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A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

  • *Corresponding author: Eren Ozceylan

    *Corresponding author: Eren Ozceylan
Abstract Full Text(HTML) Figure(5) / Table(9) Related Papers Cited by
  • Transport and logistics systems include a range of activities that deal with all sorts of decisions and operations from material handling to vehicle routing. One of the main challenges for transport and logistics processes is to deal with large-scale and complex problems. However, with increasingly diverse sets of operational real-world data becoming available, data-driven heuristic approaches are promising to pave the path for solving the problems in the field of transport and logistics. Thus, a comprehensive review is needed to observe the reflections of this path in literature. To bridge this gap, a total of 40 papers on the topic of "data-driven heuristic approaches to logistics and transportation problems" are determined. Before the categorization and content analysis; descriptive, bibliometric and social network analysis are carried out to identify the current state of the literature. All the papers are systemically reviewed based on different perspectives, namely data-driven methodology, heuristics, sub-problems and etc. Based on the review, suggestions for future research are likewise provided. Subsequently, machine learning and deep learning methods are considered to be among the most promising data-driven methodologies. The review may be useful for academicians, researchers, and practitioners for a better understanding of data-driven heuristic approaches to transportation and logistics problems.

    Mathematics Subject Classification: Primary: 00A64, 90-02; Secondary: 90B06, 90C59.

    Citation:

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  • Figure 1.  Flowchart of the Review Methodology

    Figure 2.  Paper numbers according to years (Early access papers are categorized in 2021)

    Figure 3.  Co-author network

    Figure 4.  Co-citation network

    Figure 5.  Co-word network

    Table 1.  Database Search Information

    Keywords Query results Included Duplicated Excluded
    Group A Group B Group C
    Data driven heuristic logistic 12 4 8
    transport 22 7 15
    ad. large neighb. logistic 1 1
    transport 1 1
    heuristic vehicle routing 13 5 2 6
    routing 26 6 7 13
    2-opt logistic 0
    transport 0
    hill climbing logistic 0
    transport 0
    lin karnighan logistic 0
    transport 0
    greedy logistic 5 1 3 1
    transport 6 1 4 1
    heuristic supply chain 11 2 2 7
    material handling 3 2 1
    storing 7 4 3
    order picking 2 1 1
    warehouse 0
    unitising 0
    packaging 9 1 8
    inventory 18 6 8 4
    TOTAL 136 40 56+6=62 34
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    Table 2.  Journals in which papers published

    Journal Name ISSN Number of Papers
    Production and Operations Management 1059-1478 4
    Operations Research 0030-364X 3
    International Journal of Production Economics 0925-5273 2
    Transportation Science 0041-1655 2
    Computers and Industrial Engineering 0360-8352 2
    Networks 0028-3045 1
    Manufacturing and Service Operations Management 1523-4614 1
    Journal of Heuristics 1381-1231 1
    Journal of Purchasing and Supply Management 1478-4092 1
    Journal of the Operational Research Society 0160-5682 1
    Ocean Engineering 0029-8018 1
    Transportation Research Part D 1361-9209 1
    Transportation Research Part E 1366-5545 1
    Transportation Research Record 0361-1981 1
    Pamukkale University Journal of Engineering Sciences 1300-7009 1
    Royal Society Open Science 2054-5703 1
    Transportation Research Part B 0191-2615 1
    Complexity 1076-2787 1
    Computers in Industry 0166-3615 1
    Decision Support Systems 0167-9236 1
    Complex & Intelligent Systems 2199-4536 1
    ACM Transactions on Human-Robot Interaction 2573-9522 1
    ASIA-Pacific Journal of Operational Research 0217-5959 1
    Automation in Construction 0926-5805 1
    Engineering Applications of Artificial Intelligence 0952-1976 1
    International Journal of Advanced Computer Science and Applications 2158-107X 1
    International Journal of Production Research 0020-7543 1
    Journal of Advanced Transportation 0197-6729 1
    IEEE Transactions on Knowledge and Data Engineering 1041-4347 1
    European Journal of Operational Research 0377-2217 1
    Geoinformatica 1384-6175 1
    IEEE Transactions on Intelligent Transportation Systems 1524-9050 1
    TOTAL 40
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    Table 3.  Research areas in which papers published

