Research article Special Issues

Mathematical estimation for maximum flow of goods within a cross-dock to reduce inventory

  • Received: 08 July 2022 Revised: 28 August 2022 Accepted: 04 September 2022 Published: 19 September 2022
  • Supply chain management has recently renovated its strategy by implementing a cross-docking scheme. Cross-docking is a calculated logistics strategy where freight emptied from inbound vehicles is handled straightforwardly onto outbound vehicles, eliminating the intermediate storage process. The cross-docking approach thrives on the minimum storage time of goods in the inventory. Most of the cross-docks avail temporary storage docks where items can be stored for up to 24 hours before being packed up for transportation. The storage capacity of the cross-dock varies depending on the nature of ownership. In the rented cross-docks center, the temporary storage docks are considered of infinite capacity. This study believes that the temporary storage facilities owned by the cross-dock center are of finite capacity, which subsequently affects the waiting time of the goods. The flow rate of goods within the cross-docks is expected to be maximum to avoid long waiting for goods in the queue. This paper uses a series of max-flow algorithms, namely Ford Fulkerson, Edmond Karp, and Dinic's, to optimize the flow of goods between the inbound port and the outbound dock and present a logical explanation to reduce the waiting time of the trucks. A numerical example is analyzed to prove the efficacity of the algorithm in finding maximum flow. The result demonstrates that Dinic's algorithm performs better than the Ford Fulkerson and Edmond Karp algorithm at addressing the problem of maximum flow at the cross-dock. The algorithm effectively provided the best result regarding iteration and time complexity. In addition, it also suggested the bottleneck paths of the network in determining the maximum flow.

    Citation: Taniya Mukherjee, Isha Sangal, Biswajit Sarkar, Tamer M. Alkadash. Mathematical estimation for maximum flow of goods within a cross-dock to reduce inventory[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13710-13731. doi: 10.3934/mbe.2022639

    Related Papers:

  • Supply chain management has recently renovated its strategy by implementing a cross-docking scheme. Cross-docking is a calculated logistics strategy where freight emptied from inbound vehicles is handled straightforwardly onto outbound vehicles, eliminating the intermediate storage process. The cross-docking approach thrives on the minimum storage time of goods in the inventory. Most of the cross-docks avail temporary storage docks where items can be stored for up to 24 hours before being packed up for transportation. The storage capacity of the cross-dock varies depending on the nature of ownership. In the rented cross-docks center, the temporary storage docks are considered of infinite capacity. This study believes that the temporary storage facilities owned by the cross-dock center are of finite capacity, which subsequently affects the waiting time of the goods. The flow rate of goods within the cross-docks is expected to be maximum to avoid long waiting for goods in the queue. This paper uses a series of max-flow algorithms, namely Ford Fulkerson, Edmond Karp, and Dinic's, to optimize the flow of goods between the inbound port and the outbound dock and present a logical explanation to reduce the waiting time of the trucks. A numerical example is analyzed to prove the efficacity of the algorithm in finding maximum flow. The result demonstrates that Dinic's algorithm performs better than the Ford Fulkerson and Edmond Karp algorithm at addressing the problem of maximum flow at the cross-dock. The algorithm effectively provided the best result regarding iteration and time complexity. In addition, it also suggested the bottleneck paths of the network in determining the maximum flow.



