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With the development of workshop automation, the complexity of RGV (Rail Guided Vehicle) dynamic scheduling schemes using virtual simulation technology is increasing. For the widely valued intelligent machining systems, machine learning based optimization algorithms can effectively respond to the increasingly complex RGV dynamic intelligent scheduling. In the whole model construction, how to complete the modular design of the intelligent processing system and optimize the solution is the key problem that needs to be solved urgently at present. This paper studied the use of particle swarm optimization to design the RGV dynamic scheduling model, aiming to improve the material processing production efficiency of RGV dynamic scheduling and reduce the system failure rate. Through problem modeling, solution and simulation experiment analysis, this paper applied particle swarm optimization based on machine learning, combined with RGV structure modular design and task parameter test set samples. According to the data results, the following conclusions can be drawn from the discussion. Under the background of intelligent logistics system, the RGV dynamic scheduling model using particle swarm optimization had higher material processing production efficiency than the traditional scheduling method in all job test samples, and the average increase was 13.25%. Meanwhile, in terms of system failures, optimization algorithms were better than traditional scheduling methods, with an average reduction of 4.6%. This shows that the RGV dynamic scheduling model based on particle swarm optimization has a better practical application effect. [ABSTRACT FROM AUTHOR]

This paper improves a dynamic scheduling model, Scaper, by including dependence between agents' mode choice and activity purpose. Observed differences in mode share across activity purposes are likely caused both by factors endogenous to the model such as differing levels of service by time of day or geography, and by factors which cannot be represented endogenously with the data available, such as perceived travel needs. The updated model introduces joint purpose- and mode-dependent parameters to account for these exogenous factors. The model improves on a reference model without joint parameters on mode share predictions of activities of different purposes, providing advantages for use in activity purpose-sensitive policy evaluation. [ABSTRACT FROM AUTHOR]

Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users. The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them. The proposed system introduced three -threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding). Along with it, task merging enables minimum energy consumption and response time. The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM). With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models. The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models. The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration. [ABSTRACT FROM AUTHOR]

With growing concern over global warming and increasing fuel costs, manufacturing facilities are seeking strategic and operational changes to achieve energy efficiency in production. On-site energy generation provides a greener and less expensive source of energy but its highly stochastic and intermittent nature makes production planning very complex. Demand Side Management (DSM) for real-time operation has emerged as an effective tool for carbon footprint reduction. In DSM, a local controller installed at the manufacturing facility uses an intelligent algorithm to make dynamic decisions for scheduling production line electrical loads. In this paper, we propose a framework for the intelligent algorithm in a job shop manufacturing facility aimed at minimizing the total electricity cost such that the total order delays are controlled. We consider a manufacturing facility with a wind mill as a source of on-site renewable electricity generation. The intelligent algorithm makes sequential decisions based on weighted objective function involving electricity cost and delay virtual queues while using different information e.g. historical decisions, current state and near future energy expectations from the wind mill. Job shop scheduling problems with traditional objective functions are NP Hard and the dimension of energy efficiency makes the problem more complicated. The intelligent algorithm developed in this paper uses Recurrent Neural Network for predicting electricity from the wind mill for the future time slots. Simulation results confirm that the proposed online algorithm framework for job shop scheduling is an effective DSM approach, outperforming the current industry practices and achieves results comparable to benchmark solutions. [ABSTRACT FROM AUTHOR]

In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. This paper is an extension of [20]. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGV) with different carrying capacities are required for complex material handling in smart factories, which causes resource waste. To minimize the delay and reduce the cost of logistics systems, this paper proposes a dynamic scheduling method for self-organized AGVs (SAGV) in production logistics systems, where multiple identical SAGVs can communicate and freely combine with others as one vehicle to perform one task. Using an improved gene expression programming to learning dynamic dispatching rules, experimental results show that dispatching rules learned are efficient and the cost of logistic systems by using SAGVs is significantly reduced. [ABSTRACT FROM AUTHOR]

"Wash hands frequently, reduce aggregation, and wear masks" is an important measure for the prevention and control of the new crown pneumonia epidemic. Rail transit is the basic means of transportation to ensure the daily travel of citizens. Carriages and stations are both densely populated places. Reducing the density of carriages and platforms is an urgent problem for rail transit operations in the epidemic. Therefore, this paper proposes a method for dynamic marshalling of trains based on virtual coupling in a major epidemic situation, describes in detail the operation mode of virtual coupling trains, and establishes a marshalling planning model based on passenger flow to optimize the scheduling of virtual coupling trains to reduce passenger density at stations. Then, combined with the virus infection probability model and social force-based passenger movement model, the infection risk of the entire process of passenger subway travel under virtual coupling was analyzed. After that, Matlab was used to simulate the infection analysis under the virtual coupling system and compare it with the traditional communication based train control system. The risk of infection during the entire journey of a passenger on the subway is less than that of CBTC. Finally, according to the results of simulation analysis, effective measures can be given that can be used in conjunction with virtual coupling to reduce the risk of infection. [ABSTRACT FROM AUTHOR]

