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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]

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]

Scheduling in decentralized manufacturing systems is being expanded and transformed to a smart level where manufacturing tasks are executed autonomously. However, achieving self-organized manufacturing resources that can make collaborative decisions remains challenging. This paper presents a novel negotiation model for decentralized scheduling in an autonomous manufacturing system. A variety of emotions represent the willingness of autonomous intelligent agents (IAs) to negotiate for a consensus despite their diverse goals. The proposed communication protocol considers the emotional aspects of the autonomous machine agents in decentralized decision-making. The negotiation model focuses on analyzing the effects of two key emotions: selfishness and cooperation. This study constitutes the basis for experiments on multi-goal machine tasks and artificial emotion mechanism in a flow shop production system. The complexities of negotiation dynamics between IAs, where the interplay of selfish and cooperative strategies influences the outcomes and efficiency of reaching agreements, are explored. The experimental results show that selfish IAs eventually reach agreements through gradual compromise, but cooperative IAs, with their flexibility, can quickly achieve resolutions, especially when bargaining power is balanced. Aligning with the dual concern theory, this study reveals that while selfish IAs can dominate cooperative ones, the cooperative approach often leads to faster and more effective outcomes, regardless of the level of selfishness involved. [ABSTRACT FROM AUTHOR]

Resource scheduling is an important part of military operation, especially in key-point air defense under saturation attack. Many achievements have been made in the field of radar resource scheduling and multi-aircraft scheduling. However, there is little research on the integrated scheduling of detection, tracking and attack, which will greatly increase the resource utilization to deal with the resource shortage problem caused by saturation attacks on key places. In this paper, we propose to autonomously accomplish real-time resource dispatching through end-to-end deep reinforcement learning (DRL), while allowing the commander's intervention to accomplish a variety of complex tactics. First, an integrated scheduling model of detection, tracking and interception is proposed and transformed into a sequential decision problem by introducing a disjunctive graph and a graph neural network (GNN) to extract node features. Subsequently, the Proximal Policy Optimization (PPO) algorithm is applied to learn the air defense environment (ADE), which is modeled as an Markov decision process (MDP). Benefitting from the powerful generalization capability of the policy network, our algorithm can adapt to scheduling missions of different sizes. Moreover, we propose a novel Human-Intelligence collaborative dynamic scheduling framework for emergency response. Simulation results indicate that our algorithm generates high-quality scheduling policies for defense resources, exhibiting superior performance than existing methods. In addition, the dynamic scheduling performance of the human and intelligence collaboration approach in response to multiple contingencies is proven. [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]

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]

• Robust optimization method with dynamic events is proposed. • Reusability of the system and repeatability of the processing tasks are considered. • A dynamic event response strategy (DERS) is proposed. • A particle swarm arithmetic optimization (PSAO) is used to solve the problem. • The results show that the method can effectively improve the robustness performance. This paper focuses on the production scheduling problem of flexible job shops. In the production process of flexible job shop, there are dynamic events such as machine breakdowns or new job arrivals, which will interfere with the implementation of scheduling scheme and reduce the stability of the system. In response to this problem, this paper proposes a new robust optimization method that considers dynamic events and designs two indicators for evaluating the robustness of the system, namely the reusability of the system and the reproducibility of processing tasks. Two indicators are used to evaluate the comprehensive reusability of the system jointly. In the process of system rescheduling, this paper proposes a dynamic event response strategy (DERS) considering the comprehensive reusability of the system and establishes a multi-objective optimization model considering the total energy consumption, the makespan, and the comprehensive reusability of the system. In order to solve the model efficiently and obtain the optimal Pareto frontier, a multi-objective particle swarm arithmetic optimization (PSAO) is proposed in this paper. Finally, this paper designs experiments based on standard data cases, solves them based on this model and compares them with other algorithms. The final results show that in the flexible job-shop scheduling process, this method can effectively adjust the scheduling plan to respond to dynamic events to achieve stable scheduling in an uncertain environment. [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]