    加载中


    [1] M. R. AlZgool, U. Ahmed, S. A. Shah, Q. AlMaamary, N. AlMahmoud, Examining the interplay of HR initiatives, knowledge management, technological capabilities and product innovation, J. Secur. Sustain. Issues, 10 (2020), 735–748. http://doi.org/10.9770/jssi.2020.10.2(29) doi: 10.9770/jssi.2020.10.2(29)
    [2] M. Tayyab, M. S. Habib, M. S. Jajja, B. Sarkar, Economic assessment of a serial production system with random imperfection and shortages: A step towards sustainability, Comput. Ind. Eng., 171 (2022), 108398. https://doi.org/10.1016/j.cie.2022.108398 doi: 10.1016/j.cie.2022.108398
    [3] A. S. Mahapatra, M. S. Mahapatra, B. Sarkar, S. K. Majumder, Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning, Expert Syst. Appl., 201 (2022), 117169. https://doi.org/10.1016/j.eswa.2022.117169 doi: 10.1016/j.eswa.2022.117169
    [4] S. Khalilpourazari, A. Mirzazadeh, G. W. Weber, S. H. R. Pasandideh, A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process, Optimization, 69 (2020), 63–90. https://doi.org/10.1080/02331934.2019.1630625 doi: 10.1080/02331934.2019.1630625
    [5] B. Sarkar, M. Ullah, M. Sarkar, Environmental and economic sustainability through innovative green products by remanufacturing, J. Clean. Prod., 332 (2022), 129813. https://doi.org/10.1016/j.jclepro.2021.129813 doi: 10.1016/j.jclepro.2021.129813
    [6] S. Kumar, M. Sigroha, K. Kumar, B. Sarkar, Manufacturing/remanufacturing based supply chain management under advertisements and carbon emission process, RAIRO- Oper. Res., 56 (2022), 831–851. https://doi.org/10.1051/ro/2021189 doi: 10.1051/ro/2021189
    [7] R. Lotfi, Y. Z. Mehrjerdi, M. S. Pishvaee, A. Sadeghieh, G. W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk, Numer. Algebra, Control. Optim., 11 (2021), 221–253. https://doi.org/10.3934/naco.2020023 doi: 10.3934/naco.2020023
    [8] T. Paksoy, T. Bektaş, E. Özceylan, Operational and environmental performance measures in a multi-product closed-loop supply chain, Transport Res E-Log, 47 (2011), 532–546. https://doi.org/10.1016/j.tre.2010.12.001 doi: 10.1016/j.tre.2010.12.001
    [9] A. Mondal, S. K. Roy, Multi-objective sustainable opened-and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation, Comput. Ind. Eng., 159 (2021), 107453. https://doi.org/10.1016/j.cie.2021.107453 doi: 10.1016/j.cie.2021.107453
    [10] O. Theophilus, M. A. Dulebenets, J. Pasha, O. F. Abioye, M. Kavoosi, Truck scheduling at cross-docking terminals: A follow-up state-of-the-art review, Sustainability, 11 (2019), 5245. https://doi.org/10.3390/su11195245 doi: 10.3390/su11195245
    [11] J. Van Belle, P. Valckenaers, D. Cattrysse, Cross-docking: State of the art, Omega, 40 (2012), 827–846. https://doi.org/10.1016/j.omega.2012.01.005 doi: 10.1016/j.omega.2012.01.005
    [12] S. C. Corp, Cross-docking Trend Report, Whitepaper Series, Saddle Creek Corp: Lakeland, FL, USA, 2011. Available from: 070111DCMwe.pdf (distributiongroup.com)
    [13] G. Stalk, P. Evans, L. E. Shulman, Competing on capabilities: The new rules of corporate strategy, Harv. Bus. Rev., 70 (1992), 57–69.
    [14] N. S. Ankem, Models for performance analysis of a cross-dock, Master Thesis, University Park, Pennsylvania: Pennsylvania State University, USA, 2017.
    [15] Y. Kuo, Optimizing truck sequencing and truck dock assignment in a cross docking system, Expert Syst. Appl., 40 (2013), 5532–5541. https://doi.org/10.1016/j.eswa.2013.04.019 doi: 10.1016/j.eswa.2013.04.019
    [16] J. J. Bartholdi Ⅲ, S. T. Hackman, Warehouse and Distribution Science Release 0.94, Supply Chain and Logistics Institute, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, 2011.
