Parcel transshipment terminals are complex systems that consist of automatic conveyor networks and manual handling activities. Therefore, using discrete-event simulation to evaluate a terminal seems obvious and is often used in the literature. On the other hand, mathematical optimization represents a powerful method that has been successfully used to solve a wide range of logistical problems. Simulation allows for modeling logistics systems with almost unlimited complexity very close to reality, but finding the best system configuration is difficult and time-consuming. In contrast, mathematical optimization has the ability to make complex decisions and find (near) optimal solutions. Real-world logistic systems, however, can only be solved on a lower level of detail without stochastic behaviors. In this article, we present a modeling framework for the operational planning of a transshipment terminal that closely links both methods in order to make use of their complementary advantages. Thus, we are able to handle a large number of decisions, such as (un)loading dock and sorting destination assignments, and consider complex automatic sorting systems as well as manual handling activities with stochastic elements. The approach is evaluated on the example of a parcel transshipment terminal using three different input data scenarios.
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Bermudez R and Cole MH (2001). A genetic algorithm approach to door assignment in breakbulk terminals. Technical Report MBTC-1102, Mack-Blackwell Transportation Center, University of Arkansas, Fayetteville, AR.
Bloss R (2013). Automation pushes the envelope of postal mail handling efficiency. Assembly Automation 33(1): 3–7.
Boysen N and Fliedner M (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega 38(6): 413–422.
Chmielewski A, Naujoks B, Janas M and Clausen U (2009). Optimizing the door assignment in LTL-terminals. Transportation Science 43(2): 198–210.
Clausen U and Diekmann D (2012). Simulation of transshipment terminals in the parcel delivery industry. In: Proceedings of the European Simulation and Modelling Conference 2012, Oct 22–24, Essen, Germany, pp 394–398.
Clausen U, Diekmann D, Baudach J, Kaffka J and Poeting M (2015). Improving parcel transshipment operations—impact of different objective functions in a combined simulation and optimization approach. In: Yilmaz L, Chan WKV, Moon I, Roeder TMK, Macal C and Rossetti M D (eds). Proceedings of the 2015 Winter Simulation Conference. IEEE, Piscataway, pp 1924–1935.
Diekmann D, Baudach J and Clausen U (2014). A new approach of linking mathematical optimization and discrete event simulation for operating transshipment terminals in the parcel delivery industry. In: Proceedings of the European Simulation and Modelling Conference 2014, Oct 22–23, Porto, Portugal, pp 254–260.
Gue KR (1999). The effects of trailer scheduling on the layout of freight terminals. Transportation Science 33(4): 419–428.
Haneyah SWA, Schutten JMJ and Fikse K (2014). Throughput maximization of parcel sorter systems by scheduling inbound containers. In: Clausen U, ten Hompel M and Meier F (eds). Efficiency and Innovation in Logistics: Proceedings of the International Logistics Science Conference (ILSC) 2013. Springer International Publishing, Cham, pp 147–159.
Jarrah A, Qi X and Bard J (2014). The destination-loader-door assignment problem for automated package sorting centers. Transportation Science. Advance online publication 21 May, doi: 10.1287/trsc.2014.0521.
Juan AA, Faulin J, Grasman SE, Rabe M and Figueira G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. In: Operations Research Perspectives 2: 62–72.
Kille C and Schwemmer M (2013). TOP 100 in European Transport and Logistics Services 2013/2014. Deutscher Verkehrs-Verlag, Hamburg.
Ladier AL, Greenwood A and Alpan G (2015). Modeling issues when using simulation to test the performance of mathematical programming models under stochastic conditions. In: Proceedings of the European Simulation and Modelling Conference 2015, Oct 26–28, Leicester, England, pp 117–121.
Liu Y and Takakuwa S (2009). Simulation-based personnel planning for materials handling at a cross-docking center under retail distribution environment. In: Rossetti MG, Hill RR, Johansson B, Dunkin A and Ingalls RG (eds). Proceedings of the 2009 Winter Simulation Conference. IEEE, Piscataway, pp 2414–2425.
Magableh GM, Rossetti MD and Mason S (2005). Modeling and analysis of a generic cross-docking facility. In: Kuhl ME, Steiger NM, Armstrong FB, and Joines JA (eds). Proceedings of the 37th Winter Simulation Conference. IEEE, Piscataway, pp 1613–1620.
