Skip to main content

Advertisement

SpringerLink
Log in

Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This article formulates an inventory routing problem in which backorders are allowed, each vehicle is used at most once, a central depot distributes a single product to a set of customers with an incremental delivery, each customer is served at most once over a finite planning period and the objective is minimizing transportation and inventory costs. Since the proposed model is an Np-Hard problem, for solving large scale problems two meta-heuristics, discrete invasive weed optimization and Genetic algorithm are presented. Tuning the parameters of the algorithms are performed by regression approach. In this approach, equation of fitness is found in terms of the parameters and the best value of the parameters is found in a way that the equation is minimized. Performance of the algorithms for solving the IRP is compared with statistical and multi-attribute decision making approach in terms of computational time and quality of solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Abbreviations

VRP:

Vehicle routing problem

IRP:

Inventory routing problem

LRP:

Location routing problem

LIP:

Location inventory problem

LIRP:

Location inventory routing problem

IRPTW:

Inventory routing problem with time windows

VMI:

Vendor managed inventory

GA:

Genetic algorithm

IWO:

Invasive weed optimization

DIWO:

Discrete invasive weed optimization

MADM:

Multi-attribute decision making

References

  • Abdelmaguid, T., & Dessouky, M. (2006). A genetic algorithm approach to the integrated inventory-distribution problem. International Journal of Production Research, 44(21), 4445–4464.

    Article  Google Scholar 

  • Abdelmaguid, T., Dessouky, M., & Ordonez, F. (2009). Heuristic approaches for the inventory-routing problem with backlogging. Computers and Industrial Engineering, 56(4), 1519–1534.

    Article  Google Scholar 

  • Agra, A., Christiansen, M., Delgado, A., & Hvattum, L. M. (2015). A maritime inventory routing problem with stochastic sailing and port times. Computers and Operations Research, 61, 18–30.

    Article  Google Scholar 

  • Alaei, S., & Setak, M. (2015). Multi objective coordination of a supply chain with routing and service level consideration. International Journal of Production Economics, 167, 271–281.

    Article  Google Scholar 

  • Al-Khayyal, F., & Hwang, S. (2007). Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, part I: Applications and model. European Journal of Operational Research, 176(1), 106–130.

    Article  Google Scholar 

  • Alumur, S., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190, 1–21.

    Article  Google Scholar 

  • Andersson, H., Hoff, A., Christiansen, M., Hasle, G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Combined inventory management and routing. Computers and Operations Research, 37(9), 1515–1536.

    Article  Google Scholar 

  • Archetti, C., Bertazzi, L., Hertz, A., & Speranza, M. (2010). A hybrid heuristic for an inventory routing problem. Informs Journal on Computing, 24(1), 101–116.

    Article  Google Scholar 

  • Archetti, C., Bertazzi, L., Laporte, G., & Speranza, M. (2007). A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transportation Science, 41(3), 382–391.

    Article  Google Scholar 

  • Azadeh, A., Elahi, S., Hosseinabadi Farahani, M., & Nasirian, B. (2017). A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Computers and Industrial Engineering, 104, 124–133.

    Article  Google Scholar 

  • Baños, R., Ortega, J., Gilb, C., Toro, Fd, & Montoya, M. G. (2016). Analysis of OpenMP and MPI implementations of meta-heuristics for vehicle routing problems. Applied Soft Computing, 43, 262–275.

    Article  Google Scholar 

  • Bell, W., Dalberto, L., Fisher, M., Greenfield, A., Jaikumar, R., & Kedia, P. (1983). Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer. Interfaces, 13(6), 4–23.

    Article  Google Scholar 

  • Bertazzi, L., Paletta, G., & Speranza, M. (2002). Deterministic order-up-to level policies in an inventory routing problem. Transportation Science, 36(1), 119–132.

    Article  Google Scholar 

  • Bertazzi, L., & Speranza, G. (2012). Inventory routing problems: An introduction. EURO Journal on Transportation and Logistics, 1(4), 307–326.

    Article  Google Scholar 

  • Burns, L. D., Hall, R. W., Blumenfeld, D. E., & Daganzo, C. F. (1985). Distribution strategies that minimize transportation and inventory costs. Operations Research, 33(3), 469–490.

    Article  Google Scholar 

  • Campbell, A. M., & Savelsbergh, M. W. (2004). A decomposition approach for the inventory-routing problem. Transportation Science, 38(4), 488–502.

