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  • Mathematical modeling and solution method of Milkran logistics in Saipa ‎Group's inbound supply chain under consideration of order time windows, ‎return cost of empty pallets and in-vehicle loading restrictions

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Manuscript ID : IMJ-2101-1545 (R3) Visit : 195 Page: 149 - 176

10.30495/imj.2022.688760

Article Type: Original Research

Mathematical modeling and solution method of Milkran logistics in Saipa ‎Group's inbound supply chain under consideration of order time windows, ‎return cost of empty pallets and in-vehicle loading restrictions

Subject Areas : Industrial Management

Masoum Najafian 1 , Ali Husseinzadeh Kashan 2 , Davood Mohammaditabar 3 , ALiakbar Akbari 4

1 - ‎Faculty of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.‎
2 - Assistant Professor in Industrial Engineering, College of Industrial engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Tehran, Iran.
4 - Faculty of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.‎

Received: 2021-01-30 Accepted : 2021-10-15 Published : 2022-02-01

Keywords: Direct Shipping Strategy, Grouping Evolution Strategy Algorithm, Milkran Logistics System, Mixed Integer Linear ‎Programming, Loading and Packing,

Abstract :

In the Milkran logistics system, vehicles are sent to collect orders from suppliers and deliver them to ‎assembly lines, according to pre-planned routes. In this way, the vehicle goes to the location of ‎several suppliers to pick up orders and then is sent to one or more destinations for delivery. In this ‎logistics system, cargoes are aggregated within the vehicle and through various nodes in the logistics ‎network. This paper introduces a mixed integer linear programming model for the Milkran logistics ‎problem that takes into account considerations such as three-dimensional loading of the order ‎pallets into vehicles, 50% higher cost for returning empty pallets, order timewindows, and ‎heterogeneous fleets. Given the nature of the problem, an algorithm based on grouping evolutionary ‎strategy is introduced that uses heuristic methods to ensure vehicles’ loading and routing feasibility. ‎The effectiveness of the introduced mathematical model and meta-heuristic algorithm is measured ‎using data collected from Saipa Automotive Group. The computational results show that Milkran ‎Logistics has the ability to reduce costs by 24.5% (on average), compared to the direct shipping ‎strategy pursued by Saipa.‎