Masel D (1998). Adapting the longest processing time heuristic (LPT) for output station assignment in a sortation facility. In: Proceedings of the 7th Annual Industrial Engineering Research Conference, May 23–24, Phoenix, AZ.
Masel D and Goldsmith D (1997). Using a simulation model to evaluate the configuration of a sortation facility. In: Andradottir S, Healy KJ, Withers DH and Nelson BL (eds). Proceedings of the 1997 Winter Simulation Conference. IEEE, Piscataway, pp 1210–1213.
McWilliams DL (2010). Iterative improvement to solve the parcel hub scheduling problem. Journal of Computers & Industrial Engineering 59(1): 136–144.
McWilliams DL and McBride ME (2012). A beam search heuristics to solve the parcel hub scheduling problem. Journal of Computers & Industrial Engineering 62(1): 1080–1092.
McWilliams DL, Stanfield P and Geiger C (2005). The parcel hub scheduling problem: A simulation-based solution approach. Computer & Industrial Engineering 49(3): 393–412.
McWilliams DL, Stanfield P and Geiger C (2008). Minimizing the completion time of the transfer operations in a central parcel consolidation terminal with unequal-batch-size inbound trailers. Computer & Industrial Engineering 54(4): 709–702.
Neumann L and Deymann S (2008). Transsim-Node—a simulation tool for logistics nodes. In: Industrial Simulation Conference. The European Simulation Society, Lyon, pp 283–287.
Rohrer M (1995). Simulation and cross docking. In: Alexopoulos C, Kang K, Lilegdon W R and Goldsman D (eds). Proceedings of the 1995 Winter Simulation Conference. IEEE, Piscataway, pp 846–849.
Rushton A, Croucher P and Baker P (2010). The Handbook of Logistics and Distribution Management. Kogan Page Publishers, Philadelphia.
Tsui LY and Chang CH (1992). Optimal solution to a dock door assignment problem. Computer & Industrial Engineering 23(1): 283–286.
Van Belle J, Valckenaers P and Cattrysee D (2012). Cross-docking: State of the art. Omega 40(6): 827–846.
Werners B and Wülfing T (2010). Robust optimization of internal transports at a parcel sorting center operated by Deutsche Post World Net. European Journal of Operational Research 201(2): 419–426.
Werners B, Thorn J and Freiwald S (2001). Innerbetriebliche Transportoptimierung für ein Paketzentrum der Deutschen Post World Net. OR-Spektrum 23(4): 507–523.
Yu W and Egbelu P (2008). Scheduling of inbound and outbound trucks in cross docking systems with temporary storage. European Journal of Operational Research 184(1): 377–396.
The presented results are part of the work from the research project “Development of a combined approach that links discrete mathematical optimization and stochastic simulation for planning and operating logistics nodes (applied to transshipment terminals in the parcel delivery industry)” funded by the Deutsche Forschungsgemeinschaft (DFG—German Research Foundation).
Mathematical optimization and discrete-event simulation include two of the most powerful OR techniques. However, these two techniques are mainly used separately. Our article proposes a combined framework that closely links both methods in order to make use of their complementary advantages. On the one hand, optimization allows modeling and solving complex problems to find a near optimal system configuration. On the other hand, the advantage of simulation is that it allows us to study phenomena close to reality that are difficult or too complex to describe and solve analytically. Therefore, our contribution is to bridge knowledge to enable a joint usage of both methods in a combined approach.
Our article not only aims as a valuable contribution to bridging the currently existing scientific and methodological gap at the interface between optimization and simulation, but we also intent to show the concrete practical benefits of our approach for operating parcel transshipment terminal. By using a practical application example, our target is to proof that a combined approach is applicable for practical operational planning as well as to show that the combined use of simulation and optimization ultimately leads to better results than one of two methods could achieve on their own.
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Clausen, U., Diekmann, D., Pöting, M. et al. Operating parcel transshipment terminals: a combined simulation and optimization approach. J Simulation 11, 2–10 (2017). https://doi.org/10.1057/s41273-016-0032-y
- discrete-event simulation
- parcel transshipment operations