## 4 Results' Analysis  
The optimization was run for the specified conditions. For consistency, this optimization was repeated 10 times. Regarding the algorithm analysis, the 10 optimizations took an average of 114.2 s, varying from 78.7 s to 128.9 s. As previously mentioned, duplicate individuals were eliminated, meaning that each of the 10 optimizations achieved a different final population; this varied from 19 to 30 members, with an average size of 26.7. While the optimization was run for a maximum of 2000 iterations, the implemented termination criterion caused the optimization to end beforehand. The optimization took between 1370 and 2000 iterations, with an average of 1874 iterations.  
Figure 2 shows a parallel plot of the optimization solutions for the first test run. Additionally, Figure 3 shows the Pareto fronts between the three objectives. To reduce the clutter, only 3 out of the 10 optimization results are shown, randomly selected.  
The first conclusion that can be drawn from the plots is the nicely shaped Pareto front between the energy consumption and the risk, which can also be seen by the inverse behaviour of the results seen in the parallel plot. This simply means that generally, a reduction in energy consumption (caused by a reduction in the stock kept) leads to a higher shortage risk. The slope of the Pareto front between these two objectives also indicates that a small relative increase in energy consumption leads to a substantial reduction in shortage risk.  
The holding costs had a slightly different relation to the two other objectives. They had an approximate proportionality with the energy consumption. This is an expected behaviour, as both objectives were directly proportional to the amount of stock in the storage locations. However, the relation between the two was not always direct, since the holding costs per storage location were not necessarily proportional to the energy costs per storage location. This simply means that a storage location may have a large energy  
Figure 3: Pareto fronts between each objective for 3 distinct optimization results, out of the 10 repetitions.  
Figure 2: Parallel plot of the many-objective optimization solutions. Each line corresponds to an optimal solution. The colour scale supplied regards the first objective, the energy consumption, simply to aid in identifying the solutions across the remaining objectives.  
consumption but a small holding cost, as is the case with the cold storage in this setting. From Figure 2, the general rule is that the more similar the slope between the energy consumption and holding costs between two optimization solutions, the more proportional the stocks considered. Solutions with contrasting slopes tended to have non-proportional stocks of each type of storage location. An example of this can be seen in the two solutions shown in Figure 2 as the grey lines. For clarity, these solutions were indexed as solution 4 (the one with the largest holding cost) and solution 14.  
Table 5 shows that the stock mix was indeed very different between the two solutions, even though these shared similar energy consumption and shortage risk values. The overarching conclusion that can be drawn from this comparison is that for a small reduction in the shortage risk, without substantially changing the level of energy consumption, the stocks would have to be increased, especially in the tanks, which tripled from scenario 14 to 4.