and O. Demir, A hierarchical clustering and routing procedure for large scale disaster relief logistics planning, Transportation Research Part E: Logistics and Transportation Review, 48 (2012), 591-602.  doi: 10.1016/j.tre.2011.11.003.

[32]

L. OzdamarE. Ekinci and B. Kucukyazici, Emergency logistics planning in natural disasters, Annals of Operations Research, 129 (2004), 217-245.  doi: 10.1023/B:ANOR.0000030690.27939.39.

[33]

L. Ozdamar and M. A. Ertem, Models, solutions and enabling technologies in humanitarian logistics, European Journal of Operational Research, 244 (2015), 55-65.  doi: 10.1016/j.ejor.2014.11.030.

[34]

B. VitorianoM. T. OrtunoG. Tirado and J. Montero, A multi-criteria optimization model for humanitarian aid distribution, Journal of Global Optimization, 51 (2011), 189-208.  doi: 10.1007/s10898-010-9603-z.

[35]

B. VitorianoT. Ortuno and G. Tirado, HADS, a goal programming-based humanitarian aid distribution system, Journal of Multi-Criteria Decision Analysis, 16 (2009), 55-64.  doi: 10.1002/mcda.439.

[36]

S. Yan and Y. L. Shih, Optimal scheduling of emergency roadway repair and subsequent relief distribution, Computers & Operations Research, 36 (2009), 2049-2065.  doi: 10.1016/j.cor.2008.07.002.

