No abstract

The cross-aisles shuttle-based storage/retrieval system has not only storage function but also the sorting function and makes full use of warehouse space to achieve high-density storage. It uses "part-topicker" order picking mode to respond quickly to orders and shorten sorting time. In this paper, through the analysis of the system, an effective evaluation method for the efficiency and time of system picking under a single instruction operation cycle is presented. The objective function of system minimum cost under the condition of satisfying customer's demand is constructed. The objective function is solved by an improved particle swarm algorithm based on the optimized initial particle swarm optimization. By optimizing, we can find the optimal configuration of the system (i.e., the number of tiers, number of aisles and number of bays, number of picking stations, and number of lifts with minimal system cost). Finally, the impact of different configurations on system performance is summarized. This method can guide the design planner to design a more reasonable system under minimum cost control.INDEX TERMS shuttle-based storage/retrieval system; performance analysis; system design; part-topicker; improved particle swarm optimization.

As an important intelligent transportation equipment of logistics, Multi-AGV scheduling is a key problem that restricts the operation efficiency and large-scale expansion of AGV system. This paper analyzes and elaborates three key technologies of AGV scheduling, namely Path Planning, Task Scheduling and Traffic Control Management. The method of Time Window modeling based on physical entity is superior to that based on particle motion. Finally, the trend of AGV scheduling intelligentization is illustrated by combining with the current advanced technology.

Modern electricity networks are increasing in complexity due to the integration of variable renewable energy sources at both transmission and distribution levels. As part of the daily operation and scheduling of such systems, the very short to mediumterm forecasts are a crucial element, providing the expected demand and generation output over a requested period. Artificial neural networks techniques promise can deliver adaptability which overcome the source's variability if the inputs are properly selected. This paper proposes demand and generation forecasting models for distribution systems, each employing a different artificial neural networks architecture. The models are demonstrated on real data from Energie Güssing distribution system. The adaptability of the models to changes in demand during the Covid-19 lockdown is investigated.