    Research Area Number of Papers
    Operations Research & Management Science 20
    Transportation Science & Technology 8
    Engineering, Civil 7
    Engineering, Manufacturing 7
    Management 7
    Transportation 6
    Engineering, Industrial 5
    Computer Science, Artificial Intelligence 5
    Computer Science, Information Systems 3
    Engineering, Electrical & Electronic 3
    Computer Science, Interdisciplinary Applications 3
    Multidisciplinary Sciences 2
    Computer Science, Theory & Methods 2
    Engineering, Multidisciplinary 2
    Economics 2
    Mathematics, Interdisciplinary Applications 1
    Oceanography 1
    Robotics 1
    Construction & Building Technology 1
    Computer Science, Hardware & Architecture 1
    Automation & Control Systems 1
    Engineering, Marine 1
    Geography, Physical 1
    Environmental Studies 1
    Engineering, Ocean 1
    TOTAL 40
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    Table 4.  Cited papers more than two times

    Cited Author Number of Papers
    Clarke G, 1964 5
    Toth P, 2014 4
    Dantzig G, 1959 4
    Solomon M, 1987 3
    Scarf H, 1958 3
    Ropke S, 2006 3
    Bao J, 2017 3
    Liyanage L, 2005 3
    Gallego G, 1993 3
    Pisinger D, 2007 3
    Godfrey G, 2001 3
    Levi R, 2007 3
    Breiman L, 2001 3
    Bertsimas D, 2006 3
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    Table 5.  Co-cited papers more than two times

    Citation 1 Citation 2 Co-citation
    Gallego G, 1993 Levi R, 2007 3
    Levi R, 2007 Scarf H, 1958 3
    Gallego G, 1993 Godfrey G, 2001 3
    Godfrey G, 2001 Scarf H, 1958 3
    Godfrey G, 2001 Levi R, 2007 3
    Gallego G, 1993 Scarf H, 1958 3
     | Show Table
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    Table 6.  Keywords more than three times

    Keyword Usage Count
    Optimization 10
    Chain 9
    Routing 9
    Supply 8
    Programming 7
    Vehicle 6
    Planning 6
    Prediction 5
    Algorithm 5
    Data 5
    Data-Driven 5
    Location 4
    Logistics 4
    Scheduling 4
    Heuristic 4
    Problem 4
    Time 4
    Search 4
    Model 4
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    Table 7.  Words in the title more than two times

    Words in the Title Usage Count
    Data-Driven 15
    Problem 8
    Routing 7
    Vehicle 5
    Approach 5
    Time 4
    Chain 4
    Optimization 4
    Urban 3
    Based 3
    Model 3
    System 3
    Control 3
    Bike 3
    Supply 3
    Windows 3
    Planning 3
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    Table 8.  Co-words in the title more than two times

    Word 1 Word 2 Times
    Data-Driven Routing 5
    Data-Driven Vehicle 4
    Data-Driven Problem 4
    Vehicle Routing 4
    Time Windows 3
    Routing Problem 3
    Bike Trajectories 3
    Supply Chain 3
     | Show Table
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    Table 9.  Problem type based content analysis results