    [17] K. K. Yang, J. Balakrishnan, C. H. Cheng, An analysis of factors affecting cross docking operations, J. Bus. Logist., 31 (2011), 121–148. https://doi.org/10.1002/j.2158-1592.2010.tb00131.x doi: 10.1002/j.2158-1592.2010.tb00131.x
    [18] J. J. Bartholdi Ⅲ, K. R. Gue, Best shape of a cross-dock, Transp. Sci., 38 (2004), 235–244. https://doi.org/10.1287/trsc.1030.0077 doi: 10.1287/trsc.1030.0077
    [19] I. F. Vis, K. J. Roodbergen, Positioning of goods in a cross-docking environment, Comput. Ind. Eng., 54 (2008), 677–689. https://doi.org/10.1016/j.cie.2007.10.004 doi: 10.1016/j.cie.2007.10.004
    [20] J. J. Bartholdi Ⅲ, K. R. Gue, Reducing labor costs in an LTL crossdocking terminal, Oper. Res., 48 (2000), 823–832. https://doi.org/10.1287/opre.48.6.823.12397 doi: 10.1287/opre.48.6.823.12397
    [21] J. F. Wang, A. Regan, Real-time trailer scheduling for cross dock operations, Transp. J., 47 (2008), 5–20. https://doi.org/10.5325/transportationj.47.2.0005 doi: 10.5325/transportationj.47.2.0005
    [22] G. Tadumadze, N. Boysen, S. Emde, F. Weidinger, Integrated truck and workforce scheduling to accelerate the unloading of trucks, Eur. J. Oper. Res., 278 (2019), 343–362. https://doi.org/10.1016/j.ejor.2019.04.024 doi: 10.1016/j.ejor.2019.04.024
    [23] H. G. Resat, P. Berten, Z. Kilek, M. B. Kalay, Design and development of robust optimization model for sustainable cross-docking systems: A case study in electrical devices manufacturing company, In: Muthu, SS (eds) Sustainable Packaging. Environmental Footprints and Eco-design of Products and Processes. Springer, Singapore, (2021), 203–224. https://doi.org/10.1007/978-981-16-4609-6_8
    [24] E. E. Zachariadis, A. I. Nikolopoulou, E. G. Manousakis, P. P. Repoussis, C. D. Tarantilis, The vehicle routing problem with capacitated cross-docking, Expert Syst. Appl., 196 (2022), 116620. https://doi.org/10.1016/j.eswa.2022.116620 doi: 10.1016/j.eswa.2022.116620
    [25] M. Madani-Isfahani, R. Tavakkoli-Moghaddam, B. Naderi, Multiple cross-docks scheduling using two meta-heuristic algorithms, Comput. Ind. Eng., 74 (2014), 129–138. https://doi.org/10.1016/j.cie.2014.05.009 doi: 10.1016/j.cie.2014.05.009
    [26] S. B. Choi, B. K. Dey, S. J. Kim, B. Sarkar, Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time, RAIRO- Oper. Res., 56 (2022), 1623–1653. https://doi.org/10.1051/ro/2022026 doi: 10.1051/ro/2022026
    [27] B. Pal, A. Sarkar, B. Sarkar, Optimal decisions in a dual-channel competitive green supply chain management under promotional effort, Expert Syst. Appl., (2023), 118315. https://doi.org/10.1016/j.eswa.2022.118315 doi: 10.1016/j.eswa.2022.118315
    [28] B. Sarkar, S. Bhuniya, A sustainable flexible manufacturing–remanufacturing model with improved service and green investment under variable demand, Expert Syst. Appl., 202 (2022), 117154. https://doi.org/10.1016/j.eswa.2022.117154 doi: 10.1016/j.eswa.2022.117154
    [29] M. S. Habib, M. Omair, M. B. Ramzan, T. N. Chaudhary, M. Farooq, B. Sarkar, A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network, J. Clean. Prod., 366 (2022), 132752. https://doi.org/10.1016/j.jclepro.2022.132752 doi: 10.1016/j.jclepro.2022.132752
    [30] B. Sarkar, A. Debnath, A. S. Chiu, W. Ahmed, Circular economy-driven two-stage supply chain management for nullifying waste, J. Clean. Prod., 339 (2022), 130513. https://doi.org/10.1016/j.jclepro.2022.130513 doi: 10.1016/j.jclepro.2022.130513
