Scope

This page outlines the parts of the journal article which we will attempt to reproduce from Hernandez et al. (2015). The journal does not give permission for upload and reuse of images, so you should refer to the article online to view these.

Within scope

Figure 5

“Fig. 5. Pareto fronts for tri-objective Model (2). Experiments were run with a population of 100 and 50 generations. The computational time for obtaining each Pareto front was 6.5 h. The title of each grid corresponds to the percentage of Pre-Screened Designees.” Hernandez et al. (2015)

Figure 6

“Fig. 6. Staffing levels of a sample of solutions. The Dispensing Station needs more resources regardless of the percentage of Pre-Screened Designees. The title of each grid represents the throughput of the solution in forms per hour.” Hernandez et al. (2015)

Figure 7

“Fig. 7. Pareto fronts for bi-objective Model (2). Experiments were run with a population of 50 and for 25 generations. The computational time for each Pareto was 1.6 h. The title of each grid corresponds to the percentage of Pre-Screened Designees.” Hernandez et al. (2015)

Figure 8

“Fig. 8. Pareto fronts for analyzing the impact of the population and generations parameters. Each grid has two numbers in parenthesis: the population size and the number of generations. The objectives are: minimize waiting time, maximize throughput and minimize staff members.” Hernandez et al. (2015)

Figure 9

“Fig. 9. Pareto fronts for analyzing the impact of constraining the maximum number of Line Managers. The name of each grid corresponds to the maximum number of Line Managers allowed.” Hernandez et al. (2015)

Figure 10

“Fig. 10. Impact of replications of the Discrete Event Simulation for modeling the movement of Designees inside the PODs.” Hernandez et al. (2015)

Table 3

“Table 3 Confidence intervals. Waiting times are in minutes.” Hernandez et al. (2015)

Scope: To reproduce the Python columns, but not relevant to reproduce the Arena columns.

Table 4

“Table 4 Staffing levels and associated throughput.” Hernandez et al. (2015)

Outside scope

Figure 1

“Fig. 1. Illustration of the concept of Pareto Efficiency. Blue points are non- dominated (i.e. efficient) and red points are dominated. For example, point a is dominated because there is another point with the same number of staff members but with higher throughput (indicated by the dashed line). With the Pareto front decision makers can select the trade-off solution that best meets their criteria. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)” Hernandez et al. (2015)

Table 1

“Table 1 Number of forms per Designee.” Hernandez et al. (2015)

Figure 2

“Fig. 2. Architecture of the simulation. There are four inputs (left), one control parameter (top) and three outputs (right). The objective of the simulation is to change the staffing levels (control parameter) in order to explore the trade-off solutions.” Hernandez et al. (2015)

Figure 3

“Fig. 3. Systems architecture of the proposed framework. There are three main components: a custom program based on Python that performs the optimization (top left), an Arena (http://www.arenasimulation.com/) model used to verify the results obtained by the Python program (top right) and an R program used to generate publication quality plots of the results (bottom).” Hernandez et al. (2015)

Figure 4

“Fig. 4. Network representation of the flow of Designees inside the POD. Designees follow one of the multiple paths inside the POD.” Hernandez et al. (2015)

Table 2

“Table 2 Service time.” Hernandez et al. (2015)

References

Hernandez, Ivan, Jose E. Ramirez-Marquez, David Starr, Ryan McKay, Seth Guthartz, Matt Motherwell, and Jessica Barcellona. 2015. “Optimal Staffing Strategies for Points of Dispensing.” Computers & Industrial Engineering 83 (May): 172–83. https://doi.org/10.1016/j.cie.2015.02.015.