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Agent-based simulation study for improving logistic warehouse performance

  • Patrizia Ribino
  • Massimo Cossentino
  • Carmelo Lodato
  • Salvatore Lopes
Article

Abstract

Logistic warehouses are critical nodes in a supply chain and improving their performance is a crucial issue when trying to avoid unproductive bottlenecks. Warehouse optimization involves several problems, some of which must be considered at the design stage and others during real-time operations. In this study, we performed an agent-based simulation to analyze the behavior of automatic logistic warehouses under the influence of specific factors, thereby obtaining indicators to supporting decision making during warehouse performance improvement. This study focused mainly on automatic warehouses where goods are moved by automatic guided vehicles.

Keywords

simulation logistics management optimization 

Notes

Acknowledgements

The authors would like to thank Prof. Valeria Seidita for extremely useful suggestions.

Statement of contribution

In this paper, we present an agent-based simulation study that has been conducted for addressing a real case study provided by an industrial project in order to support decision-making for optimising warehouse performance. Specifically, the work deals with improving the performance of logistics warehouses where lorries carrying pallets arrive and deliver their payload. Pallets are to be unloaded and sent to a Sorting Area where goods are processed, packed in smaller parcels and forwarded toward a new destination (usually by city-sized vehicles)

The proposed approach deals with two different phases of warehouse performance optimisation (configuration and operations management) and defines guidelines for both.

The first include the definition of internal layout, of routes to be followed by AGVs, and of the optimal number of AGVs; it is intuitive that increasing the number of AGVs, their cost and routes congestion increase. Operative guidelines include strategies for the optimization of AGV missions and for optimal allocation of docking platforms to incoming lorries.

Optimising AGV missions means defining how many AGVs are devoted to unload each lorry, what paths they follow, where to store pallets that cannot be automatically processed.

Our study included the evaluation of the following performance metrics:
  • Warehouse productivity in terms of Throughput.

  • Speedup factor defined as the ratio between the time necessary to unload the same number of pallets with 1 AGV and with n AGV.

  • Efficiency defined as the average utilization of the AGVs.

  • Unloading policies used for allocating AGVs to each lorry unloading task.

  • Allocation of docking platforms to incoming lorries.

In order to achieve such goals, we run more than 3000 agent-based simulations of a normal working day for a specific warehouse. Warehouse physical dimensions and number/load of incoming lorries have been taken from a real world case study. Nonetheless, obtained results may be considered general and may be seamlessly applied to other real-world situations.