    [31] A. Garai, B. Sarkar, Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel, J. Clean. Prod., 334 (2022), 129977. https://doi.org/10.1016/j.jclepro.2021.129977 doi: 10.1016/j.jclepro.2021.129977
    [32] T. Wu, J. Blackhurst, Modelling supply chain information and material perturbations, in Supply Chain Management and Knowledge Management (A. Dwivedi and T. Butcher eds), Palgrave Macmillan, London, (2009), 107–123. https://doi.org/10.1057/9780230234956_6
    [33] B. Ponte, J. Costas, J. Puche, R. Pino, D. de la Fuente, The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains, Transport Res. E-Log., 111 (2018), 165–185. https://doi.org/10.1016/j.tre.2018.01.014 doi: 10.1016/j.tre.2018.01.014
    [34] E. B. Tirkolaee, A. Goli, A. Faridnia, M. Soltani, G. W. Weber, Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms, J. Clean. Prod., 276 (2020), 122927. https://doi.org/10.1016/j.jclepro.2020.122927 doi: 10.1016/j.jclepro.2020.122927
    [35] J. J. Vogt, The Successful cross-dock based supply chain, J. Bus. Logist., 31 (2010), 99–119. https://doi.org/10.1002/j.2158-1592.2010.tb00130.x doi: 10.1002/j.2158-1592.2010.tb00130.x
    [36] O. Theophilus, M. A. Dulebenets, J. Pasha, Y. Y. Lau, A. M. Fathollahi-Fard, A. Mazaheri, Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations, Comput. Ind. Eng., 156 (2021), 107240. https://doi.org/10.1016/j.cie.2021.107240 doi: 10.1016/j.cie.2021.107240
    [37] M. R. Galbreth, J. A. Hill, S. Handley, An investigation of the value of cross-docking for supply chain management, J. Bus. Logist., 29 (2008), 225–239. https://doi.org/10.1002/j.2158-1592.2008.tb00076.x doi: 10.1002/j.2158-1592.2008.tb00076.x
    [38] M. Vanajakumari, H. Sun, A. Jones, C. Sriskandarajah, Supply chain planning: A case for Hybrid Cross-Docks, Omega, 108 (2022), 102585. https://doi.org/10.1016/j.omega.2021.102585 doi: 10.1016/j.omega.2021.102585
    [39] D. Mardanya, G. Maity, S. K. Roy, The multi-objective multi-item just-in-time transportation problem, Optimization, (2021), 1–32. https://doi.org/10.1080/02331934.2021.1963246 doi: 10.1080/02331934.2021.1963246
    [40] P. B. Castellucci, A. M. Costa, F. Toledo, Network scheduling problem with cross-docking and loading constraints, Comput Oper Res, 132 (2021), 105271. https://doi.org/10.1016/j.cor.2021.105271 doi: 10.1016/j.cor.2021.105271
    [41] B. K. Giri, S. K. Roy, Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem, Int. J. Mach. Learn. Cybern., (2022), 1–24. https://doi.org/10.1007/s13042-022-01582-y doi: 10.1007/s13042-022-01582-y
    [42] S. K. Das, M. Pervin, S. K. Roy, G. W. Weber, Multi-objective solid transportation-location problem with variable carbon emission in inventory management: a hybrid approach, Ann. Oper. Res., (2021), 1–27. https://doi.org/10.1007/s10479-020-03809-z doi: 10.1007/s10479-020-03809-z
    [43] A. M. Fathollahi-Fard, M. Ranjbar-Bourani, N. Cheikhrouhou, M. Hajiaghaei-Keshteli, Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system, Comput. Ind. Eng., 137 (2019), 106103, https://doi.org/10.1016/j.cie.2019.106103 doi: 10.1016/j.cie.2019.106103
    [44] M. A. Dulebenets, A diploid evolutionary algorithm for sustainable truck scheduling at a cross-docking facility, Sustainability, 10 (2018), 1333. https://doi.org/10.3390/su10051333 doi: 10.3390/su10051333