• The dynamic distributed reconfigurable flowshop scheduling problem is studied via DRL. • The DRL-based intelligent scheduling and reconfiguration systems are modelled. • An expected deep Q-network algorithm with precise state-action values is proposed. • The DRL-based training and execution processes for the studied problem are proposed. • Our proposed expected deep Q-network outperforms some popular DRL algorithms. Distributed manufacturing has become common in the globalized production environment. Facing the tendency of mass customization, distributed workshops are required to have the reconfiguration capability to produce multiple products. Based on the scheduling demands of distributed workshops, this paper studied the dynamic distributed reconfigurable flowshop scheduling problem (DRFSP) with new job arrivals using deep reinforcement learning (DRL) for the first time. The DRL-based intelligent scheduling and reconfiguration systems are modeled by designing efficient rewards, actions, and state features for both scheduling and reconfiguration agents. A new state feature normalization approach is proposed to address the variability in production configurations and dynamics. A novel expected deep Q-network (EDQN) algorithm is proposed to provide more precise state-action values rather than using estimations. Moreover, the DRL-based training and execution procedures for the dynamic DRFSP are provided by combining decision-making, agent learning, and reconfigurable production. Training curves validate the reasonableness of system modeling and the good learning efficiency of the EDQN algorithm. Extensive comparison experiments show that our EDQN algorithm outperforms three popular DRL algorithms and four well-known priority dispatching rules (PDRs) by around 20 % and 89 % respectively, with good generalization ability and time efficiency. The average decision time under dynamics is only 0.65 ms, which enables real-time scheduling. [ABSTRACT FROM AUTHOR]

• Solve a multi-equipment integrated scheduling problem in a U-shaped port. • Consider double-cycling mode and uncertain operation time with a cascade effect. • Incorporate anti-cascade effect into a new robust-proactive scheduling framework. • Integrate a mixed-integer programming model with a bi-objective solution algorithm. • Examine real case study and explore implications in port or other industries. The integrated scheduling of quay cranes, internal vehicles, and yard cranes in container terminals aims to improve port operations and often requires scheduling robustness under uncertainty with cascade effects. In container terminal operations, uncertainty in equipment operating time poses challenges to effective scheduling, as even small fluctuations can create cascade effects throughout the operations, rendering the original schedule ineffective. This research aims to develop a new method that enables a balance between optimization and robustness in container terminal scheduling. Additionally, the double-cycling operation mode and U-shaped port layout, known for their improved efficiency in container terminals, are gaining increasing attention and hence are incorporated into this study. It creates a three-stage hybrid flow shop scheduling problem with bi-directional flows, waiting time, and uncertain operation time. To address the complex problem, a mixed integer programming model is proposed to characterize the integrated scheduling problem, and an index based on complex network structure entropy is designed to evaluate the anti-cascade effect as well as the robustness of the schedule. The index and makespan serve as the bi-objectives, transforming the original problem into a bi-objective optimization one. The non-dominated sorting genetic algorithm-Ⅱ with appropriate coding and decoding rules is utilized to solve the model and obtain a set of Pareto frontier solutions. The feasibility of the new method is verified through a real case analysis. Specifically, comparative analysis with basic stochastic programming, basic robust optimization, triangular fuzzy programming, and maximum gap method are used to demonstrate the effectiveness of the new method. The paper also provides insightful practical implications for port managers, and the genericity of the method could also contribute to its practical values spreading to a wider scope of beneficiaries, such as manufacturing warehousing and distribution management. [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]

Abstract: The paper presents an adaptive iterative distributed scheduling algorithm that operates in a market-based production control system. The manufacturing system is agentified, thus, every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior and search space reduction, a triple-level learning mechanism is proposed. The top level of learning incorporates a simulated annealing algorithm, the middle (and the most important) level contains a reinforcement learning system, while the bottom level is done by a numerical function approximator, such as an artificial neural network. The paper suggests a cooperation technique for the agents, as well. It also analyzes the time and space complexity of the solution and presents some experimental results. [Copyright &y& Elsevier]

This paper describes a model predictive control strategy to find the optimal decision variables to maximize profit in supply chains with multiproduct, multiechelon distribution networks with multiproduct batch plants. The key features of this paper are: (1) a discrete time MILP dynamic model that considers the flow of material and information within the system; (2) a general dynamic optimization framework that simultaneously considers all the elements of the supply chain and their interactions; and (3), a rolling horizon approach to update the decision variables whenever changes affecting the supply chain arise. The paper compares the behavior of a supply chain under centralized and decentralized management approaches, and shows that the former yields better results, with profit increases of up to 15% as shown in an example problem. [Copyright &y& Elsevier]