    Problem type Frequency
    Vehicle routing problem (VRP) 6
    Newsvendor Problem 3
    Bike lane planning 2
    Freight distribution/Location routing problem (LRP) 2
    Material handling 2
    Travelling salesman problem (TSP) 2
    Credit term determination 1
    Eco-driving 1
    Facility layout/ Shelf space allocation 1
    Fastest route recommendation 1
    Flight timetabling and fleet assignment 1
    Inventory Shrinkage Problem/phantom stockout 1
    Labor-planning 1
    Last-Mile Problem/ the joint order assignment and routing 1
    Multi-Channel Switching 1
    Multimodal Logistics 1
    Network growth 1
    Optimal route selection 1
    Order picking/joint order batching and generalized assignment model 1
    Production–distribution problem 1
    Route planning 1
    Shelf space selection 1
    Storage Location Assignment Problem (SLAP) 1
    Supply chain coordination 1
    Supply chain production and distribution planning 1
    Train speed profile optimization 1
    Unit Loading Device handling sequence, workstation assignment, and vehicle schedule 1
    Urban transport/vehicle scheduling 1
    Vehicle sequence 1
     | Show Table
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  • [1] A. Akkas, Shelf space selection to control product expiration, Prod. Oper. Manag., 28 (2019), 2184-2201.  doi: 10.1111/poms.13034.
    [2] E. Avraham and T. Raviv, The data-driven time-dependent traveling salesperson problem, Transp. Res. Part B, 134 (2020), 25-40.  doi: 10.1016/j.trb.2020.01.005.
    [3] V. Batagelj, Networks/Pajek Program for Large Network Analysis, 2018. Available from: http://vlado.fmf.uni-lj.si/pub/networks/pajek/.
    [4] S. BelhaizaR. M'HallahG. Ben Brahim and G. Laporte, Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows, J. Heuristics, 25 (2019), 485-515.  doi: 10.1007/s10732-019-09412-1.
    [5] J. G. Carlsson and E. Delage, Robust partitioning for stochastic multivehicle routing, Oper. Res., 61 (2013), 727-744.  doi: 10.1287/opre.2013.1160.
    [6] L. Chen, Fixing phantom stockouts: Optimal data-driven shelf inspection policies, Prod. Oper. Manag., 30 (2021), 689-702.  doi: 10.1111/poms.13310.
    [7] Q. ChenB. G. De Soto and B. T. Adey, Supplier-contractor coordination approach to managing demand fluctuations of ready-mix concrete, Autom. Constr., 121 (2021), 103423.  doi: 10.1016/j.autcon.2020.103423.
    [8] H. Chu, W. Zhang, P. Bai and Y. Chen, Data-driven optimization for last-mile delivery, Complex. Intell. Syste., (2021). doi: 10.1007/s40747-021-00293-1.
    [9] H. H.-C. ChuangR. Oliva and O. Perdikaki, Traffic-based labor planning in retail stores, Prod. Oper. Manag., 25 (2016), 96-113.  doi: 10.1111/poms.12403.
    [10] I. CobanogluI. Gure and V. Bayram, Data driven storage location assignment problem considering order picking frequencies: A heuristic approach, Pamukkale University J. Eng. Sci., 27 (2021), 520-531.  doi: 10.5505/pajes.2021.34979.
    [11] A. DolguiF. Sgarbossa and M. Simonetto, Design and management of assembly systems 4.0: Systematic literature review and research agenda, Int. J. Prod. Res., 60 (2021), 184-210.  doi: 10.1080/00207543.2021.1990433.
    [12] Y. FengQ. Zhu and K.-H. Lai, Corporate social responsibility for supply chain management: A literature review and bibliometric analysis, J. Clean. Prod., 158 (2017), 296-307.  doi: 10.1016/j.jclepro.2017.05.018.
    [13] V. M. S. GandraH. CalikT. WautersT. A. M. ToffoloM. A. M. Carvalho and G. Vanden Berghe, The impact of loading restrictions on the two-echelon location routing problem, Comput. Ind. Eng., 160 (2021), 107609.  doi: 10.1016/j.cie.2021.107609.
    [14] C. Gkerekos and I. Lazakis, A novel, data-driven heuristic framework for vessel weather routing, Ocean Eng., 197 (2020), 106887.  doi: 10.1016/j.oceaneng.2019.106887.
    [15] F. GongD. S. Kung and T. Zeng, The impact of different contract structures on IT investment in logistics outsourcing, Int. J. Prod. Eco., 195 (2018), 158-167.  doi: 10.1016/j.ijpe.2017.10.009.