References

  1. Aleisa EE and Lin L (2005). For effective facilities planning: layout optimization then simulation, or vice versa? In Proceedings of the 37th conference on Winter Simulation, 2005, pp 1381–1385.Google Scholar
  2. Amaral AR (2006). On the exact solution of a facility layout problem. European Journal of Operational Research, 173(2): 508–518.CrossRefGoogle Scholar
  3. Amaral AR and Letchford AN (2013). A polyhedral approach to the single row facility layout problem. Mathematical Programming, 141(1–2): 453–477.CrossRefGoogle Scholar
  4. Anjos MF and Vannelli A (2008). Computing globally optimal solutions for single-row layout problems using semidefinite programming and cutting planes. INFORMS Journal on Computing, 20(4): 611–617.CrossRefGoogle Scholar
  5. Ariafar S and Ismail N (2009). An improved algorithm for layout design in cellular manufacturing systems. Journal of Manufacturing Systems, 28(4): 132–139.CrossRefGoogle Scholar
  6. Arifin R and Egbelu PJ (2000). Determination of vehicle requirements in automated guided vehicle systems: a statistical approach. Production Planning & Control, 11(3): 258–270.CrossRefGoogle Scholar
  7. Bonini C (1963). Simulation of information and decision systems in the firm, Prentice-Hall: New Jersey.Google Scholar
  8. Bordini R Hübner J and Wooldridge M (2007). Programming multi-agent systems in AgentSpeak using Jason. Wiley-Interscience.Google Scholar
  9. Borshchev A and Filippov A (2004). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In Proceedings of the 22nd International Conference of the System Dynamics Society, volume 22.Google Scholar
  10. Chan WK and Malmborg CJ (2010). A Monte Carlo simulation based heuristic procedure for solving dynamic line layout problems for facilities using conventional material handling devices. International Journal of Production Research, 48(10): 2937–2956.CrossRefGoogle Scholar
  11. Chan WKV, Son Y-J and Macal CM (2010). Agent-based simulation tutorial-simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. In Proceedings of the Winter Simulation Conference, pp 135–150. Winter Simulation Conference.Google Scholar
  12. Chen X, Ong Y-S, Tan P-S, Zhang N, and Li Z (2013). Agent-based modeling and simulation for supply chain risk management-a survey of the state-of-the-art. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pp 1294–1299. IEEE.Google Scholar
  13. Chen Y, Xiao Q, and Tang X (2011). Product layout optimization and simulation model in a multi-level distribution center. Systems Engineering Procedia, 2: 300–307.CrossRefGoogle Scholar
  14. Cheng R and Gen M (1998). Loop layout design problem in flexible manufacturing systems using genetic algorithms. Computers & Industrial Engineering, 34(1): 53–61.CrossRefGoogle Scholar
  15. Cossentino M, Chella A, Lodato C, Lopes S, Ribino P, and Seidita V (2012a). A notation for modeling Jason-like BDI agents. In Complex, Intelligent and Software Intensive Systems (CISIS), 2012 Sixth International Conference on, pp 12–19. IEEE.Google Scholar
  16. Cossentino M, Lodato C, Lopes S, Ribino P, Seidita V, and Chella A (2012b). Towards a design process for modeling MAS organizations. In Multi-Agent Systems, pp 63–79. Springer.Google Scholar
  17. Datta D, Amaral AR, and Figueira JR (2011). Single row facility layout problem using a permutation-based genetic algorithm. European Journal of Operational Research, 213(2): 388–394.CrossRefGoogle Scholar
  18. Devise O and Pierreval H (2000). Indicators for measuring performances of morphology and material handling systems in flexible manufacturing systems. International Journal of Production Economics, 64(1): 209–218.CrossRefGoogle Scholar
  19. Djellab H and Gourgand M (2001). A new heuristic procedure for the single-row facility layout problem. International Journal of Computer Integrated Manufacturing, 14(3): 270–280.CrossRefGoogle Scholar
  20. Drira A, Pierreval H, and Hajri-Gabouj S (2007). Facility layout problems: a survey. Annual Reviews in Control, 31(2): 255–267.CrossRefGoogle Scholar
  21. El-Baz MA (2004). A genetic algorithm for facility layout problems of different manufacturing environments. Computers & Industrial Engineering, 47(2): 233–246.CrossRefGoogle Scholar
  22. Georgé J, Gleizes M, Glize P, and Régis C (2003). Real-time simulation for flood forecast: an adaptive multi-agent system staff. In Proceedings of the AISB, 3: 109–114.Google Scholar
  23. Gu J, Goetschalckx M, and McGinnis L (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1): 1–21.CrossRefGoogle Scholar