Figure 1.  Road network of Fatih County in Istanbul
Table .   
Algorithm 1
1: Initialize $k=0$
2:While $(k < \frac{T}{\Delta t})$ do
3:     $k++$
4:     Define $y_{ijcq}$ and $v_{ijcq}$ for $q=(k-1)\Delta t+1, ..., k\Delta t$ as integers, let $y_{ijcq}$ and $v_{ijcq}$ be positive float variables for $q= k\Delta t+1, ..., T$
5:     Solve Model R
6:     Fix $y_{ijcq}$ and $v_{ijcq}$ for $q=(k-1)\Delta t+1, ..., k\Delta t$ at their optimal integer values
7: end while
8: Report final solution
9: End (Algorithm1)
Algorithm 1
1: Initialize $k=0$
2:While $(k < \frac{T}{\Delta t})$ do
3:     $k++$
4:     Define $y_{ijcq}$ and $v_{ijcq}$ for $q=(k-1)\Delta t+1, ..., k\Delta t$ as integers, let $y_{ijcq}$ and $v_{ijcq}$ be positive float variables for $q= k\Delta t+1, ..., T$
5:     Solve Model R
6:     Fix $y_{ijcq}$ and $v_{ijcq}$ for $q=(k-1)\Delta t+1, ..., k\Delta t$ at their optimal integer values
7: end while
8: Report final solution
9: End (Algorithm1)
Table 1.  Performance of the RF Algorithm as a function of $\Delta t$
Region Method Obj. CPU(secs.) Rel.Gap % Dev
1
(56 links)
No RF 1948.89 590.48 0.001 -
RF ($\Delta t$=2) 1928.38 680.01 -1.052
RF ($\Delta t$=4) 1931.90 415.80 -0.872
RF ($\Delta t$=8) 1932.05 374.31 -0.864
2
(54 links)
No RF 2055.46 3609.47 0.017 -
RF ($\Delta t$=2) 2037.86 734.53 -0.856
RF ($\Delta t$=4) 2052.20 489.48 -0.159
RF ($\Delta t$=8) 2053.23 744.67 -0.108
3
(65 links)
No RF 1659.32 3611.89 0.021 -
RF ($\Delta t$=2) 1667.39 999.43 0.486
RF ($\Delta t$=4) 1655.30 636.56 -0.242
RF ($\Delta t$=8) 1656.58 645.14 -0.165
4
(52 links)
No RF 3967.29 3609.02 0.003 -
RF ($\Delta t$=2) 3964.52 713.17 -0.070
RF ($\Delta t$=4) 3961.60 424.16 -0.143
RF ($\Delta t$=8) 3967.04 278.05 -0.006
5
(46 links)
No RF 2969.91 3610.08 0.009 -
RF ($\Delta t$=2) 2969.68 304.56 -0.008
RF ($\Delta t$=4) 2973.97 164.88 0.137
RF ($\Delta t$=8) 2972.40 140.04 0.084
6
(63 links)
No RF 1347.78 3612.22 0.018 -
RF ($\Delta t$=2) 1346.13 948.06 -0.122
RF ($\Delta t$=4) 1353.34 533.05 0.413
RF ($\Delta t$=8) 1353.42 472.09 0.418
7
(64 links)
No RF 1834.74 3608.97 0.011 -
RF ($\Delta t$=2) 1822.31 999.92 -0.677
RF ($\Delta t$=4) 1824.13 539.54 -0.578
RF ($\Delta t$=8) 1830.28 475.05 -0.243
Region Method Obj. CPU(secs.) Rel.Gap % Dev
1
(56 links)
No RF 1948.89 590.48 0.001 -
RF ($\Delta t$=2) 1928.38 680.01 -1.052
RF ($\Delta t$=4) 1931.90 415.80 -0.872
RF ($\Delta t$=8) 1932.05 374.31 -0.864
2
(54 links)
No RF 2055.46 3609.47 0.017 -
RF ($\Delta t$=2) 2037.86 734.53 -0.856
RF ($\Delta t$=4) 2052.20 489.48 -0.159
RF ($\Delta t$=8) 2053.23 744.67 -0.108
3
(65 links)
No RF 1659.32 3611.89 0.021 -
RF ($\Delta t$=2) 1667.39 999.43 0.486
RF ($\Delta t$=4) 1655.30 636.56 -0.242
RF ($\Delta t$=8) 1656.58 645.14 -0.165
4
(52 links)
No RF 3967.29 3609.02 0.003 -
RF ($\Delta t$=2) 3964.52 713.17 -0.070
RF ($\Delta t$=4) 3961.60 424.16 -0.143
RF ($\Delta t$=8) 3967.04 278.05 -0.006
5
(46 links)
No RF 2969.91 3610.08 0.009 -
RF ($\Delta t$=2) 2969.68 304.56 -0.008
RF ($\Delta t$=4) 2973.97 164.88 0.137
RF ($\Delta t$=8) 2972.40 140.04 0.084
6
(63 links)
No RF 1347.78 3612.22 0.018 -
RF ($\Delta t$=2) 1346.13 948.06 -0.122
RF ($\Delta t$=4) 1353.34 533.05 0.413
RF ($\Delta t$=8) 1353.42 472.09 0.418
7
(64 links)
No RF 1834.74 3608.97 0.011 -
RF ($\Delta t$=2) 1822.31 999.92 -0.677
RF ($\Delta t$=4) 1824.13 539.54 -0.578
RF ($\Delta t$=8) 1830.28 475.05 -0.243
Table 2.  Comparison of solution quality for various decomposition strategies
Subnetworks Obj. CPU Rel.Gap No. of Links
67 3527.10 7251.00 0.03 127
6+7 3182.52 7221.19
6+7 (RF) 3183.70 947.14
12 3998.60 7241.77 0.02 110
1+2 4004.35 4199.95
1+2 (RF) 3985.28 1118.97
45 8499.28 7232.73 0.01 98
4+5 6937.20 7219.10
4+5 (RF) 6939.44 418.10
37 3744.10 7243.97 0.10 129
3+7 3494.06 7220.86
3+7 (RF) 3486.86 1120.19
34 5621.70 7246.52 0.01 117
3+4 5626.61 7220.91
3+4 (RF) 5623.62 923.19
345 9050.53 10897.60 0.12 163
34+5 8591.61 10856.60
3+4+5 8596.52 10830.99
3+4+5 (RF) 8596.02 1063.23
456 9713.23 10905.90 0.08 161
45+6 9847.06 10844.95
4+5+6 8284.98 10831.32
4+5+6 (RF) 8292.86 890.18
567 5520.59 10914.90 0.24 173
5+67 6497.01 10861.08
5+6+7 6152.43 10831.27
5+6+7 (RF) 6156.10 1087.18
123 650.07 10939.80 1.00 175
12+3 5657.92 10853.66
1+2+3 5663.67 7811.84
1+2+3 (RF) 5641.86 1764.11
12345 1145.08 18450.00 1.00 273
12+345 13049.13 18139.37
123+45 9149.35 18172.53
1+2+3+4+5 12600.87 15030.94
1+2+3+4+5 (RF) 12581.30 2182.21
34567 1170.16 18560.90 1.00 290
34+567 11142.29 18161.42
37+456 13457.33 18149.87
3+4+5+6+7 11779.04 18052.18
3+4+5+6+7 (RF) 11779.72 2010.37
Subnetworks Obj. CPU Rel.Gap No. of Links
67 3527.10 7251.00 0.03 127
6+7 3182.52 7221.19
6+7 (RF) 3183.70 947.14
12 3998.60 7241.77 0.02 110
1+2 4004.35 4199.95
1+2 (RF) 3985.28 1118.97
45 8499.28 7232.73 0.01 98
4+5 6937.20 7219.10
4+5 (RF) 6939.44 418.10
37 3744.10 7243.97 0.10 129
3+7 3494.06 7220.86
3+7 (RF) 3486.86 1120.19
34 5621.70 7246.52 0.01 117
3+4 5626.61 7220.91
3+4 (RF) 5623.62 923.19
345 9050.53 10897.60 0.12 163
34+5 8591.61 10856.60
3+4+5 8596.52 10830.99
3+4+5 (RF) 8596.02 1063.23
456 9713.23 10905.90 0.08 161
45+6 9847.06 10844.95
4+5+6 8284.98 10831.32
4+5+6 (RF) 8292.86 890.18
567 5520.59 10914.90 0.24 173
5+67 6497.01 10861.08
5+6+7 6152.43 10831.27
5+6+7 (RF) 6156.10 1087.18
123 650.07 10939.80 1.00 175
12+3 5657.92 10853.66
1+2+3 5663.67 7811.84
1+2+3 (RF) 5641.86 1764.11
12345 1145.08 18450.00 1.00 273
12+345 13049.13 18139.37
123+45 9149.35 18172.53
1+2+3+4+5 12600.87 15030.94
1+2+3+4+5 (RF) 12581.30 2182.21
34567 1170.16 18560.90 1.00 290
34+567 11142.29 18161.42
37+456 13457.33 18149.87
3+4+5+6+7 11779.04 18052.18
3+4+5+6+7 (RF) 11779.72 2010.37
[1]

