Comparing an Approximate Queuing Approach with Simulation for the Solution of a Cross-Docking Problem

Abstract

Cross-docking is a logistics management concept in which products are temporarily unloaded at intermediate facilities and loaded onto output trucks to be sent to their final destination. In this paper, we propose an approximate nonstationary queuing model to size the number of docks to receive the trucks, so that their unloading will be as short as possible at the receiving dock, thus making the cross-docking process more efficient. It is observed that the stochastic queuing process may not reach the steady equilibrium state. A type of modeling that does not depend on the stationary characteristics of the process developed is applied. In order to measure the efficiency, performance, and possible adjustments of the parameters of the algorithm, an alternative simulation model is proposed using the Arena5 software. The simulation uses analytic tools to make the problem more detailed, which is not allowed in the theoretical model. The computational analysis compares the results of the simulated model with the ones obtained with the theoretical algorithm, considering the queue length and the average waiting time of the trucks. Based on the results obtained, the simulation represented very well the proposed problem and possible changes can be easily detected with small adjustments in the simulated model.

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“…In simulation, analytic tools are used to make the problem more detailed to be compared with the theoretical model. The computational analysis will compare the results from the simulated model with the results from the theoretical algorithm [20]. Due to the complexity and the growing size of applications of simulation, new programming techniques such as object-oriented programming and virtual reality tools are commonly used.…”

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“…Technological process planning has changed together with the dynamic social demand, according to the Industry 4.0 concept (Briesemeister & Novaes, 2017). In particular, certain shifts occurred in logistic models of inputs and outputs of the production process.…”

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“…Simulation models are also often characterised with the lack of clear structure and often cannot reach an acceptable level of performance [18]. In contrast, an advantage of analytical techniques is the ease of computing using explicit mathematical formulas and numerical computation methods [19]. As observed by Shao et al [20], simulation-based evaluations are customised for special purposes and are difficult to apply them to other scenarios.…”