Search

• A simulation-based multi-objective optimization framework is proposed. • NSGA-III is modified with parallel computing structure and result-saving archive. • Environmental, economic, and comfort objectives are optimized. • The method is applied to optimize energy retrofit of residential buildings in Iran. • Different scenarios regarding future climate and energy price are investigated. Improving building energy performance is central to promote sustainability and mitigate climate change, but it has proved to be a challenging process requiring investigation of a large domain of energy retrofit measures and contrasting objectives. Besides, the unique characteristic of Iran, namely heavily subsidized energy prices and tiered utility rates, further complicate the problem. This research introduces a novel simulation-based multi-objective optimization approach based on a modification of NSGA-III algorithm, denoted as prNSGA-III, powered by parallel computing design and result-saving archive that offers an increased computational potential. The method is developed by integrating EnergyPlus as energy simulation engine with optimization algorithm coded in MATLAB programming language. A large domain of active, passive, water conservation, and renewable retrofit measures are explored to maximize environmental performance while minimizing thermal discomfort and life cycle cost. The method is applied to a residential building in Tehran, Iran, subjected to different scenarios with reference to future climate change and energy price variation. The results show that implementing suggested energy retrofit measures can yield substantial improvement in environmental performance and curb CO-eq emission by up to 73%, and also reduce thermal discomfort by up to 46%. However, energy-efficient solutions can only be achieved at the expense of increased life cycle cost. Moreover, the result demonstrates that climate change can affect optimal solutions, particularly heating and cooling systems, due to changes in external thermal loads. [ABSTRACT FROM AUTHOR]

Mass vaccination in pandemics is one of the most effective strategies to halt disease transmission. A well-organized vaccination plan is essential for guaranteeing its success in cases with limited vaccines. On the other hand, most vaccines need more than one dose for appropriate immunity, intensifying the vaccine shortage probability and complicating decision-making. This study presents a bi-objective mixed-integer linear model for a vaccine distribution chain problem, simultaneously considering economic and social objectives, with multi-dose vaccination adaptable even for booster doses. Moreover, the compatible vaccines in each vaccination process are considered a mix-and-match strategy to diversify vaccination alternatives and alleviate the vaccine shortage risk. It is shown that the problem is NP-hard, and we develop two heuristic and meta-heuristic algorithms to solve real-size instances in a reasonable time. Both algorithms use problem size reduction and problem decomposition techniques to produce an approximate Pareto front for the problem. The heuristic algorithm employs relax-and-fix and fix-and-optimize techniques, and an epsilon-constraint method to solve the multi-objective problem. The proposed meta-heuristic algorithm is based on the hybrid of two well-known evolutionary algorithms, particle swarm optimization, and genetic algorithm. It also uses a multi-objective framework called PESA-II to generate the Pareto front. Both proposed heuristic and meta-heuristic algorithms can employ parallel computing, which significantly reduces computational time. Finally, we investigate the proposed algorithms' performance using 30 random problem instances and a case study from Iran. The results demonstrate that heuristic and meta-heuristic algorithms have obtained a reasonable solution with a maximum of 6.9% and 16.3% cost objective function gap respectively. The heuristic algorithm is superior to the meta-heuristic in terms of Pareto front quality metrics. • Formulating a model for the vaccine distribution chain considering multi-dose vaccines. • We apply a mix-and-match vaccination strategy. • Introducing a comprehensive social objective function. • We develop two heuristic and meta-heuristic multi-objective algorithms. [ABSTRACT FROM AUTHOR]