Louis Caccetta, Syarifah Z. Nordin. Mixed integer programming model for scheduling in unrelated parallel processor system with priority consideration. Numerical Algebra, Control and Optimization, 2014, 4 (2) : 115-132. doi: 10.3934/naco.2014.4.115

[2]

Elham Mardaneh, Ryan Loxton, Qun Lin, Phil Schmidli. A mixed-integer linear programming model for optimal vessel scheduling in offshore oil and gas operations. Journal of Industrial and Management Optimization, 2017, 13 (4) : 1601-1623. doi: 10.3934/jimo.2017009

[3]

Mahdi Karimi, Seyed Jafar Sadjadi. Optimization of a Multi-Item Inventory model for deteriorating items with capacity constraint using dynamic programming. Journal of Industrial and Management Optimization, 2022, 18 (2) : 1145-1160. doi: 10.3934/jimo.2021013

[4]

Wan Nor Ashikin Wan Ahmad Fatthi, Adibah Shuib, Rosma Mohd Dom. A mixed integer programming model for solving real-time truck-to-door assignment and scheduling problem at cross docking warehouse. Journal of Industrial and Management Optimization, 2016, 12 (2) : 431-447. doi: 10.3934/jimo.2016.12.431

[5]

Ye Tian, Cheng Lu. Nonconvex quadratic reformulations and solvable conditions for mixed integer quadratic programming problems. Journal of Industrial and Management Optimization, 2011, 7 (4) : 1027-1039. doi: 10.3934/jimo.2011.7.1027

[6]

Alessia Marigo, Benedetto Piccoli. A model for biological dynamic networks. Networks and Heterogeneous Media, 2011, 6 (4) : 647-663. doi: 10.3934/nhm.2011.6.647

[7]