  24. Gu J, Goetschalckx M, and McGinnis L (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research, 203(3): 539–549.CrossRefGoogle Scholar
  25. Hall NG, Sriskandarajah C, and Ganesharajah T (2001). Operational decisions in AGV-served flowshop loops: fleet sizing and decomposition. Annals of Operations Research, 107(1–4): 189–209.CrossRefGoogle Scholar
  26. Hasan MA, Sarkis J, and Shankar R (2012). Agility and production flow layouts: An analytical decision analysis. Computers & Industrial Engineering, 62(4): 898–907.CrossRefGoogle Scholar
  27. Heragu SS and Alfa AS (1992). Experimental analysis of simulated annealing based algorithms for the layout problem. European Journal of Operational Research, 57(2): 190–202.CrossRefGoogle Scholar
  28. Heragu SS and Kusiak A (1991). Efficient models for the facility layout problem. European Journal of Operational Research, 53(1): 1–13.CrossRefGoogle Scholar
  29. Hubner J, Sichman J, and Boissier O (2007). Developing organised multiagent systems using the MOISE+ model: programming issues at the system and agent levels. International Journal of Agent-Oriented Software Engineering, 1(3): 370–395.CrossRefGoogle Scholar
  30. Jarvis JM and McDowell ED (1991). Optimal product layout in an order picking warehouse. IIE transactions, 23(1): 93–102.CrossRefGoogle Scholar
  31. Ji M and Xia J (2010). Analysis of vehicle requirements in a general automated guided vehicle system based transportation system. Computers & Industrial Engineering, 59(4): 544–551.CrossRefGoogle Scholar
  32. Kahraman A, Gosavi A, and Oty K (2008). Stochastic modeling of an automated guided vehicle system with one vehicle and a closed-loop path. IEEE Transactions on Automation Science and Engineering, 5(3): 504–518.CrossRefGoogle Scholar
  33. Kasilingam R and Gobal S (1996). Vehicle requirements model for automated guided vehicle systems. The International Journal of Advanced Manufacturing Technology, 12(4): 276–279.CrossRefGoogle Scholar
  34. Keller B and Buscher U (2015). Single row layout models. European Journal of Operational Research, 245(3): 629–644.CrossRefGoogle Scholar
  35. Khalili-Damghani K, Khatami-Firouzabadi SA, and Diba M (2014). A genetic algorithm to solve process layout problem. International Journal of Management and Decision Making, 13(1): 42–61.CrossRefGoogle Scholar
  36. Klügl F, Fehler M, and Herrler R (2005). About the role of the environment in multi-agent simulations. Environments for Multi-agent Systems, 33740: 127–149.CrossRefGoogle Scholar
  37. Kothari R and Ghosh D (2013). Tabu search for the single row facility layout problem using exhaustive 2-opt and insertion neighborhoods. European Journal of Operational Research, 224(1): 93–100.CrossRefGoogle Scholar
  38. Kouvelis P and Chiang W-C (1996). Optimal and heuristic procedures for row layout problems in automated manufacturing systems. Journal of the Operational Research Society, 47(6): 803–816.CrossRefGoogle Scholar
  39. Krishnamurthy NN, Batta R, and Karwan MH (1993). Developing conflict-free routes for automated guided vehicles. Operations Research, 41(6): 1077–1090.CrossRefGoogle Scholar
  40. Kumar KR, Hadjinicola GC, and Lin T-l (1995). A heuristic procedure for the single-row facility layout problem. European Journal of Operational Research, 87(1): 65–73.CrossRefGoogle Scholar
  41. Kumar RS, Asokan P, and Kumanan S (2008). Design of loop layout in flexible manufacturing system using non-traditional optimization technique. The International Journal of Advanced Manufacturing Technology, 38(5-6): 594–599.CrossRefGoogle Scholar
  42. Lee T, Park N, and Lee D (2003). A simulation study for the logistics planning of a container terminal in view of SCM. Maritime Policy & Management, 30(3): 243–254.CrossRefGoogle Scholar
  43. Macal CM and North MJ (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3): 151–162.CrossRefGoogle Scholar
  44. Macro J and Salmi R (2002). Warehousing and inventory management: a simulation tool to determine warehouse efficiencies and storage allocations. In Proceedings of the 34th Winter Conference on Simulation: Exploring New Frontiers, pp 1274–1281. Winter Simulation Conference.Google Scholar
  45. Möhring RH, Köhler E, Gawrilow E, and Stenzel B (2005). Conflict-free real-time AGV routing. In Operations Research Proceedings 2004, pp 18–24. Springer.Google Scholar
  46. Morad N (2000). Genetic algorithms optimization for the machine layout problem. In International Journal of the Computer, the Internet and Management, 8(1).Google Scholar