    Article  Google Scholar 

  • Carter, M., Farvolden, J., Laporte, G., & Xu, J. (1996). Solving an integrated logistics problem arising in grocery distribution. INFOR, 34, 290–306.

    Google Scholar 

  • Chandra, P. (1993). A dynamic distribution model with warehouse and customer replenishment requirements. Journal of the Operational Research Society, 44(7), 681–692.

    Article  Google Scholar 

  • Christiansen, M. (1999). Decomposition of a combined inventory and time constrained ship routing problem. Transportation Science, 33(1), 3–16.

    Article  Google Scholar 

  • Coelho, L., Cordeau, J., & Laport, G. (2014). Thirty years of inventory routing. Transportation Science, 48(1), 1–19.

    Article  Google Scholar 

  • Dey, S., Bhattacharyya, S., & Ujjwal, M. (2017). Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding. Applied Soft Computing, 56, 472–513.

    Article  Google Scholar 

  • Diabat, A., Dehghani, E., & Jabbarzadeh, A. (2017). Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. Journal of Manufacturing Systems, 43, 139–149.

    Article  Google Scholar 

  • Diabat, A., Richard, J., & Codrington, C. (2013). A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design. Annals of Operations Research, 203, 55–80.

    Article  Google Scholar 

  • Galina, I., Limbourg, S., & Riane, F. (2017). The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains. International Journal of Production Economics, 183, 570–582.

    Article  Google Scholar 

  • Gaur, V., & Fisher, M. L. (2004). A periodic inventory routing problem at a supermarket chain. Operations Research, 52(6), 813–822.

    Article  Google Scholar 

  • Hemmelmayr, V., Doerner, K. F., Hartl, R. F., Savelsbergh, M., et al. (2009). Delivery strategies for blood products supplies. OR Spectrum, 31(4), 707–725.

    Article  Google Scholar 

  • Huang, S. H., & Lin, P. C. (2010). A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty. Transportation Research Part E, 46(5), 598–611.

    Article  Google Scholar 

  • Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597.

    Article  Google Scholar 

  • Jiang, Y., & Grossmann, I. (2015). Alternative mixed-integer linear programming models of a maritime inventory routing problem. Computers and Chemical Engineering, 77, 147–161.

    Article  Google Scholar 

  • Kim, J., & Kim, Y. (2000). A Lagrangian relaxation approach to multi-period inventory/distribution planning. Journal of the Operational Research Society, 51(3), 364–370.

    Article  Google Scholar 

  • Kleywegt, A. J., Nori, V. S., & Savelsbergh, M. W. (2002). The stochastic inventory routing problem with direct deliveries. Transportation Science, 36(1), 94–118.

    Article  Google Scholar 

  • Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408–416.

    Article  Google Scholar 

  • Lefever, W., Aghezzaf, E.-H., & Hadj-Hamou, K. (2016). A convex optimization approach for solving the single-vehicle cyclic inventory routing problem. Computers and Operation Research, 72, 97–106.

    Article  Google Scholar 

  • Li, K., Chen, B., Sivamkumar, A. L., & Wu, Y. (2014). An inventory-routing problem with the objective of travel time minimization. European Journal of Operational Research, 236(3), 936–945.

    Article  Google Scholar 

  • Liu, S. C., & Lee, W. T. (2011). A heuristic method for the inventory routing problem with time windows. Expert Systems with Applications, 38(10), 13223–13231.

    Article  Google Scholar 

  • Lopes, M. C., Andrade, C. E., Queiroz, T. A., Resende, M. G., & Miyazawa, F. K. (2016). Heuristics for a hub location-routing problem. Networks, 68(1), 54–90.

    Article  Google Scholar 

  • Mavrotas, G., Florios, K., & Figueira, J. (2015). An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics. Applied Mathematics and Computation, 270, 25–43.

    Article  Google Scholar 

  • Mehrabian, A., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1(4), 355–366.

    Article  Google Scholar 

  • Miller, D. (1987). Convergence of an annealing algorithm. Mathematical Programming, 34(1), 111–124.

    Google Scholar 

  • Mirzaei, S., & Seifi, A. (2015). Considering lost sale in inventory routing problems for perishable goods. Computers and Industrial Engineering, 87, 213–227.

    Article  Google Scholar 

  • Moin, N., Salhi, S., & Aziz, N. (2010). An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. International Journal of Production Economics, 133(1), 334–343.

    Article  Google Scholar 

  • Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177, 649–679.