Xinwei Ma, Danli Wang, Nan Zheng, Song Zhang. Aggregation and Adjustment mechanisms for disaster relief task allocation with uneven distribution. Journal of Industrial and Management Optimization, 2022  doi: 10.3934/jimo.2022015

[8]

Haiying Liu, Wenjie Bi, Kok Lay Teo, Naxing Liu. Dynamic optimal decision making for manufacturers with limited attention based on sparse dynamic programming. Journal of Industrial and Management Optimization, 2019, 15 (2) : 445-464. doi: 10.3934/jimo.2018050

[9]

Yufeng Zhou, Bin Zheng, Jiafu Su, Yufeng Li. The joint location-transportation model based on grey bi-level programming for early post-earthquake relief. Journal of Industrial and Management Optimization, 2022, 18 (1) : 45-73. doi: 10.3934/jimo.2020142

[10]

René Henrion, Christian Küchler, Werner Römisch. Discrepancy distances and scenario reduction in two-stage stochastic mixed-integer programming. Journal of Industrial and Management Optimization, 2008, 4 (2) : 363-384. doi: 10.3934/jimo.2008.4.363

[11]

Edward S. Canepa, Alexandre M. Bayen, Christian G. Claudel. Spoofing cyber attack detection in probe-based traffic monitoring systems using mixed integer linear programming. Networks and Heterogeneous Media, 2013, 8 (3) : 783-802. doi: 10.3934/nhm.2013.8.783

[12]

Mahdi Roozbeh, Saman Babaie–Kafaki, Zohre Aminifard. Two penalized mixed–integer nonlinear programming approaches to tackle multicollinearity and outliers effects in linear regression models. Journal of Industrial and Management Optimization, 2021, 17 (6) : 3475-3491. doi: 10.3934/jimo.2020128

[13]

Zhiguo Feng, Ka-Fai Cedric Yiu. Manifold relaxations for integer programming. Journal of Industrial and Management Optimization, 2014, 10 (2) : 557-566. doi: 10.3934/jimo.2014.10.557

[14]

Meihong Qiao, Anping Liu, Qing Tang. The dynamics of an HBV epidemic model on complex heterogeneous networks. Discrete and Continuous Dynamical Systems - B, 2015, 20 (5) : 1393-1404. doi: 10.3934/dcdsb.2015.20.1393

[15]

Jinxian Li, Ning Ren, Zhen Jin. An SICR rumor spreading model in heterogeneous networks. Discrete and Continuous Dynamical Systems - B, 2020, 25 (4) : 1497-1515. doi: 10.3934/dcdsb.2019237

[16]

Huai-Che Hong, Bertrand M. T. Lin. A note on network repair crew scheduling and routing for emergency relief distribution problem. Journal of Industrial and Management Optimization, 2019, 15 (4) : 1729-1731. doi: 10.3934/jimo.2018119

[17]

Fanwen Meng, Kiok Liang Teow, Kelvin Wee Sheng Teo, Chee Kheong Ooi, Seow Yian Tay. Predicting 72-hour reattendance in emergency departments using discriminant analysis via mixed integer programming with electronic medical records. Journal of Industrial and Management Optimization, 2019, 15 (2) : 947-962. doi: 10.3934/jimo.2018079

[18]

Ömer Arslan, Selçuk Kürşat İşleyen. A model and two heuristic methods for The Multi-Product Inventory-Location-Routing Problem with heterogeneous fleet. Journal of Industrial and Management Optimization, 2022, 18 (2) : 897-932. doi: 10.3934/jimo.2021002

[19]

Mahmoud Ameri, Armin Jarrahi. An executive model for network-level pavement maintenance and rehabilitation planning based on linear integer programming. Journal of Industrial and Management Optimization, 2020, 16 (2) : 795-811. doi: 10.3934/jimo.2018179

[20]

Yongjian Yang, Zhiyou Wu, Fusheng Bai. A filled function method for constrained nonlinear integer programming. Journal of Industrial and Management Optimization, 2008, 4 (2) : 353-362. doi: 10.3934/jimo.2008.4.353

2020 Impact Factor: 1.801

Metrics

  • PDF downloads (631)
  • HTML views (1733)
  • Cited by (2)

[Back to Top]