Rescheduling of Distributed Manufacturing System with Machine Breakdowns

This study attempts to explore the dynamic scheduling problem from the perspective of operational research optimization. The goal is to propose a rescheduling framework for solving distributed manufacturing systems that consider random machine breakdowns as the production disruption. We establish a mathematical model that can better describe the scheduling of the distributed blocking flowshop. To realize the dynamic scheduling, we adopt an “event-driven” policy and propose a two-stage “predictive-reactive” method consisting of two steps: initial solution pre-generation and rescheduling. In the first stage, a global initial schedule is generated and considers only the deterministic problem, i.e., optimizing the maximum completion time of static distributed blocking flowshop scheduling problems. In the second stage, that is, after the breakdown occurs, the rescheduling mechanism is triggered to seek a new schedule so that both maximum completion time and the stability measure of the system can be optimized. At the breakdown node, the operations of each job are classified and a hybrid rescheduling strategy consisting of “right-shift repair + local reorder” is performed. For local reorder, we designed a discrete memetic algorithm, which embeds the differential evolution concept in its search framework. To test the effectiveness of DMA, comparisons with mainstream algorithms are conducted on instances with different scales. The statistical results show that the ARPDs obtained from DMA are improved by 88%.

Research on Dynamic Scheduling of Unbalanced Supply and Demand of the SROC Based on the Customers Priority Principle

The problem of imbalance between supply and demand in car-sharing scheduling has greatly restricted the development of car-sharing. This paper first analyzes the three supply and demand modes of car-sharing scheduling systems. Secondly, for the station-based with reservation one-way car-sharing problem (SROC), this article establishes a dynamic scheduling model under the principle of customer priority. The model introduces balance coefficients to predict the balance mode, and systematically rebalance the fleet networks in each period. In the case of meeting customer needs, the model objective function is to maximize the total profit and minimize the scheduling and loss costs. Then, in view of the diversity and uncertainty of scheduling schemes, a scheme information matrix is constructed. In the iterative process of genetic algorithm, individuals are selected and constructed according to the pheromone matrix, and evolution probability is proposed to control the balance between global search and local search of genetic algorithm. Finally, the data of Haikou City is used for simulation experiment.

Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.

Can LTE-A Support Real-Time Smart Meter Traffic in the Smart Grid?

The chapter investigates the scheduling load added on a long-term evolution (LTE) and/or LTE-Advanced (LTEA) network when automatic meter reading (AMR) in advanced metering infrastructures (AMI) is performed using internet of things (IoT) deployments of smart meters in the smart grid. First, radio resource management algorithms to perform dynamic scheduling of the meter transmissions are proposed and shown to allow the accommodation of a large number of smart meters within a limited coverage area. Then, potential techniques for reducing the signaling load between the meters and base stations are proposed and analyzed. Afterwards, advanced concepts from LTE-A, namely carrier aggregation (CA) and relay stations (RSs) are investigated in conjunction with the proposed algorithms in order to accommodate a larger number of smart meters without disturbing cellular communications.

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%.

Digital Twin-Driven Human Robot Collaboration Using a Digital Human

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

ACPEC: A Resource Management Scheme Based on Ant Colony Algorithm for Power Edge Computing

With the development of power IoTs (Internet of Things) technology, more and more intelligent devices access the network. Cloud computing is used to provide the resource storage and task computing services for power network. However, there are many problems with traditional cloud computing such as the long-time delay and resource bottleneck. Therefore, in this paper, a two-level resource management scheme is put forward based on the idea of edge computing. Furthermore, a new task scheduling algorithm is presented based on the ant colony algorithm, which realized the resource sharing and dynamic scheduling. The data of simulation show that this algorithm has a good effect on the performance of task execution time, power consumption, and so on.