pareto (Indicates Pareto-optimal solutions: 1 = Pareto-optimal, 0 = non-optimal)<\/span><\/li>\n<\/ul>\nThe vertical axis for each variable is scaled independently.<\/span><\/p>\nThe plot shows trade-offs between objectives using different colors: <\/span>red lines<\/b><\/span> represent Pareto-optimal solutions that offer the best balance, while <\/span>blue lines<\/b><\/span> indicate non-optimal solutions. Positively correlated variables show parallel trends, whereas negatively correlated ones intersect. The optimization aims to minimize intervention duration and costs while considering environmental impacts (CO2, NOx, SO2, energy use). The red lines show how different design choices balance these factors. Several variations in certain axes, such as cost and duration, indicate a broad range of feasible solutions, while nearly constant parameters indicate minimal influence on optimization.<\/span><\/strong><\/p>\nTable 2. Comparison of the\u00a0Best Solutions for Each Objective<\/p>\n
\n\n\n| Objective<\/th>\n | Best Duration
\n(First Version)
\nSolution 2<\/th>\n | Best Duration
\n(Second Version)
\nSolution 7<\/th>\n | Best CO2
\n(First Version)
\nSolution 1<\/th>\n | Best CO2
\n(Second Version)
\nSolution 4<\/th>\n<\/tr>\n<\/thead>\n |
\n\n| Duration (years)<\/td>\n | 31.5<\/td>\n | 36<\/td>\n | 85.5<\/td>\n | 110.5<\/td>\n<\/tr>\n |
\n| Interval dist. (years)<\/td>\n | -9<\/td>\n | -8<\/td>\n | -3<\/td>\n | -1<\/td>\n<\/tr>\n |
\n| Energy (MJ)<\/td>\n | 2.12E+08<\/td>\n | 3.12E+08<\/td>\n | 2.12E+08<\/td>\n | -3.26E+08<\/td>\n<\/tr>\n |
\n| CO\u2082 (kg)<\/td>\n | 1,390,611<\/td>\n | 5,926,867<\/td>\n | 1,390,611<\/td>\n | -1,104,263<\/td>\n<\/tr>\n |
\n| NO\u2093 (kg)<\/td>\n | 867,306<\/td>\n | 1,308,111<\/td>\n | 867,306<\/td>\n | 1,300,409<\/td>\n<\/tr>\n |
\n| SO\u2082 (kg)<\/td>\n | 366,567<\/td>\n | 552,057<\/td>\n | 366,567<\/td>\n | 550,298<\/td>\n<\/tr>\n |
\n| Cost (\u20ac)<\/td>\n | 15.94<\/td>\n | 24.15<\/td>\n | 15.94<\/td>\n | 23.80<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n We examined the difference between the best solutions from both optimization versions we have observed that:<\/span><\/strong><\/p>\nTrade-off Between Duration and Environmental Impact<\/h3>\nThe newly identified optimal duration is slightly longer (36 years compared to 31.5 years). However, this minor increase leads to higher environmental impact and cost, highlighting a trade-off: reducing maintenance frequency results in greater long-term environmental and financial burdens. Additionally, the updated best CO\u2082 solution differs significantly (-1.1E+06 vs. 1.39E+06). The second optimization run appears to have identified an extreme trade-off, potentially overemphasizing CO\u2082 reduction at the expense of practical feasibility.<\/p>\n Exploration of More Extreme Values<\/h3>\nCompared to the first run, the second optimization explores more extreme values, offering a broader spectrum of solutions.As we can see the initial run produced a more stable range, the updated results include longer intervention distances, significantly lower CO\u2082 emissions (-1.1E+06), and reduced costs (12.48B). This expanded solution space suggests a more wide range of trade-offs, suggesting unconventional yet impactful alternatives that assists further evaluation.<\/p>\n Choosing the Best Solution Set<\/h3>\nThe best solution <\/span>sets<\/span>\u00a0depends on the desired trade-offs and <\/span>objective. If <\/span>the decision maker prefers <\/span>a <\/span>balanced and stable approach, the first set of solutions will<\/span> be more suitable. However, if the priority is to<\/span> achieve <\/span>more aggressive reductions in <\/span>both emissions and costs, the second set of solutions <\/span>offers greater advantages<\/span>. <\/span>Given the objective of minimizing duration while maximizing intervention distance, the <\/span>second solution set<\/b> that contains more Pareto-optimal solutions is preferable because it provides greater flexibility in selecting the best trade-offs.<\/span><\/p>\n
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