    [16] T. HeJ. BaoS. RuanR. LiY. LiH. He and Y. Zheng, Interactive bike lane planning using sharing bikes' trajectories, IEEE Trans. Knowl. Data. Eng., 32 (2020), 1529-1542.  doi: 10.1109/TKDE.2019.2907091.
    [17] K. HuangJ. WuX. YangZ. GaoF. Liu and Y. Zhu, Discrete train speed profile optimization for urban rail transit: A data-driven model and integrated algorithms based on machine learning, J. Adv. Transp., 2019 (2019), 7258986.  doi: 10.1155/2019/7258986.
    [18] Y. Kou and Z. Wan, A new data-driven robust optimization approach to multi-item newsboy problems, J. Ind. Manag. Optim., 19 (2023), 197-223.  doi: 10.3934/jimo.2021180.
    [19] F. LejarzaI. Pistikopoulos and M. Baldea, Scalable real-time solution strategy for supply chain management of fresh produce: A Mexico-to-United States cross border study, Int. J. Prod. Econ., 240 (2021), 108212.  doi: 10.1016/j.ijpe.2021.108212.
    [20] R. LeviG. Perakis and J. Uichancoi, The data-driven newsvendor problem: New bounds and insights, Oper. Res., 63 (2015), 1294-1306.  doi: 10.1287/opre.2015.1422.
    [21] H. LiL. MaiW. Zhang and X. Tian, Optimizing the credit term decisions in supply chain finance, J. Purch. Supply. Manag., 25 (2019), 146-156.  doi: 10.1016/j.pursup.2018.07.006.
    [22] H. Lin and C. Tang, Intelligent bus operation optimization by integrating cases and data driven based on business chain and enhanced quantum genetic algorithm, Trans. Intell. Transp. Syst., 23 (2022), 9869-9882.  doi: 10.1109/TITS.2021.3121289.
    [23] L. Liu and S. Mei, Visualising the GVC research: A co-occurrence network based bibliometric analysis, Scientometrics, 109 (2016), 953-977.  doi: 10.1007/s11192-016-2100-5.
    [24] S. Liu, Z. J. M. Shen and X. Ji, Urban bike lane planning with bike trajectories: Models, algorithms, and a real-world case study, to appear, Manuf. Serv. Oper. Manag.
    [25] M. MatusiakR. de Koster and J. Saarinen, Utilizing individual picker skills to improve order batching in a warehouse, Eur. J. Oper. Res., 263 (2017), 888-899.  doi: 10.1016/j.ejor.2017.05.002.
    [26] D. Merchan and M. Winkenbach, An empirical validation and data-driven extension of continuum approximation approaches for urban route distances, Networks, 73 (2019), 418-433.  doi: 10.1002/net.21874.
    [27] M. MiottiZ. A. NeedellS. RamakrishnanJ. Heywood and J. E. Trancik, Quantifying the impact of driving style changes on light-duty vehicle fuel consumption, Transp. Res. D. Transp. Environ., 98 (2021), 102918.  doi: 10.1016/j.trd.2021.102918.
    [28] S. M. MirhedayatianT. G. CrainicM. Guajardo and S. W. Wallace, A two-echelon location-routing problem with synchronisation, J. Oper. Res. Soc., 72 (2021), 145-160.  doi: 10.1080/01605682.2019.1650625.
    [29] W. de NooyA. Mrvar and  V. BatageljExplarotary Social Network Analysis with Pajek, 2 edition, Cambridge University Press, New York, 2011.  doi: 10.1017/CBO9780511996368.
    [30] L. G. N. OrozcoF. BattistonG. Iniguez and M. Szell, Data-driven strategies for optimal bicycle network growth, R. Soc. Open. Sci., 7 (2020), 201130.  doi: 10.1098/rsos.201130.
    [31] E. Ozgormus and A. E. Smith, A data-driven approach to grocery store block layout, Comput. Ind. Eng., 139 (2020), 105562.  doi: 10.1016/j.cie.2018.12.009.
    [32] S. K. PaulR. Sarker and D. Essam, Managing risk and disruption in production-inventory and supply chain systems: A review, J. Ind. Manag. Optim., 12 (2016), 1009-1029.  doi: 10.3934/jimo.2016.12.1009.
    [33] B. S. PerelmanA. W. Evans and K. E. Schaefer, Where do you think you're going? Characterizing spatial mental models from planned routes, ACM Trans. Hum., 9 (2020), 1-55.  doi: 10.1145/3385008.
    [34] H. N. PereraB. Fahimnia and T. Tokar, Inventory and ordering decisions: A systematic review on research driven through behavioral experiments, Int. J. Oper. Prod., 40 (2020), 997-1039.  doi: 10.1108/IJOPM-05-2019-0339.
    [35] O. Persson, Bib-Excel, 2018. Available from: http://homepage.univie.ac.at/juan.gorraiz/bibexcel/.
    [36] O. Persson, R. Danell and J. W. Schneider, How to use Bibexcel for various types of bibliometric analysis. In: Celebrating Scholarly Communication Studies: A Festschrift for Olle Persson at His 60th Birthday, International Society for Scientometrics and Informetrics, Sweden, 2009.