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_||_

  1. Ghiani, G, Laporte, G, Musmanno, R. (2004). Introduction to logistics systems planning and control, Igarss 2014. John Wiley & Sons.
    2.
  2. Boysen, N, Emde, S, Hoeck, M, Kauderer, M. (2015). Part logistics in the automotive industry: Decision problems, literature review and research agenda. European Journal of Operational Research, 242, 107–120.
    3.
  3. Sadjadi, S. J, Jafari, M, Amini, T. (2009). A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study). International Journal of Advanced Manufacturing Technology, 44, 194–200.
    4.
  4. Kilic, H. S, Durmusoglu, M. B, Baskak, M. (2012). Classification and modeling for in-plant milk-run distribution systems. International Journal of Advanced Manufacturing Technology, 62, 1135–1146.
    5.
  5. Nemoto, T, Rothengatter, W. (2012). Efficient Green Logistics in Urban Areas: Milk Run Logistics in the Automotive Industry. In: Mackett, R, May, A, Kii M, Pan H. (eds), Sustainable Transport for Chinese Cities, 319–337.
    6.
  6. Gyulai, D, Pfeiffer, A, Sobottka, T, Váncza, J. (2013). Milkrun vehicle routing approach for shop-floor logistics. Procedia CIRP, 7, 127–132.
    7.
  7. Nemoto, T, Hayashi, K, Hashimoto, M. (2010). Milk-Run logistics by Japanese automobile manufacturers in Thailand. Procedia-Social and Behavioral Sciences, 2, 5980–5989.
    8.
  8. Hosseini, S. D, Shirazi, M. A, Fatemi Ghomi, S. M. T. (2014). Harmony search optimization algorithm for a novel transportation problem in a consolidation network. Engineering Optimization, 46, 1538–1552.
    9.
  9. Ranjbaran, F, Husseinzadeh Kashan, A, Kazemi, A. (2020). Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets. International Journal of Production Research, 58, 1741-1775.
    10.
  10. Jafari-Eskandari, M, Sadjadi, S. J, Jabalameli, M, Bozorgi-Amiri, A. (2009). A robust optimization approach for the Milk Run problem (An auto industry Supply Chain Case Study). International Conference On Computers And Industrial Engineering, CIE 2009, 1076–1081.
    11.
  11. Du, T, Wang, F. K, Lu, P. Y. (2007). A real-time vehicle-dispatching system for consolidating milk runs. Transportation Research Part E: Logistics and Transportation Review, 43, 565–577.
    12.
  12. Chuah, K. H, Yingling, J. C. (2005). Routing for a Just-in-Time Supply Pickup and Delivery System. Transportation Science, 39, 328–339.
    13.
  13. Parragh, S. N, Doerner, K. F, Hartl, R. F. (2008a). A survey on pickup and delivery problems: Part I: Transportation between customers and depot. Journal fur Betriebswirtschaft, 58, 21–51.
    14.
  14. Parragh, S. N, Doerner, K. F, Hartl, R. F. (2008b). A survey on pickup and delivery problems: Part II: Transportation between pickup and delivery locations. Journal fur Betriebswirtschaft, 58, 81–117.
    15.
  15. Berbeglia, G, Cordeau, J. F, Gribkovskaia, I, Laporte, G. (2007). Static pickup and delivery problems: a classification scheme and survey. Top, 15, 1–31.
    16.
  16. Berbeglia G, Cordeau J. F, Laporte, G. (2010). Dynamic pickup and delivery problems. European Journal of Operational Research, 202, 8–15.
    17.
  17. Ropke, S, Cordeau, J. F. (2009). Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows. Transportation Science, 43, 267–286.
    18.
  18. Baldacci, R, Bartolini, E, Mingozzi, A. (2011). An Exact Algorithm for the Pickup and Delivery Problem with Time Windows. Operations Research, 59, 414–426.
    19.
  19. Battarra, M, Cordeau, J. F, Iori, M. (2014). Pickup-and-Delivery Problems for Goods Transportation, In: Toth, P, Vigo, D. (eds), Vehicle Routing: Problems, Methods, and Applications, MOS-SIAM Series on Optimization, 161–191, Philadelphia.
    20.
  20. Cherkesly, M, Desaulniers, G, Irnich, S, Laporte, G. (2015). Branch-price-and-cut algorithms for the pickup and delivery problem with time windows and multiple stacks. European Journal of Operational Research, 250, 782-793.
    21.
  21. Veenstra, M, Cherkesly, M, Desaulniers, G, Laporte, G. (2017). The pickup and delivery problem with time windows and handling operations. Computers and Operations Research, 77, 127–140.
    22.
  22. Li, H, Lim, A. (2003). A metaheuristic for the pickup and delivery problem with time windows. International Journal on Artificial Intelligence Tools, 12, 173–186.
    23.
  23. Hosny, M. I, Mumford, C. L. (2010). The single vehicle PDPTW: Intelligent operators for heuristic and metaheuristic algorithms. Journal of Heuristics, 16, 417–439.
    24.
  24. Lim, A, Zhang, Z, Qin, H. (2017). Pickup and Delivery Service with Manpower Planning in Hong Kong Public Hospitals. Transportation Science, 51, 688–705.
    25.
  25. Abbasi-Pooya, A, Husseinzadeh Kashan, A. (2017). New mathematical models and a hybrid Grouping Evolution Strategy algorithm for optimal helicopter routing and crew pickup and delivery. Computers and Industrial Engineering, 112, 35–56.
    26.
  26. Nguyen, P. K, Crainic, T,, Toulouse, M. (2017). Multi-trip pickup and delivery problem with time windows and synchronization. Annals of Operations Research, 253, 899–934.
    27.
  27. Qu, Y, Bard, J. F. (2013). The heterogeneous pickup and delivery problem with configurable vehicle capacity. Transportation Research Part C: Emerging Technologies, 32, 1–20.
    28.
  28. Bettinelli, A. Ceselli, A, Righini, G. (2014). A branch-and-price algorithm for the multi-depot heterogeneous-fleet pickup and delivery problem with soft time windows. Mathematical Programming Computation, 6, 171–197.
