5. Modelling the Logistical Network
5.4. Experimental Design
To be able to come up with answers regarding the impact upon the logistical network, both the
ExperimentManager and the EventController are used within the simulation software. To do so, decisions upon the run length of the simulation (1) and the warm-up period are required. Furthermore, the experimental setup encompassing the experimental input factors (2) and KPIs (3) are described. Lastly, the number of replications per experiment (4) are explained, which determine the running time for concise analysis and deal with the extent of variability.
1. Run Length & Warm – Up Period
To ensure that the simulation represents reality in a reliable manner, the run length needs to be determined. The run length equals the time until the end of one simulation run. Since this research case integrating the new warehouse is based upon the long-term, the decision is made to simulate one year per simulation run. Within one year, a good measure upon the performance indicators can be done. Stochastic behavior and variability within the data can thereby be eliminated as much as possible. However, the simulation is due to its stochastic behavior rather uncertain, especially at the beginning. Within terminating simulations, the principle of a warm-up period is integrated. This warm-up period is taken into account during analysis to ensure that after this period the simulation enters a steady-state in which its behavior is representable and reliable. Based upon the run length of one year, the warm-up period is considered to be one week. So during simulation, this week is used to configure the simulation, and especially prevent unexpected and highly variable behavior from happening within the starting phase.
2. Input Factors – Interactive Dashboard
By means of the simulation, the possibility of creating an interactive dashboard in which the user can apply his/her wishes and simulate different situations is opted. In here, multiple situations are derived where the possibilities and impact can be analyzed. Varying these input parameters is based primarily upon the bottleneck framework of Chapter 4 to check if transportation upon the Company X site can happen more efficiently when certain elements are adjusted or removed within the simulation.
FIGURE 5-18:EXPERIMENT MANAGER
FIGURE 5-19:PLANT SIM ICONS
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The following instants are investigated within this dashboard, where the results upon varying the parameters are computed in Chapter 6:
• Replacing Z5 Supply – recall that the manual supply of Product X for production facility Z5 is crossing the AGV track multiple times. These crossings can interrupt the AGV and therefore the option of replacing this manual supply by automatic supply via pipelines (as is the case with the other product X supplies) is investigated. To do so, a Boolean Expression is used where the replacement can take on the values true and false, indicating if replacement via pipelines is put in place.
• Relocating Company Y Activities –here the same Boolean Expression is used, but now the option of relocating the Company Y activities towards the other side of the Location X site is opted. This is part of the master plan of Company X and therefore the influence that this relocation has upon the fluency of the network needs to be investigated.
• Relocating Pallet Supplementing –the pallets that need to be supplemented from the storage area at the production facilities are transported by means of forklift trucks. These transports cross the AGV tracks as well and therefore a Boolean Expression is used again to indicate if these pallet transports might be performed from another place. If the value for this relocation is true than the pallet supplementing is performed from another side, preventing the crossings of the AGV track. • Waiting System Expedition X Transports –there is a huge difference between allowing the truck drivers of the F3 department to do what they want or to regulate their behavior. This has especially impact upon the functionality of the AGV. By regulating, we mean that the drivers are stopped by means of traffic lights or traffic barriers if the AGV is in the neighborhood. Meaning that the AGV always has priority ruling and the truck drivers have to wait until it passed by. Such a waiting system needs to be investigated whether or not it has a positive influence upon the logistical network.
• Processing Time Porter’s Lodge –the processing time of the new porter’s lodge is highly variable, especially in the beginning. This is due to all the safety rules and regulations that ought to be provided to the new truck drivers arriving at the Company X site. Therefore, distinctions between the processing times of new truck drivers and known truck drivers is made. The known truck drivers, whom will be expanding with time (since they get acquainted with the rules), are able to compel to the safety rules and know exactly what to do on the site of Company X. Furthermore, automating certain activities at the Porter’s Lodge by for example using Automatic Number Plate Recognition as we saw earlier in Chapter 4.5. might reduce the processing times. To test the consequences of the processing times, three scenarios are analyzed. The minimum processing time on average is 2.5 minutes here, whereas the maximum processing time of a truck is set at 12.5 minutes. To test this adequately, uniform ranges are created where the processing times vary between the upper and the lower bounds. Based on these ranges, Company X can gain insights in future impacts and whether to choose for automation of certain activities in order to reduce processing times. The uniform ranges of the processing times in minutes, with slight overlaps, are: 1– U [2.30;7:30], 2– U [5.00;10.00] and 3– U [7.30;12.30].
• Velocity AGV – as we described earlier, the AGV has an average speed of 6 km/h based upon market perspective. However, variations are possible. Increasing the velocity might result in more AGV transports on a daily basis. But safety is a major concern of Company X and when for example the AGV drives around with 20-25 km/h on the Company X site this could result in dangerous situations. Therefore, a trade-off between velocity and safety needs to be made. To test this, four input values are considered for the AGV velocity: 4,6,8 and 10 km/h.
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During experimentation, all the previously mentioned factors are tested within a full-factorial design. Since there are two options for the first four parameters, three options for the fifth parameter and four options for the last one, we have a 24∗ 41∗ 31= 24∗ 22∗ 31= 26∗ 31 = 192-factorial design.
Meaning that 192 experiments are required to check upon the performance indicators that are explained in Section 5.3. Overall, the following alternations for the input parameters are tested:
TABLE 5-2:SIMULATION INPUT PARAMETERS USED FOR TESTING DIFFERENT SCENARIOS
The exact configurations among these alternations and corresponding best outcomes are handled in Chapter 6 where the results are analyzed.
3. Experimental Output
Recall that we made an overview of the KPIs throughout this research. These KPIs are used as output, to check what the impact upon the network is. First of all, AHP is performed with help of dedicated Company X employees, to categorize the KPIs. Hereafter, data is gathered for all the KPIs but the emphasis lies upon the outcomes of the AHP analysis. If required, more output factors might be evaluated as well to check for every instance what the consequences are by means of the model.
4. #Replications
The number of replications/observations per run is set at three. So every experiment is simulated three times to prevent inconsistencies and extreme variances from happening. In a later stage the averages of these three replications are computed, to be able to obtain results with an as high as possible level of confidence.
5.5. Conclusion
Within this chapter an outline of the modelling approach is provided. First of all, the Technomatix PlantSimulation model is described, where elements like the ControlPanel,Frames,MUs, TableFiles and corresponding Logic are explained. Furthermore, the Key Performance Indicators (KPIs) for this specific case study are elaborated per logistical process: (1) Inbound Logistics, (2) AGV Transportation, (3) Outbound Logistics and (4) The Porter’s Lodge. These KPIs can be seen as the output of the simulation runs. Besides the outline and the KPIs, the applicability of the input data is discussed. The integration of CAD models, the discrete empirical distribution fitting amongst the transportation frequencies and the different dimensions regarding the vehicles and facilities are described. Moreover, the experimental setup and accompanying factors are discussed. The decision has been made to make use of six different input factors: (1) Replacement Z5 Supply, (2) Relocation Company Y Activities, (3) Relocation Pallet Supplementing, (4) Waiting System Expedition X Transports, (5) Processing Time Porter’s Lodge and (6) Velocity AGV. These can be configured in such a way that a 26∗ 31 = 192 full factorial experimental design
is fulfilling all the different options. The experiments are running for one year with every experiment consisting out of three replications in order to reduce variability in the output.