    [37] G. PesantM. GendreauJ. Y. Potvin and J. M. Rousseau, An exact constraint logic programming algorithm for the traveling salesman problem with time windows, Transp. Sci., 32 (1998), 12-29.  doi: 10.1287/trsc.32.1.12.
    [38] P. PinakpaniA. PolisettyG. B. N. RaoD. H. SunilB. M. KumarD. Deepthi and A. Sidhireddy, An algorithmic approach for maritime transportation, Int. J. Adv. Comput. Sci. Appl., 11 (2020), 764-775.  doi: 10.14569/IJACSA.2020.0110296.
    [39] S. PuniaS. P. Singh and J. K. Madaan, From predictive to prescriptive analytics: A data-driven multi-item newsvendor model, Decis. Support Syst., 136 (2020), 113340.  doi: 10.1016/j.dss.2020.113340.
    [40] V. RamamurthyJ. G. Shanthikumar and Z. J. M. Shen, Inventory policy with parametric demand: Operational statistics, linear correction, and regression, Prod. Oper. Manag., 21 (2012), 291-308.  doi: 10.1111/j.1937-5956.2011.01261.x.
    [41] S. RamanN. PatwaI. NiranjanU. RanjanK. Moorthy and A. Mehta, Impact of big data on supply chain management, Int. J. Logist. Res. Appl., 21 (2018), 579-596.  doi: 10.1080/13675567.2018.1459523.
    [42] A. I. Sivakumar and C. S. Chong, A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing, Comput. Ind., 45 (2001), 59-78.  doi: 10.1016/S0166-3615(01)00081-1.
    [43] J. Smoczek and J. Szpytko, Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system, Eng. Appl. Artif. Intell., 28 (2014), 190-200.  doi: 10.1016/j.engappai.2013.07.013.
    [44] C. Wang, C. Li, H. Huang, J. Qiu, J. Qu and L. Yin, ASNN-FRR: A traffic-aware neural network for fastest route recommendation, to appear, Geoinformatica, (2021). doi: 10.1007/s10707-021-00458-7.
    [45] Q. WangX. YangZ. Huang and Y. Yuan, Multi-vehicle trajectory design during cooperative adaptive cruise control platoon formation, Transp. Res. Rec., 2674 (2020), 30-41.  doi: 10.1177/0361198120913290.
    [46] K. J. WeiV. Vaze and A. Jacquillat, Airline timetable development and fleet assignment incorporating passenger choice, Transp. Sci., 54 (2020), 139-163.  doi: 10.1287/trsc.2019.0924.
    [47] C. WenP. HuangZ. LiJ. LessanL. FuC. Jiang and X. Xu, Train dispatching management with data- driven approaches: A comprehensive review and appraisal, IEEE Access, 7 (2019), 114547-114571.  doi: 10.1109/ACCESS.2019.2935106.
    [48] S. Winkelhaus and E. H. Grosse, Logistics 4.0: A systematic review towards a new logistics system, Int. J. Prod. Res., 58 (2020), 18-43.  doi: 10.1080/00207543.2019.1612964.
    [49] T. WuF. XiaoC. ZhangD. Zhang and Z. Liang, Regression and extrapolation guided optimization for production-distribution with ship-buy-exchange options, Transport. Res. E Log., 129 (2019), 15-37.  doi: 10.1016/j.tre.2019.06.012.
    [50] J. ZhangS. Onal and S. Das, Price differentiated channel switching in a fixed period fast fashion supply chain, Int. J. Prod. Econ., 193 (2017), 31-39.  doi: 10.1016/j.ijpe.2017.06.030.
    [51] Y. ZhangZ. ZhangA. Lim and M. Sim, Robust Data-driven vehicle routing with time windows, Oper. Res., 69 (2021), 469-485.  doi: 10.1287/opre.2020.2043.
    [52] Q. ZhaoC. Zhou and G. Pedrielli, Decision support system for data-driven driver-experience augmented vehicle routing problem, Asia Pac. J. Oper. Res., 37 (2020), 2050018.  doi: 10.1142/S0217595920500189.
    [53] S. ZhongY. GengW. Liu and W. Chen, A bibliometric review on natural resource accounting during 1995-2014, J. Clean. Prod., 139 (2016), 122-132.  doi: 10.1016/j.jclepro.2016.08.039.
    [54] C. ZhouA. StephenX. Cao and S. Wang, A data-driven business intelligence system for large-scale semi-automated logistics facilities, Int. J. Prod. Res., 59 (2021), 2250-2268.  doi: 10.1080/00207543.2020.1727048.
    [55] E. ZunicD. Donko and E. Buza, An adaptive data-driven approach to solve real-world vehicle routing problems in logistics, Complex., 2020 (2020), 7386701.  doi: 10.1155/2020/7386701.
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