    Article  Google Scholar 

  • Nambirajan, R., Mendoza, A., Pazhani, S., Narendran, T., & Ganesh, K. (2016). CARE: Heuristics for two-stage multi-product inventory routing problems with replenishments. Computers and Industrial Engineering, 97, 41–57.

    Article  Google Scholar 

  • Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V., & Javanmard, S. (2014). Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers and Industrial Engineering, 76, 204–221.

    Article  Google Scholar 

  • Oppen, J., Løkketangen, A., & Desrosiers, J. (2010). Solving a rich vehicle routing and inventory problem using column generation. Computers and Operations Research, 37(7), 1308–1317.

    Article  Google Scholar 

  • Park, Y. B., Yoo, J. S., & Park, H. S. (2016). A genetic algorithm for the vendor-managed inventory routing problem with lost sales. Expert Systems with Applications, 53, 149–159.

    Article  Google Scholar 

  • Pasandideh, S., Akhavan Niaki, S., & Mousavi, S. (2013). Two metaheuristics to solve a multi-item multiperiod inventory control problem under storage constraint and discounts. The International Journal of Advanced Manufacturing Technology, 59(5), 1671–1684.

    Article  Google Scholar 

  • Persson, J. A., & Göthe-Lundgren, M. (2005). Shipment planning at oil refineries using column generation and valid inequalities. European Journal of Operational Research, 163(3), 631–652.

    Article  Google Scholar 

  • Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238, 1–17.

    Article  Google Scholar 

  • Qin, L., Miao, L., Ruan, Q., & Zhang, Y. (2014). A local search method for periodic inventory routing problem. Expert Systems with Applications, 41(2), 765–778.

    Article  Google Scholar 

  • Raa, B. (2015). Fleet optimization for cyclic inventory routing problems. International Journal of Production Economics, 160, 172–181.

    Article  Google Scholar 

  • Raa, B., & Dullaert, W. (2017). Route and fleet design for cyclic inventory routing. European Journal of Operational Research, 256(2), 404–411.

    Article  Google Scholar 

  • Rusdiansyah, A., & Tsao, D. (2005). An integrated model for the periodic delivery problems for vending-machine supply chains. Journal of Food Engineering, 70(3), 421–434.

    Article  Google Scholar 

  • Sadjadi, S., Makui, A., Dehghani, E., & Pourmohammad, M. (2016). Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations. Applied Mathematical Modelling, 40(1), 578–596.

    Article  Google Scholar 

  • Savelsberg, M., & Song, J. H. (2007). Inventory routing with continuous moves. Computers and Operations Research, 34(6), 1744–1763.

    Article  Google Scholar 

  • Savelsberg, M., & Song, J. H. (2008). An optimization algorithm for the inventory routing problem with continuous moves. Computers and Operations Research, 35(7), 2266–2282.

    Article  Google Scholar 

  • Shukla, N., Tiwari, M., & Ceglarek, D. (2013). Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand. International Journal of Production Research, 51, 118–137.

    Article  Google Scholar 

  • Singh, T., Arbogast, J., & Neagu, N. (2015). An incremental approach using local-search heuristic for inventory routing problem in industrial gases. Computers and Chemical Engineering, 80, 199–210.

    Article  Google Scholar 

  • Speranza, M. G., & Ukovich, W. (1994). Minimizing transportation and inventory costs for several products on a single link. Operations Research, 42, 879–894.

    Article  Google Scholar 

  • Vidović, M., Popović, D., & Ratković, B. (2014). Mixed integer and heuristics model for the inventory routing problem in fuel delivery. International Journal of Production Economics Part C, 147, 593–604.

    Article  Google Scholar 

  • Yugang, Y., Haoxun, C., & Feng, C. (2008). A new model and hybrid approach for large scale inventory routing problems. European Journal of Operational Research, 189(3), 1022–1040.

    Article  Google Scholar 

  • Zhong, Y., & Aghezzaf, E.-H. (2011). Combining DC-programming and steepest-descent to solve the single-vehicle inventory routing problem. Computers and Industrial Engineering, 61(2), 313–321.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Hadi Jahangir, Mohammad Mohammadi, Seyed Hamid Reza Pasandideh & Neda Zendehdel Nobari

Authors
  1. Hadi Jahangir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Hamid Reza Pasandideh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neda Zendehdel Nobari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyed Hamid Reza Pasandideh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jahangir, H., Mohammadi, M., Pasandideh, S.H.R. et al. Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery. J Intell Manuf 30, 2327–2353 (2019). https://doi.org/10.1007/s10845-018-1393-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-018-1393-z

Keywords

Search

Navigation