Multi-objective optimization for sustainable road network maintenance under traffic equilibrium: Incorporating costs and environmental impacts

2021 ◽  
pp. 130103
Author(s):  
Jinchao Guan ◽  
Xu Yang ◽  
Lingyun You ◽  
Ling Ding ◽  
Xiaoyun Cheng
Keyword(s):  
Environmental Impacts ◽  
Road Network ◽  
Multi Objective Optimization ◽  
Traffic Equilibrium ◽  
Multi Objective ◽  
Network Maintenance

scholarly journals Multi-objective optimization identifies trade-offs between self-sufficiency and environmental impacts of regional agriculture in Baden-Württemberg, Germany

2020 ◽  
pp. 1-20
Author(s):  
Christian Buschbeck ◽  
Larissa Bitterich ◽  
Christian Hauenstein ◽  
Stefan Pauliuk
Keyword(s):  
Environmental Impact ◽  
Environmental Impacts ◽  
Food Supply ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Regional Food ◽  
Multi Objective ◽  
Impact Reduction ◽  
Self Sufficiency ◽  
Political Targets

Regional food supply, organic farming, and changing food consumption are three major strategies to reduce the environmental impacts of the agricultural sector. In the German Federal State of Baden-Württemberg (population: 11 million), multiple policy and economic incentives drive the uptake of these three strategies, but quantitative assessments of their overall impact abatement potential are lacking. Here, the question of how much food can be produced regionally while keeping environmental impacts within political targets is tackled by comparing a scenario of maximum productivity to an optimal solution obtained with a multi-objective optimization (MO) approach. The investigation covers almost the entirety of productive land in the state, two production practices (organic or conventional), four environmental impact categories, and three demand scenarios (base, vegetarian, and vegan). We present an area-based indicator to quantify the self-sufficiency of regional food supply, as well as the database required for its calculation. Environmental impacts are determined using life cycle assessment. Governmental goals for reducing environmental impacts from agriculture are used by the MO to determine and later rate the different Pareto-efficient solutions, resulting in an optimal solution for regional food supply under environmental constraints. In the scenario of maximal output, self-sufficiency of food supply ranged between 61% and 66% (depending on the diet), and most political targets could not be met. On the other hand, the optimal solution showed a higher share of organic production (ca. 40%–80% com¬pared to 0%) and lower self-sufficiency values (between 40% and 50%) but performs substantially better in meeting political targets for environmental impact reduction. At the county level, self-sufficiency varies between 2% for densely populated urban districts and 80% for rural counties. These results help policy-makers benchmark and refine their goalsetting regarding regional self-sufficiency and environmental impact reduction, thus ensuring effective policymaking for sustainable community development.

scholarly journals Robust Multi-Objective Optimization of Solid Oxide Fuel Cell–Gas Turbine Hybrid Cycle and Uncertainty Analysis

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Shivom Sharma ◽  
François Maréchal
Keyword(s):  
Fuel Cell ◽  
Uncertainty Analysis ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Environmental Impacts ◽  
Capital Cost ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

Chemical process optimization problems often have multiple and conflicting objectives, such as capital cost, operating cost, production cost, profit, energy consumptions, and environmental impacts. In such cases, multi-objective optimization (MOO) is suitable in finding many Pareto optimal solutions, to understand the quantitative tradeoffs among the objectives, and also to obtain the optimal values of decision variables. Gaseous fuel can be converted into heat, power, and electricity, using combustion engine, gas turbine (GT), or solid oxide fuel cell (SOFC). Of these, SOFC with GT has shown higher thermodynamic performance. This hybrid conversion system leads to a better utilization of natural resource, reduced environmental impacts, and more profit. This study optimizes performance of SOFC–GT system for maximization of annual profit and minimization of annualized capital cost, simultaneously. For optimal SOFC–GT designs, the composite curves for maximum amount of possible heat recovery indicate good performance of the hybrid system. Further, first law energy and exergy efficiencies of optimal SOFC–GT designs are significantly better compared to traditional conversion systems. In order to obtain flexible design in the presence of uncertain parameters, robust MOO of SOFC–GT system was also performed. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for parametric uncertainty analysis with respect to market and operating conditions, and solution obtained via robust MOO found to be less sensitive.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective
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