Agent-based simulation study for improving logistic warehouse performance
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.
Keywordssimulation logistics management optimization
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.
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.
- 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
- Bonini C (1963). Simulation of information and decision systems in the firm, Prentice-Hall: New Jersey.Google Scholar
- Bordini R Hübner J and Wooldridge M (2007). Programming multi-agent systems in AgentSpeak using Jason. Wiley-Interscience.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Niroomand S (2013). Studies on Different Types of Facility Layout Problems. PhD thesis, Eastern Mediterranean University.Google Scholar
- Pisaruk N (2012). Optimization in operations management.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- Taylor B and Russell R (2000). Operations management multimedia version.Google Scholar
- Tompkins JA, White JA, Bozer YA, and Tanchoco JMA (2010). Facilities planning. Wiley: New York.Google Scholar
- 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
- 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
- Wooldridge MJ (2001). Introduction to Multiagent Systems. Wiley: New York, NY, USA.Google Scholar
- 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