    [45] M. A. Dulebenets, A comprehensive evaluation of weak and strong mutation mechanisms in evolutionary algorithms for truck scheduling at cross-docking terminals, IEEE Access, 6 (2018), 65635–65650. https://doi.org/10.1109/ACCESS.2018.2874439 doi: 10.1109/ACCESS.2018.2874439
    [46] M. A. Dulebenets, An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal, Inf. Sci., 565 (2021), 390–421. https://doi.org/10.1016/j.ins.2021.02.039 doi: 10.1016/j.ins.2021.02.039
    [47] G. C. Issi, R. Linfati, J. W. Escobar, Mathematical optimization model for truck scheduling in a distribution center with a mixed service-mode dock area, J. Adv. Transp., 2020 (2020). https://doi.org/10.1155/2020/8813372 doi: 10.1155/2020/8813372
    [48] A. H. Goodarzi, R. Tavakkoli-Moghaddam, A. Amini, A new bi-objective vehicle routing-scheduling problem with cross-docking: mathematical model and algorithms., Comput. Ind. Eng., 149 (2020), 106832. https://doi.org/10.1016/j.cie.2020.106832 doi: 10.1016/j.cie.2020.106832
    [49] F. Heidari, S. H. Zegordi, R. Tavakkoli-Moghaddam, Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization, J. Intell. Manuf., 29 (2018), 1155–1170. https://doi.org/10.1007/s10845-015-1160-3 doi: 10.1007/s10845-015-1160-3
    [50] W. Wisittipanich, T. Irohara, P. Hengmeechai, Truck scheduling problems in the cross docking network, Int. J. Logist. Syst. Manag., 33 (2019), 420–439. https://doi.org/10.1504/IJLSM.2019.101164 doi: 10.1504/IJLSM.2019.101164
    [51] A. Shahmardan, M. S. Sajadieh, Truck scheduling in a multi-door cross-docking center with partial unloading—Reinforcement learning-based simulated annealing approaches, Comput. Ind. Eng., 139 (2020), 106134. https://doi.org/10.1016/j.cie.2019.106134 doi: 10.1016/j.cie.2019.106134
    [52] S. I. Sayed, I. Contreras, J. A. Diaz, D. E. Luna, Integrated cross-dock door assignment and truck scheduling with handling times, TOP, 28 (2020), 705–727. https://doi.org/10.1007/s11750-020-00556-z doi: 10.1007/s11750-020-00556-z
    [53] H. Khorshidian, M. A. Shirazi, S. M. F. Ghomi, An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross dock, J. Intell. Manuf., 30 (2019), 163–184. https://doi.org/10.1007/s10845-016-1229-7 doi: 10.1007/s10845-016-1229-7
    [54] A. Motaghedi-Larijani, Solving the number of cross-dock open doors optimization problem by combination of NSGA-Ⅱ and multi-objective simulated annealing, Appl. Soft Comput., 128 (2022), 109448. https://doi.org/10.1016/j.asoc.2022.109448 doi: 10.1016/j.asoc.2022.109448
    [55] B. Werners, T. Wülfing, Robust optimization of internal transports at a parcel sorting center operated by Deutsche Post World Net, Eur. J. Oper. Res., 201 (2010), 419–426. https://doi.org/10.1016/j.ejor.2009.02.035 doi: 10.1016/j.ejor.2009.02.035
    [56] F. Essghaier, H. Allaoui, G. Goncalves, Truck to door assignment in a shared cross-dock under uncertainty, Expert Syst. Appl., 182 (2021), 114889. https://doi.org/10.1016/j.eswa.2021.114889 doi: 10.1016/j.eswa.2021.114889
    [57] S. Gelareh, F. Glover, O. Guemri, S. Hanafi, P. Nduwayo, R. Todosijević, A comparative study of formulations for a cross-dock door assignment problem, Omega, 91 (2020), 102015. https://doi.org/10.1016/j.omega.2018.12.004 doi: 10.1016/j.omega.2018.12.004
    [58] M. T. Kyi, S. S. Maw, L. L. Naing, Mathematical estimation for maximum flow in electricity distribution network by Ford-Fulkerson iteration algorithm, Int. J. Sci. Res. Publ., 9 (2019), https://doi.org/10.29322/IJSRP.9.08.2019.p9229 doi: 10.29322/IJSRP.9.08.2019.p9229