  47. Na Z, Kelin X, and Shuang G (2010). Research on multi-row layout based on genetic algorithm. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2010 International Conference on, 1: 380–384. IEEE.Google Scholar
  48. Niroomand S (2013). Studies on Different Types of Facility Layout Problems. PhD thesis, Eastern Mediterranean University.Google Scholar
  49. Picard J-C and Queyranne M (1981). On the one-dimensional space allocation problem. Operations Research, 29(2): 371–391.CrossRefGoogle Scholar
  50. Pisaruk N (2012). Optimization in operations management.Google Scholar
  51. Qiu L and Hsu W-J (2000). Conflict-free AGV routing in a bi-directional path layout. In Proceedings of the 5th International Conference on Computer Integrated Manufacturing, Citeseer, 1: 392–403.Google Scholar
  52. Rao A (1996). AgentSpeak (L): BDI agents speak out in a logical computable language. In European Workshop on Modelling Autonomous Agents in a Multi-Agent World , pp 42–55.Google Scholar
  53. Rao A and Georgeff M (1995). BDI agents: From theory to practice. In Proceedings of the First International Conference on Multi-agent Systems (ICMAS-95), San Francisco, pp 312–319.Google Scholar
  54. Ribino P, Cossentino M, Lodato C, Lopes S, and Seidita V (2015). Requirement analysis abstractions for AMI system design. Journal of Intelligent & Fuzzy Systems, 28(1): 55–70.Google Scholar
  55. Ribino P, Seidita V, Lodato C, Lopes S, and Cossentino M (2014). Common and domain-specific metamodel elements for problem description in simulation problems. In M. Ganzha, L. Maciaszek, M. P., editor, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, volume 2 of Annals of Computer Science and Information Systems, pp 1467–1476. IEEE.Google Scholar
  56. Sai-nan L (2008). Optimization problem for AGV in automated warehouse system. In IEEE International Conference on Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. 2: 1640–1642.Google Scholar
  57. Samarghandi H and Eshghi K (2010). An efficient tabu algorithm for the single row facility layout problem. European Journal of Operational Research, 205(1): 98–105.CrossRefGoogle Scholar
  58. Siebers P, Macal CM, Garnett J, Buxton D, and Pidd M (2010). Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation, 4(3): 204–210.CrossRefGoogle Scholar
  59. Solimanpur M, Vrat P, and Shankar R (2005). An ant algorithm for the single row layout problem in flexible manufacturing systems. Computers & Operations Research, 32(3): 583–598.CrossRefGoogle Scholar
  60. Tansel BC and Bilen C (1998). Move based heuristics for the unidirectional loop network layout problem. European Journal of Operational Research, 108(1): 36–48.CrossRefGoogle Scholar
  61. Taylor B and Russell R (2000). Operations management multimedia version.Google Scholar
  62. Tompkins JA, White JA, Bozer YA, and Tanchoco JMA (2010). Facilities planning. Wiley: New York.Google Scholar
  63. Vis I, de Koster R, Roodbergen K, and Peeters L (2001). Determination of the number of automated guided vehicles required at a semi-automated container terminal. Journal of the Operational Research Society, 52(4): 409–417.CrossRefGoogle Scholar
  64. Vitayasak S, and Pongcharoen P (2015). Genetic algorithm based robust layout design by considering various demand variations. In Advances in Swarm and Computational Intelligence. Springer: New York, pp 257–265.Google Scholar
  65. Weyns D, Schelfthout K, Holvoet T, and Lefever T (2005). Decentralized control of E’GV transportation systems. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems. ACM: New York, pp 67–74.Google Scholar
  66. Wooldridge MJ (2001). Introduction to Multiagent Systems. Wiley: New York, NY, USA.Google Scholar
  67. Yifei T, Junruo C, Meihong L, Xianxi L, and Yali F (2010). An estimate and simulation approach to determining the automated guided vehicle fleet size in FMS. In Third IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010, 9: 432–435.Google Scholar
  68. Yoo J-W, Sim E-S, Cao C, and Park J-W (2005). An algorithm for deadlock avoidance in an AGV system. The International Journal of Advanced Manufacturing Technology, 26(5-6): 659–668.CrossRefGoogle Scholar

Copyright information

© The Operational Research Society 2017

Authors and Affiliations

  • Patrizia Ribino
    • 1
  • Massimo Cossentino
    • 1
  • Carmelo Lodato
    • 1
  • Salvatore Lopes
    • 1
  1. 1.Istituto di Calcolo e Reti ad Alte PrestazioniConsiglio Nazionale delle RicerchePalermoItaly

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