    29.
  29. Avci, M, Topaloglu, S. (2016). A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 53, 160–171.
    30.
  30. Soleimani, H, Chaharlang, Y, Ghaderi, H. (2018). Collection and distribution of returned-remanufactured products in a vehicle routing problem with pickup and delivery considering sustainable and green criteria. Journal of Cleaner Production, 172, 960–970.
    31.
  31. Ceselli, A, Righini, G, Salani, M. (2009). A Column Generation Algorithm for a Rich Vehicle-Routing Problem. Transportation Science, 43, 56–69.
    32.
  32. Subramanian, A, Uchoa, E, Ochi, L. S. (2010). New lower bounds for the vehicle routing problem with simultaneous pickup and delivery. International Symposium on Experimental Algorithms, 276–287.
    33.
  33. Tasan, A. S, Gen, M. (2012). A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries. Computers and Industrial Engineering, 62, 755–761.
    34.
  34. Liu, R, Xie, X, Augusto, V, Rodriguez, C. (2013). Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. European Journal of Operational Research, 230, 475–486.
    35.
  35. Chen, Q, Li, K, Liu, Z. (2014). Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads. Transportation Research Part E: Logistics and Transportation Review, 69, 218–235.
    36.
  36. Muter, I, Cordeau, J, Laporte, G. (2014). A Branch-and-Price Algorithm for the Multidepot Vehicle Routing Problem with Interdepot Routes. Transportation Science, 48, 425–441.
    37.
  37. Polat, O, Kalayci, C. B, Kulak, O, Günther, H. O. (2015). A perturbation based variable neighborhood search heuristic for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery with Time Limit. European Journal of Operational Research, 242, 369-382.
    38.
  38. Avci, M, Topaloglu, S. (2015). An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries. Computers and Industrial Engineering, 83, 15–29.
    39.
  39. Mu, D, Wang, C, Zhao, F, Sutherland, J. (2016). Solving vehicle routing problem with simultaneous pickup and delivery using parallel simulated annealing algorithm. International Journal of Shipping and Transport Logistics, 8, 81-106.
    40.
  40. Detti, P, Papalini, F, de Lara, G. Z. M. (2017). A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare. Omega, 70, 1–14.
    41.
  41. Bula, G. A, Prodhon, C, Gonzalez, F. A, Afsar, M, Velasco N. (2017). Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation. Journal of Hazardous Materials, 324, 472–480.
    42.
  42. Zhou, L, Baldacci, R, Vigo, D, Wang, X. (2018). A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution. European Journal of Operational Research, 265, 765–778.
    43.
  43. Fernández Cuesta, E, Andersson, H, Fagerholt, K, Laporte, G. (2017). Vessel routing with pickups and deliveries: An application to the supply of offshore oil platforms. Computers and Operations Research, 79, 140–147.
    44.
  44. Dragomir, A. G, Nicola, D, Soriano, A, Gansterer, M. (2018). Multidepot pickup and delivery problems in multiple regions: a typology and integrated model. International Transactions in Operational Research, 25, 569–597.
    45.
  45. Niu, Y, Yang, Z, Chen, P, Xioao, J. (2018). Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost. Journal of Cleaner Production, 171, 962–971.
    46.
  46. Alinaghian, M, Shokouhi, N. (2018). Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search. Omega, 76, 85-99.
    47.
  47. Wei, L, Zhang, Z, Zhang, D, Leung, S. C. H. (2018). A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 265, 843–859.
    48.
  48. Belgin, O, Karaoglan, I, Altiparmak, F. (2018). Two-echelon vehicle routing problem with simultaneous pickup and delivery: Mathematical model and heuristic approach. Computers and Industrial Engineering, 115, 1–16.
    49.
  49. López-Sánchez, A. D, Hernández‐Díaz, A. G, Gortázar, F, Hinojosa, M.A. (2018). A multiobjective GRASP-VND algorithm to solve the waste collection problem. International Transactions in Operational Research, 25, 545–567.
    50.
  50. Scheffler, M, Hermann, C, Kasper, M. (2018). Splitting Procedure of Genetic Algorithm for Column Generation to Solve a Vehicle Routing Problem. In Fink A, Fügenschuh A, Geiger M. J. (eds), Operations Research Proceedings 2016, Springer International Publishing, 321–328.
    51.
  51. Mak-Hau, V, Moser, I. b, Aleti, A. (2018). An Exact Algorithm for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints. In Sarker, R. et al. (eds), Data and Decision Sciences in Action, Springer International Publishing, 91–101.
    52.
  52. Hojabri, H, Gendreau, M, Potvin, J. Y, Rousseau, L. M. (2018). Large neighborhood search with constraint programming for a vehicle routing problem with synchronization constraints. Computers & Operations Research, 92, 87–97.
    53.
  53. Rahbari, A, Nasiri, M. M, Werner, F, Musavi, M. M, Jolai F. (2019). The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling, 70, 605-625.
    54.
  54. Beraldi, P, De Maio, A, Laganà, D, Violi, A. (2019). A pick-up and delivery problem for logistics e-marketplace services. Optimization Letters, https://doi.org/10.1007/s11590-019-01472-3.
    55.
  55. Sitek, P, Wikarek, J, Rutczynska-Wdowiak, K, Bocewicz, G, Banaszak, Z. (2020). Optimization of capacitated vehicle routing problem with alternative delivery, pick-up and time windows: A modified hybrid approach. Neurocomputing, https://doi.org/10.1016/j.neucom.2020.02.126
    56.
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