    [59] M. T. Kyi, L. L. Naing, Application of Ford-Fulkerson algorithm to maximum flow in water distribution pipeline network, Int. J Sci. Res. Publ., 8 (2018), https://doi.org/10.29322/IJSRP.8.12.2018.p8441 doi: 10.29322/IJSRP.8.12.2018.p8441
    [60] A. Özmen, E. Kropat, G. W. Weber, Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty, Optimization, 66 (2017), 2135–2155. https://doi.org/10.1080/02331934.2016.1209672 doi: 10.1080/02331934.2016.1209672
    [61] A. Bellanger, S. Hanafi, C. Wilbaut, Three-stage hybrid-flowshop model for cross-docking, Comput Oper Res, 40 (2013), 1109–1121. https://doi.org/10.1016/j.cor.2012.11.009 doi: 10.1016/j.cor.2012.11.009
    [62] C. Daquin, H. Allaoui, G. Goncalves, T. Hsu, Variable neighborhood search based algorithms for crossdock truck assignment, RAIRO Oper. Res., 55 (2021), S2291–S2323. https://doi.org/10.1051/ro/2020087 doi: 10.1051/ro/2020087
    [63] L. R. J. Ford, D. R. Fulkerson, Flows in Networks, Princeton University Press, Princeton, NJ. 1962
    [64] J. Edmonds, R. M. Karp, Theoretical improvements in algorithmic efficiency for network flow problems, J. ACM, 19 (1972), 248–264. https://doi.org/10.1145/321694.321699 doi: 10.1145/321694.321699
    [65] C. Jain, D. Garg, Improved Edmond Karp algorithm for network flow problem, Int. J. Comput. Appl., 37 (2012), 48–53. https://doi.org/10.5120/4576-6624 doi: 10.5120/4576-6624
    [66] K. K. Mallick, A. R. Khan, M. M. Ahmed, M. S. Arefin, M. S. Uddin, Modified Edmonds-Karp algorithm to solve maximum flow problem, Open J. App. Sci., 6 (2016), 131–140. https://doi.org/10.4236/ojapps.2016.62014 doi: 10.4236/ojapps.2016.62014
    [67] Y. Peretz, Y. Fischler, A fast parallel max-flow algorithm, J. Parallel Distrib. Comput., 169 (2022), 226–241. https://doi.org/10.1016/j.jpdc.2022.07.003 doi: 10.1016/j.jpdc.2022.07.003
    [68] M. Bulut, E. Özcan, Optimization of electricity transmission by Ford–Fulkerson algorithm, Sustain. Energy, Grids Netw., 28 (2021), 100544. https://doi.org/10.1016/j.segan.2021.100544 doi: 10.1016/j.segan.2021.100544
    [69] M. S. Sabbagh, H. Ghafari, S. R. Mousavi, A new hybrid algorithm for the balanced transportation problem, Comput. Ind. Eng., 82 (2015), 115–126. https://doi.org/10.1016/j.cie.2015.01.018 doi: 10.1016/j.cie.2015.01.018
    [70] D. Goldfarb, Z. Jin, A new scaling algorithm for the minimum cost network flow problem, Oper. Res. Lett., 25 (1999), 205–211. https://doi.org/10.1016/S0167-6377(99)00047-4 doi: 10.1016/S0167-6377(99)00047-4
    [71] H. Bui, E. S. Jung, V. Vishwanath, A. Johnson, J. Leigh, M. E. Papka, Improving sparse data movement performance using multiple paths on the Blue Gene/Q supercomputer, Parallel Comput., 51 (2016), 3–16. https://doi.org/10.1016/j.parco.2015.09.002 doi: 10.1016/j.parco.2015.09.002
    [72] R. M. Kaplan, An improved algorithm for multi-way trading for exchange and barter, Electron. Commer. Res. Appl., 10 (2011), 67–74. https://doi.org/10.1016/j.elerap.2010.08.001 doi: 10.1016/j.elerap.2010.08.001
    [73] G. R. Waissi, Worst case behavior of the Dinic algorithm, Appl. Math. Lett., 4 (1991), 57–60. https://doi.org/10.1016/0893-9659(91)90145-L doi: 10.1016/0893-9659(91)90145-L
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2032) PDF downloads(135) Cited by(15)

Article outline

Figures and Tables

Figures(8)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog