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A mixed integer programming model for solving realtime trucktodoor assignment and scheduling problem at cross docking warehouse
Location and capacity design of congested intermediate facilities in networks
1.  School of Management, SouthCentral University for Nationalities, Wuhan, 430074, China 
2.  Department of Automation, School of Power and Mechanical Engineering, Wuhan University, Wuhan, 430072, China 
References:
[1] 
R. Aboolian, O. Berman and D. Krass, Profit maximizing distributed service system design with congestion and elastic demand, Transportation Science, 46 (2012), 247261. doi: 10.1287/trsc.1110.0392. 
[2] 
S.R. Agnihothri, S. Narasimhan and H. Pirkul, An assignment problem with queueing time cost, Naval Research Logistics, 37 (1990), 231244. doi: 10.1002/15206750(199004)37:2<231::AIDNAV3220370204>3.0.CO;2N. 
[3] 
M. Armony, E. Plambeck and S. Seshadri, Sensitivity of optimal capacity to customer impatience in an unobservable m/m/s queue (why you shouldn't shout at the dmv), Manufacturing & Service Operations Management, 11 (2009), 1932. doi: 10.1287/msom.1070.0194. 
[4] 
O. Berman and Z. Drezner, Location of congested capacitated facilities with distancesensitive demand, IIE Transactions, 38 (2006), 213221. doi: 10.1080/07408170500288190. 
[5] 
O. Berman and Z. Drezner, The multiple server location problem, Journal of the Operational Research Society, 58 (2006), 9199. doi: 10.1057/palgrave.jors.2602126. 
[6] 
M. L. Brandeau and S. S. Chiu, A center location problem with congestion, Annals of operations research, 40 (1992), 1732. doi: 10.1007/BF02060468. 
[7] 
M. L. F. Cheong, R. Bhatnagar and S. C. Graves, Logistics network design with supplier consolidation hubs and multiple shipment options, Journal of Industrial and Management Optimization, 3 (2007), 5169. doi: 10.3934/jimo.2007.3.51. 
[8] 
S. M. Choi, X. Huang and W. K. Ching, Minimizing equilibrium expected sojourn time via performancebased mixed threshold demand allocation in a multipleserver queueing environment, Journal of Industrial and Management Optimization, 8 (2012), 299323. doi: 10.3934/jimo.2012.8.299. 
[9] 
M. S. Daskin, C. R. Coullard and Z.J. M. Shen, A maximum expected covering location model: formulation, properties and heuristic solution, Transportation Science, 17 (1983), 4870. doi: 10.1287/trsc.17.1.48. 
[10] 
M. S. Daskin, C. R. Coullard and Z.J. M. Shen, An inventorylocation model: Formulation, solution algorithm and computational results, Annals of Operations Research, 110 (2002), 83106. doi: 10.1023/A:1020763400324. 
[11] 
M. S. Daskin, Network and Discrete Location: Models, Algorithms, and Applications, John Wiley & Sons, 2011. doi: 10.1002/9781118032343. 
[12] 
S. Elhedhli and H. Wu, A lagrangean heuristic for hubandspoke system design with capacity selection and congestion, INFORMS Journal on Computing, 22 (2010), 282296. doi: 10.1287/ijoc.1090.0335. 
[13] 
A. F. Gabor and J. Van Ommeren, An approximation algorithm for a facility location problem with stochastic demands and inventories, Operations research letters, 34 (2006), 257263. doi: 10.1016/j.orl.2005.04.009. 
[14] 
R. Hassin and M. Haviv, To Queue or not to Queue: Equilibrium Behavior in Queueing Systems, Kluwer Academic Publishers, 2002. doi: 10.1007/9781461503590. 
[15] 
D. Hu, C. Yang and J. Yang, Budget constrained flow interception location model for congested systems, Journal of Systems Engineering and Electronics, 20 (2009), 12551262. 
[16] 
S. Huang, R. Batta and R. Nagi, Distribution network design: Selection and sizing of congested connections, Naval Research Logistics, 52 (2005), 701712. doi: 10.1002/nav.20106. 
[17] 
V. Marianov and D. Serra, Probabilistic, maximal covering locationallocation models for congested systems, Journal of Regional Science, 38 (1998), 401424. 
[18] 
S. H. R. Pasandideh, S. T. A. Niaki and V. Hajipour, A multiobjective facility location model with batch arrivals: two parametertuned metaheuristic algorithms, Journal of Intelligent Manufacturing, 24 (2013), 331348. 
[19] 
S. H. A. Rahmati, A. Ahmadi, M. Sharifi and A. Chambari, A multiobjective model for Facility Locationallocation Problem with immobile servers within queuing framework, Computers and Industrial Engineering, 74 (2014), 110. doi: 10.1016/j.cie.2014.04.018. 
[20] 
H. Shavandi and H. Mahlooji, A fuzzy queuing location model with a genetic algorithm for congested systems, Applied mathematics and computation, 181 (2006), 440456. doi: 10.1016/j.amc.2005.12.058. 
[21] 
Q. Wang, R. Batta and C. M. Rump, Algorithms for a facility location problem with stochastic customer demand and immobile servers, Annals of Operations Research, 111 (2002), 1734. doi: 10.1023/A:1020961732667. 
[22] 
Q. Wang, R. Batta and C. M. Rump, Facility location models for immobile servers with stochastic demand, Naval Research Logistics, 51 (2004), 137152. doi: 10.1002/nav.10110. 
[23] 
L. Zhang and G. Rushton, Optimizing the size and locations of facilities in competitive multisite service systems, Computers & Operations Research, 35 (2008), 327338. doi: 10.1016/j.cor.2006.03.002. 
show all references
References:
[1] 
R. Aboolian, O. Berman and D. Krass, Profit maximizing distributed service system design with congestion and elastic demand, Transportation Science, 46 (2012), 247261. doi: 10.1287/trsc.1110.0392. 
[2] 
S.R. Agnihothri, S. Narasimhan and H. Pirkul, An assignment problem with queueing time cost, Naval Research Logistics, 37 (1990), 231244. doi: 10.1002/15206750(199004)37:2<231::AIDNAV3220370204>3.0.CO;2N. 
[3] 
M. Armony, E. Plambeck and S. Seshadri, Sensitivity of optimal capacity to customer impatience in an unobservable m/m/s queue (why you shouldn't shout at the dmv), Manufacturing & Service Operations Management, 11 (2009), 1932. doi: 10.1287/msom.1070.0194. 
[4] 
O. Berman and Z. Drezner, Location of congested capacitated facilities with distancesensitive demand, IIE Transactions, 38 (2006), 213221. doi: 10.1080/07408170500288190. 
[5] 
O. Berman and Z. Drezner, The multiple server location problem, Journal of the Operational Research Society, 58 (2006), 9199. doi: 10.1057/palgrave.jors.2602126. 
[6] 
M. L. Brandeau and S. S. Chiu, A center location problem with congestion, Annals of operations research, 40 (1992), 1732. doi: 10.1007/BF02060468. 
[7] 
M. L. F. Cheong, R. Bhatnagar and S. C. Graves, Logistics network design with supplier consolidation hubs and multiple shipment options, Journal of Industrial and Management Optimization, 3 (2007), 5169. doi: 10.3934/jimo.2007.3.51. 
[8] 
S. M. Choi, X. Huang and W. K. Ching, Minimizing equilibrium expected sojourn time via performancebased mixed threshold demand allocation in a multipleserver queueing environment, Journal of Industrial and Management Optimization, 8 (2012), 299323. doi: 10.3934/jimo.2012.8.299. 
[9] 
M. S. Daskin, C. R. Coullard and Z.J. M. Shen, A maximum expected covering location model: formulation, properties and heuristic solution, Transportation Science, 17 (1983), 4870. doi: 10.1287/trsc.17.1.48. 
[10] 
M. S. Daskin, C. R. Coullard and Z.J. M. Shen, An inventorylocation model: Formulation, solution algorithm and computational results, Annals of Operations Research, 110 (2002), 83106. doi: 10.1023/A:1020763400324. 
[11] 
M. S. Daskin, Network and Discrete Location: Models, Algorithms, and Applications, John Wiley & Sons, 2011. doi: 10.1002/9781118032343. 
[12] 
S. Elhedhli and H. Wu, A lagrangean heuristic for hubandspoke system design with capacity selection and congestion, INFORMS Journal on Computing, 22 (2010), 282296. doi: 10.1287/ijoc.1090.0335. 
[13] 
A. F. Gabor and J. Van Ommeren, An approximation algorithm for a facility location problem with stochastic demands and inventories, Operations research letters, 34 (2006), 257263. doi: 10.1016/j.orl.2005.04.009. 
[14] 
R. Hassin and M. Haviv, To Queue or not to Queue: Equilibrium Behavior in Queueing Systems, Kluwer Academic Publishers, 2002. doi: 10.1007/9781461503590. 
[15] 
D. Hu, C. Yang and J. Yang, Budget constrained flow interception location model for congested systems, Journal of Systems Engineering and Electronics, 20 (2009), 12551262. 
[16] 
S. Huang, R. Batta and R. Nagi, Distribution network design: Selection and sizing of congested connections, Naval Research Logistics, 52 (2005), 701712. doi: 10.1002/nav.20106. 
[17] 
V. Marianov and D. Serra, Probabilistic, maximal covering locationallocation models for congested systems, Journal of Regional Science, 38 (1998), 401424. 
[18] 
S. H. R. Pasandideh, S. T. A. Niaki and V. Hajipour, A multiobjective facility location model with batch arrivals: two parametertuned metaheuristic algorithms, Journal of Intelligent Manufacturing, 24 (2013), 331348. 
[19] 
S. H. A. Rahmati, A. Ahmadi, M. Sharifi and A. Chambari, A multiobjective model for Facility Locationallocation Problem with immobile servers within queuing framework, Computers and Industrial Engineering, 74 (2014), 110. doi: 10.1016/j.cie.2014.04.018. 
[20] 
H. Shavandi and H. Mahlooji, A fuzzy queuing location model with a genetic algorithm for congested systems, Applied mathematics and computation, 181 (2006), 440456. doi: 10.1016/j.amc.2005.12.058. 
[21] 
Q. Wang, R. Batta and C. M. Rump, Algorithms for a facility location problem with stochastic customer demand and immobile servers, Annals of Operations Research, 111 (2002), 1734. doi: 10.1023/A:1020961732667. 
[22] 
Q. Wang, R. Batta and C. M. Rump, Facility location models for immobile servers with stochastic demand, Naval Research Logistics, 51 (2004), 137152. doi: 10.1002/nav.10110. 
[23] 
L. Zhang and G. Rushton, Optimizing the size and locations of facilities in competitive multisite service systems, Computers & Operations Research, 35 (2008), 327338. doi: 10.1016/j.cor.2006.03.002. 
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