There has been a rapid increase in usage of unmanned aerial vehicles (UAVs) in different application areas that are unfriendly to humans. These UAVs have been used in Aerial Mesh Networks (AMNs) that act as backbone n...
详细信息
There has been a rapid increase in usage of unmanned aerial vehicles (UAVs) in different application areas that are unfriendly to humans. These UAVs have been used in Aerial Mesh Networks (AMNs) that act as backbone network to support communication in a post-disaster scenario. However, there may be limited available number of UAV nodes that need to be utilized efficiently to improve the performance of such networks. Here, we consider three important objectives of the network i.e. target coverage, Quality of Service and energy consumption by the network that need to be optimized efficiently to improve the performance. Yet, it is a grueling task to optimize all of these conflicting objectives at the same time, which is affected by the height of UAVs. To optimize more than one conflicting objectives, we used metaheuristic based multi-objective optimization algorithms i.e. Multi-objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic algorithmii (NSGA-ii), Strength pareto Evolutionary algorithm 2 (SPEA2) and pareto envelope-based selection algorithm ii (PESA-ii), which suggest the optimal placement of UAVs. These algorithms are compared based on four performance metrics i.e. generational distance, diversification metric, spread of non-dominant solutions and percentage of domination in three different scenarios. The rigorous experiments are performed by each algorithm in small, medium and large-scale scenarios to compare their results. The ANOVA's validation test suggests that SPEA2 performs better than others in small-scale scenarios while NSGA-ii performs better than others in medium and large-scale scenarios. However, MOPSO has lowest average execution time, after that NSGA-ii, then PESA-ii and then SPEA2.
Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significan...
详细信息
Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario -based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the epsilon-constraint method, the Non-dominated Sorting Genetic algorithm-ii (NSGA-ii), the Strength pareto Evolutionary algorithm 2 (SPEA2), and the pareto envelope-based selection algorithm ii (PESA-ii) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-ii is the fastest, while their objective function values are nearly the same.
暂无评论