Storage assignment optimization for fishbone robotic mobile fulfillment systems

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

A Novel adaptive genetic algorithm for Dynamic Vehicle Routing Problem with Backhaul and Two-dimensional loading constraints, A Case in Tunisian Posta

In this paper, we consider an extension of the Dynamic Vehicle Routing Problem with Backhauls integrated with two-dimensional loading problem called DVRPB with 2D loading constraints (2L-DVRPB). In the VRPB, a vehicle can deliver (Linehaul) then collect goods from customers (backhaul) and bring back to the depot. Once customer demand is formed by a set of two-dimensional items the problem will be treat as a 2L-VRPB. The 2L-VRPB has been studied on the static case. However, in most real-life application, new customer requests can be happen over time of backhaul and thus perturb the optimal routing schedule that was originally invented. This problem has not been analysed sofar in the literature. The 2L-DVRPB is an NP-Hard problem, so, we propose to use a Genetic algorithm for routing and a packing problems. We applied our approach in a real case study of the Regional Post Office of the city of Jendouba in the North of Tunisia. Results indicate that the AGA approach is considered as the best approach in terms of solutions quality for a real world routing system.

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transportation of equipment during the operation, and the reduction in the completion time of the task. Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, this paper discusses how to use delivery task information and AGVs spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithm, and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information. In the framework, firstly, the AGVs scheduling process is defined as a Markov decision process, which analyzes the system’s spatiotemporal state information in detail, introduces assignment heuristic rules, and rewards the reshaping mechanism in order to realize the decoupling of the model and the AGVs dynamic scheduling problem. Then, a multi-channel matrix is built to characterize space–time state information, the CNN is used to generalize and approximate the action value functions of different state information, and the DDPG algorithm is used to achieve the best AGV and container matching in the decision stage. The proposed model and algorithm frame are applied to experiments with different cases. The scheduling performance of the adaptive genetic algorithm and rolling horizon approach is compared. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%.

Optimization of Port Emergency Materials Dispatch with Time Windows Constraints under Uncertainties

Uncertainties exist and affect the actual port production. For example, at the beginning of 2020, the sudden outbreak of COVID-19 seriously affected terminal production and increased the short-term pressure of handling at container terminals. Consequently, a large number of containers were stacked at terminals, and the problem of terminal congestion became more serious. To solve the congestion problem of container terminals and ensure the priority dispatch of emergency materials, this study uses the optimized arrival patterns of external trucks and a priority dispatch strategy for emergency materials to establish a bilevel optimization model for container terminals and proposes a chaotic genetic algorithm based on logistic mapping as a solution. Through numerical experiments, the algorithm proposed in this study was compared with the genetic algorithm and adaptive genetic algorithm. The experimental results show that the model and algorithm proposed in this study can effectively reduce the total cost of containers in a terminal while ensuring the priority dispatch of emergency materials, reducing the overlapping part of the time window, optimizing the arrival mode of external trucks, and reducing the waiting time of external trucks, effectively alleviating the terminal congestion problem.

Research on the Application of Adaptive Genetic Algorithm in Software Unit Security Test Simulation

At present, the security test and simulation of software unit mainly focuses on several links, such as software control structure amelioration, software process alternating quantity model control and model inspection tech, and there are still many shortcomings, such as high missed inspection rate, difficult to effectively guarantee the needs of practice, etc. Based on this, this paper first analyses the purpose and principle of software unit security test and simulation, then studies the utilization of ameliorated genetic algorithm in software unit security test simulation, and finally gives the simulation results analysis of software unit security test based on AGA.

Optimal Operation Of Integrated Energy System Considering Demand Response

Due to the high cost of energy storage part in traditional integrated energy systems, the demand response effect is poor. The paper proposes electrolytic water hydrogen production technology and applies it to the optimal operation of integrated energy system. By optimizing the operating cost of the system through adaptive genetic algorithm, we show that when the load matching degree was increased from 50% to 70%, the system operating cost was reduced by about 15.8%, and the carbon displacement was decreased by about 35%. System operating costs, carbon emissions, and the amount of electrolytic water systems involved in the demand response have all decreased.