The goal of the alert generating model is to recognize all the 12NCs that have an imbalanced network. However, it is undesirable that situations are being flagged as requiring an intervention, while it would actually not be beneficial to execute a transshipment (either due to non-availability reasons or to economic reasons). It would be inefficient and undesirable to analyze these alerts and generate interventions for them in the first place. Therefore, the decision parametersαE[N AV],
αSRII and βE[N AV] need to be optimized in such a way that the network is balanced optimally for all the relevant 12NCs and that there are no alerts for 12NCs for which a proactive lateral transshipment is impossible or inefficient. Using this logic I have derived a formula to express the performance of a combination of parameter values. The performance of the parameter set is equal to the sum of the objective value improvements minus a penalty for alerts without an intervention and a smaller penalty for alerts with an intervention with very little impact. The penalties are determined such that the performance of a set decreases when the additional alerts do not generate interventions with substantial impact. I chose a high penalty for alerts that lead to no intervention and a smaller penalty for alerts that lead to an intervention proposal with small impact. The reason for this is that it is undesirable to run the intervention generating model without result. Interventions with small impact are undesired, however it is the choice of the planner when to execute and intervention proposal or when not to take the costs and risks. During the validation it became clear that the a reduction of the maximum risk lower than 0.1 unplanned non-availabilities in the next month is a good cut-off value.
δa= Objective value before intervention for parta−
Objective value after intervention for parta (4.20) Performance= ∑
a∈A
δa−0.75∗Count of alerts for which no intervention is proposed −0.25∗Count of alerts for whichδa<0.1 (4.21)
To find the optimal parameter setting I performed 143 experiments, each with a different parameter setting, varying the parameters between the values found in table4.1. I have plotted the surface of the alert trigger performance when two of the three parameters are changed. All test were executed for data from Wednesday 15thof august, which is exactly in the middle of the week and month. The objective function that minimizes the maximum regional expected unplanned non-availabilities is chosen since this option is preferred by ASML.
Table 4.1: Range of values for the decision parameters Parameter Range Step size
αE[N AV] 0.1 - 0.7 0.1
αSRII 0% - 100% 10%
βE[N AV] 0.1 - 0.7 0.1
Expected unplanned non-availabilities for the receiving region vs. scheduled receipts
First, I varied the values of the expected unplanned non-availabilities in the next month for the receiving region and the percentage scheduled receipts within two weeks at the Central
Warehouse. For this series of experiments, the maximum level of expected unplanned
non-availabilities for the shipping region is kept at 0.5. I have plotted the resulting performance of the set of decision parameters in a surface plot. The goal is to find the set of parameter values
4.3. VALIDATION AND NUMERICAL EXPERIMENTS CHAPTER 4. DECISION MODEL
The results of the first series of experiments can be found in figure4.3where the performance of the parameter set is plotted as a surface. The maximum performance can be found for the values:
αE[N AV]=0.3 andαSRII =40% whereβE[N AV]=0.5 for all experiments.
The relation that becomes clear from this series of experiments is that when the allowed scheduled receipts percentage is increased, more alerts will be generated for which no intervention is proposed. Hence, the performance decreases. This is caused by the fact that the alerts are triggered based on the expected unplanned non-availabilities in the next month of a region, without incorporating the replenishment quantity that will most likely arrive as well in this month. The intervention generating model does incorporate this supply and adjusts (read: decreases) the expected unplanned non-availabilities in these regions. This means that the impact of an intervention is smaller or interventions are not even proposed.
The same relation holds for a very low threshold on the expected unplanned non-availabilities in the next month for the receiving region. This makes sense because when regions with relatively few expected unplanned non-availabilities in the next month are allowed to receive, the reduction of the already low risk will be very small. Lastly, when the threshold on the expected unplanned non-availabilities is increased, the number of useful alerts decreases.
Figure 4.3: Surface plot of alert trigger performance
Expected unplanned non-availabilities for the supporting region vs. scheduled receipts To optimize the value of the maximum level of expected unplanned non-availabilities at the supporting region, I’ve varied the values ofβE[N AV] andαSRII while keepingαE[N AV] at 0.3, the optimal value found in the previous series of experiments.
The results of the second series of experiments can be found in figure4.4. The surface shows no effect of the parameter on changes in expected unplanned non-availabilities in the next month of the shipping region. The main effect of the parameter on the percentage scheduled receipts of theCritical shortageis similar to the effect shown in the first series of experiments. Because of the
4.3. VALIDATION AND NUMERICAL EXPERIMENTS CHAPTER 4. DECISION MODEL
negligible main effect of the parameterβE[N AV]I decided not to perform 35 additional experiments to plot the surface of the performance withαE[N AV]and βE[N AV]variables.
Figure 4.4: Surface plot of alert trigger performance
Choice of parameters In the first series of experiments I have found the best performance of the set of alerts with the parameters set toαE[N AV]=0.3 andαSRII =40% whereβE[N AV]=0.5 for all experiments. In the second series of experiments, I have found thatβE[N AV]does not have influence on the performance of the set of alerts whenβE[N AV] >0.1. Furthermore, it does not make sense that theβE[N AV] is higher than the αE[N AV], otherwise it is possible that alerts are generated in situations where it can be possible for a region to be eligible to both send and receive support for a part. Therefore, I propose the parameter settings to be used in the alert generating model, as shown in4.2.
Table 4.2: Proposed parameter settings
Parameter Description Optimal value
αE[N AV] Minimum expected unplanned non-availabilities in the next month for the receiving region.
0.30
αSRII Maximum scheduled receipts quantity in the next two weeks as a percentage of theCritical shortagein the network.
40%
βE[N AV] Maximum expected unplanned non-availabilities in the next month for the sending region.
0.30