Many of the problems of manufacturing systems, including scheduling, are NP-hard and therefore cannot be solved and optimized in real time as required in the real world. This paper investigates the possibility of controlling manufacturing systems while maximizing the enterprise profit in a stochastic and dynamically varying environment. In this research, we developed a multi-agents system that operates on the factory floor level and directly controls the manufacturing system. The paper examines the proposed multi-agents system, demonstrates its feasibility by simulation under various experimental settings, presents important findings and excellent results, and draws conclusions. [ABSTRACT FROM AUTHOR]

Fluctuating customer demands, expected short delivery times and the need for quick order confirmation creates a fast-paced scheduling environment for modern production systems. In this turbulent scene, using the data provided by intelligent elements of cyber-physical production systems opens up new possibilities for dynamic scheduling. The paper introduces a reinforcement learning approach, in particular Q-Learning, to reduce the average lead-time of production orders in a job-shop production system. The intelligent product agents are able to choose a machine for every production step based on real-time information. A performance comparison against standard dispatching rules is given, which shows that in the presented dynamic scheduling use-cases the application of RL reduces the average lead-time. [ABSTRACT FROM AUTHOR]

Scheduling problems are a classic type of optimization problems in the manufacturing domain, such as job shop scheduling, flexible job shop scheduling, and distributed job shop scheduling problems. Especially, the dynamic task scheduling problem is closer to the requirements of real manufacturing systems than the static scheduling problem. In recent years, with the deeper application of the Internet of Things, big data, and cloud platform technologies, manufacturing systems have evolved from job shops to networked, collaborative and intelligent manufacturing systems. Smart manufacturing scheduling has some characteristics compared with job shop scheduling not only because of the larger number of tasks and services, but also because of the dynamic state of services and uncertainties. In this paper, we analyze the smart manufacturing service scheduling problem and give its mathematical description. Then a deep reinforcement learning-based method is proposed to minimize the maximum completion time of all tasks. In the system framework of the proposed method, the agent, environment, as well as the interaction between them, are designed. The queue times of all candidate services are considered as the system state, and the maximum queue time at the current moment is considered as the target value. Besides, we use two networks the prediction network and the target network to learn the prediction value and the target value, respectively. Two case studies are used to show the efficiency of the considered problem and the proposed method. [ABSTRACT FROM AUTHOR]

In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration and revision of pre-established schedules. The objectives of the research reported in this paper are to respond to changes in the problem, to solve the new problem faster and to use some parts of the previous solution for the next problem. In this paper, based on Network Simplex Algorithm, a dynamic algorithm, which is called Dynamic Network Simplex Algorithm (DNSA), is presented. Although the traditional network simplex algorithm is at least one hundred times faster than traditional simplex algorithm for Linear Programs (through specialization), for dynamic scheduling with large scale problems it still takes time to make a new graph model and to solve it. The overall approach of DNSA is to update the graph model dynamically and repair its spanning tree by some strategies when any changes happen. To test the algorithm and its performance, an application of this algorithm to Dynamic Scheduling of Automated Guided Vehicles in the container terminal is used. The dynamic problem arises when new jobs are arrived, the fulfilled jobs are removed and the links or junctions are blocked (which results in distances between points being changed). The results show considerable improvements, in terms of reducing the number of iterations and CPU time, to solve randomly generated problems. [ABSTRACT FROM AUTHOR]

Abstract: Aimed at the imbalance problem of resource capacity in Production Scheduling System, this paper uses Production Scheduling System based on multi-agent which has been constructed, and combines the dynamic and autonomous of Agent; the bottleneck problem in the scheduling is solved dynamically. Firstly, this paper uses Bottleneck Resource Agent to find out the bottleneck resource in the production line, analyses the inherent mechanism of bottleneck, and describes the production scheduling process based on bottleneck resource. Bottleneck Decomposition Agent harmonizes the relationship of job''s arrival time and transfer time in Bottleneck Resource Agent and Non-Bottleneck Resource Agents, therefore, the dynamic scheduling problem is simplified as the single machine scheduling of each resource which takes part in the scheduling. Finally, the dynamic real-time scheduling problem is effectively solved in Production Scheduling System. [Copyright &y& Elsevier]

Nowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result. [ABSTRACT FROM AUTHOR]

Just in time (JIT) manufacturing is one of the main methodologies used to enhance manufacturers’ competitiveness through inventory and lead time reduction. However implementing JIT has some challenges, e.g. lack of required information sharing or communication between stakeholders, insufficient sound action or planning system etc. Internet of Things (IoT) technology has the potential to be used for acquiring data and information in real time to facilitate dynamic JIT manufacturing. This paper presents a research on using IoT based solution to enhance JIT manufacturing. The general challenges of JIT implementation are identified first, then an IoT based solution is proposed to address the JIT challenges in a selected case study. A framework to support the proposed IoT solution is developed and its implementation steps are suggested. [ABSTRACT FROM AUTHOR]

This paper presents the design and prototype implementation of a service based control system responsible for the material supply operations in assembly lines. Mobile Assistant Units (MAUs) are responsible for the transportation of the consumables from the warehouses to the production stations. A service oriented, web based software has been developed for the monitoring of the shop floor status and the parts’ supply dynamic scheduling, based on time and inventory. The proposed system has been applied to a case from the automotive sector demonstrating the ease of deployment and efficiency in the co-ordination of the mobile units’ operation. [ABSTRACT FROM AUTHOR]

We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to untie the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center. [ABSTRACT FROM AUTHOR]

Commodity processors are comprised of several CPU cores and one integrated GPU. To fully exploit this type of architectures, one needs to automatically determine how to partition the workload between both devices. This is specially challenging for irregular workloads, where each iteration's work is data dependent and shows control and memory divergence. In this paper, we present a novel adaptive partitioning strategy specially designed for irregular applications running on heterogeneous CPU-GPU chips. The main novelty of this work is that the size of the workload assigned to the GPU and CPU adapts dynamically to maximize the GPU and CPU utilization while balancing the workload among the devices. Our experimental results on an Intel Haswell architecture using a set of irregular benchmarks show that our approach outperforms exhaustive static and adaptive state-of-the-art approaches in terms of performance and energy consumption. [ABSTRACT FROM AUTHOR]

Abstract: This study experiments with the manufacturing efficiency by layout change of a factory by means of agent-based autonomous production scheduling, using the virtual factory on a multi-agent simulation system. As infrastructure software for agent based simulation, the artisoc(c) is used. In this virtual factory, three types of agents are equipped. Users can alter a configuration such as input new jobs, adjusting a machine setting, etc, with monitoring conditions of agents. As a result, by adjustment of the agent's behavior with shop floor detail, the assembly schedule becomes more effective. The experiment is carried out to show that local negotiations contribute to global optimization when resources in the factory are effectively distributed and shared. In this paper, the effectiveness of job-list cleanup method is shown. In addition, the scheduling influence is simulated by the communication range of agents. A part agent chooses a machine, by the length of a job list and the conveyance cost. But the communication cost between agents increases with the size of the communication range. From experimental results, when extending the communication range simply, the conclusion is reached that optimization did not necessarily result in progress. [Copyright &y& Elsevier]

Abstract: Flexible manufacturing system (FMS) is a highly dynamic system with considerable product mix and manufacturing environment is also dynamic in nature. With these dynamic scenarios of manufacturing, static scheduling of FMS will not yield the expected results. Scheduling research has largely ignored this problem, focusing instead on optimization of static schedules. In this paper, an attempt has been made to study the performance of an FMS scheduling with machine breakdown and compared without breakdown using various dispatching rules. The results show the proposed RIT/TPT performs better than SPT & FIFO on the performance of FMS. [Copyright &y& Elsevier]

Abstract: This paper presents a dynamic scheduling model for the steelmaking-continuous casting (SCC) with a consideration of quality disturbance. The constraints with dynamic variation of this model are described by formal language of Dynamic Constraint Satisfaction Problem (DCSP). To keep the solution consistency, the objective to minimize the difference in machine assignment, starting time and processing time between the new and the original schedules is proposed. The disturbances from the component and temperature of molten steel are analyzed on the aspects of the impact on scheduling, the disposal strategy, and the mapping to the DCSP model. The establishment of this model provides valuable instructions for developing an efficient dynamic scheduling algorithm. [Copyright &y& Elsevier]

Abstract: Scheduling for the flexible job shop is very important in the fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problems with traditional optimization approaches owing to the high computational complexity. In this paper, dynamic scheduling in flexible job shop is considered. The dynamic status intensifies the complexity of this problem. Nevertheless, there are many industries which have a dynamic status. Two objectives are considered to make a balance between efficiency and stability of the schedules. A multi-objective mathematical model for the considered problem is developed. Since the problem is well known as NP-hard, a meta-heuristic algorithm based on the genetic algorithm is developed. Numerical experiments are used to evaluate the performance and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm is capable to achieve the optimal solutions for the small size problems and near optimal solutions for the medium size problems. [Copyright &y& Elsevier]

In this paper, we deal with dynamic scheduling components integrating new Quality of Service (QoS) functionalities into a Linux-based real-time operating system. In our approach, periodic tasks allow occasional deadline violations within given bounds specified according to the Skip-Over task model. Hence, every task has a minimal QoS guarantee which is expressed by the ratio of periodic task instances which must complete before their deadline. The work stated here provides two on-line scheduling algorithms, namely RLP and RLP/T, which enhance the existing Skip-Over algorithms. More specifically, the proposed algorithms aim at improving the actual QoS observed for periodic tasks (which is always greater or equal to the QoS guarantee). These novel scheduling techniques rely on the EDL (Earliest Deadline as Late as possible) scheduling strategy. Simulation results show the performance of RLP and RLP/T with respect to basic Skip-Over algorithms. Finally, we present the integration of these QoS scheduling services into CLEOPATRE open-source component library, a patch to Linux/RTAI. [ABSTRACT FROM AUTHOR]

Abstract: Although a considerable amount of efforts has been devoted to developing optimum negotiation for dynamic scheduling, most of them are inappropriate for the non-cooperative, self-interested participants in a distributed project for practical purpose. In this paper, an agent-based approach with a mutual influencing, many-issue, one-to-many-party, compensatory negotiation model is proposed. In the model, the activity agents possess various negotiation tactics and strategies formed by respective self-interested owner''s subjective preference, aim to find the contracts of schedule adjustment mutually acceptable to respective participant''s acquaintance while encountering conflicts over rescheduling settlement. In order to find the fitting negotiation strategies that are optimally adapted for each activity agent, an evolutionary computation approach that encodes the parameters of tactics and strategies of an agent as genes in GAs is also addressed. In the final, a prototype with a case discussed in researches is evaluated to validate the feasibility and applicability of the model, and some characteristics and future works are also exhibited. [Copyright &y& Elsevier]

Dynamic scheduling strategies employed in real-time operating systems dynamically assign priorities to processes based on the time remained to some predefined deadlines, laxities, or other run-time metrics. Although, theoretically, these strategies do not restrict CPU utilization, in reality, they produce some run-time overhead, which is mainly due to the time required to sort processes in the ready queue each time a process is preempted. This paper tries to attack the above shortcoming by proposing and evaluating two alternative approaches to optimizing the structure of the ready queue in order to eliminate the need for a traditional sorting algorithm and consequently minimize the run-time overhead. These approaches are arbitrarily based on the mathematical properties of laxities (although they can well be based on the mathematical properties of any other dynamic priority variables considered in the system). It is shown that these approaches can. reduce the time complexity of the run-time overhead of dynamic scheduling strategies, in terms of the number of active processes in the ready queue (n), from O(nlogn) to O(n). This can considerably improve the performance of real-time operating systems. [ABSTRACT FROM AUTHOR]

A classical problem in the field of distributed computation and parallel processing concerns reasonable allocation of resources among computational-intensive data flows. We introduce the generalized cluster in this paper for processing large-scale scientific computations and to further explore a consecutive cooperation game-based dynamic scheduling strategy. We construct an abstract generalized cluster environment and summarize the types of data flows. We then convert the multi-objective scheduling problem into a multi-objective expectation function-based continuous cooperation game model and discuss its strategy and a solution for its kernel. We also propose a dynamic scheduling mechanism to address the instability of generalized clusters to ensure a reasonable, real-time adjusting allocation scheme by monitoring and compensating appropriately. Finally, we apply our method to a real world application to demonstrate our successful scheduling strategy that achieves superior results for overall cost, cost-performance index, and mean run time when compared with other methods. • Designed the continuous bidding game algorithm to solve the optimal scheduling scheme in GC. • A monitoring mechanism based on the communication agent used to monitor the member status. • The compensation mechanism (CM) realized a dynamic and reasonable scheduling scheme. [ABSTRACT FROM AUTHOR]