Dynamic scheduling has received widespread attention from academia and industry due to the increasing complexity in manufacturing systems. Highly dynamic and adaptable behaviours are necessary for an improved production efficiency in unstable and constantly changing environments. This paper proposes an agent-based hybrid tabu-search heuristic (AB-TSH) to solve dynamic flexible job-shop scheduling problems. The solution is fully implemented and tested in an industrial environment for seven distinct dynamic scenarios derived from a static scenario using the benchmark of AIP-PRIMECA Flexible Manufacturing System. The scheduling plan is obtained by exploitation using a greedy heuristic on tabu search solution points. The hybrid tabu-search heuristic is supported by a multi-agent system that react and re-optimize the scheduling plan in case of disturbances and unpredicted events. The proposed solution demonstrated superior performance in terms of makespan in the majority of dynamic scenarios tested when compared to previous studies in the literature. This improved performance is attributed to the solution's ability to combine the scheduling plan both statically and dynamically. • Agent based tabu-search heuristic is proposed to solve a dynamic flexible job-shop problems. • A multi-agent architecture is fully-implemented on the JADE platform for real-time scheduling. • Real-time reactive capabilities of agent-based solution are combined with the planning capabilities of a scheduling algorithm. • Deployment of the solution in an industrial prototype within an intelligent automation laboratory. [ABSTRACT FROM AUTHOR]

The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization. [ABSTRACT FROM AUTHOR]

Welding is one of the most important technologies in manufacturing industries due to its extensive applications. Welding scheduling can affect the efficiency of the welding process greatly. Thus, welding scheduling problem is important in welding production. This paper studies a challenging problem of dynamic scheduling in a real-world welding industry. To satisfy needs of dynamic production, three types of dynamic events, namely, machine breakdown, job with poor quality and job release delay, are considered. Furthermore, controllable processing times (CPT), sequence-dependent setup times (SDST) and job-dependent transportation times (JDTT) are also considered. Firstly, we formulate a model for the multi-objective dynamic welding scheduling problem (MODWSP). The objectives are to minimize the makespan, machine load and instability simultaneously. Secondly, we develop a hybrid multi-objective grey wolf optimizer (HMOGWO) to solve this MODWSP. In the HMOGWO, a modified social hierarchy is designed to improve its exploitation and exploration abilities. To further enhance the exploration, genetic operator is embedded into the HMOGWO. Since one characteristic of this problem is that multiple machines can handle one operation at a time, the solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. To evaluate the effectiveness of the proposed HMOGWO, we compare it with other well-known multi-objective metaheuristics including NSGA-II, SPEA2, and multi-objective grey wolf optimizer. Experimental studies demonstrate that the proposed HMOGWO outperforms other algorithms in terms of convergence, spread and coverage. In addition, the case study shows that this method can solve the real-world welding scheduling problem well. [ABSTRACT FROM AUTHOR]

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]

To address the uncertainty of renewable energy in unit commitment, an adjustable uncertainty set of renewable energy is introduced and unit commitment is scheduled to guarantee the safe operation of the power system in this set. However, the operational risk arises when renewable energy falls out of the adjustable uncertainty set and this risk should be evaluated to determine the adjustable uncertainty set. In this paper, a distributionally robust optimization approach is proposed to calculate the risks of load shedding and renewable energy curtailment at the lower and upper bounds of the adjustable uncertainty set. After the evaluation of the operational risk, the day-ahead unit commitment with the adjustable uncertainty set is determined together with the demand response reserve in the reduction of the operational risk. In real time, the dynamic demand response is proposed to further reduce the operational risk. The proposed distributionally robust method with the dynamic demand response for dealing with the uncertainty of renewable energy is verified on the IEEE 6-bus, 30-bus, and 118-bus systems. Simulation results show that the proposed method reduces the cost of the unit commitment and the operational risk. • The adjustable uncertainty set and demand response are used to tackle the uncertainty. • A distributionally robust optimization approach is proposed to evaluate the risk. • A dynamic demand response scheduling is proposed to reduce the operational risk. [ABSTRACT FROM AUTHOR]

This paper presents an agent-based fuzzy constraint-directed negotiation (AFCN) mechanism to solve distributed job shop scheduling problems (JSSPs). The scheduling problem is modelled as a set of fuzzy constraint satisfaction problems (FCSPs), interlinked by inter-agent constraints. Each FCSP represents the perspective of the participants and is governed by autonomous agents. The novelty of the proposed AFCN is to bring the concept of a fuzzy membership function to represent the imprecise preferences of task start time for job and resource agents. This added information sharing is crucial for the effectiveness of distributed coordination. It not only can speed up the convergence, but also enforce a global consistency through iterative exchange of offers and counter-offers. The AFCN mechanism can also flexibly adopt different negotiation strategies, such as competitive, win–win, and collaborative strategies, for different production environments. The experimental results demonstrate that the proposed model can provide not only high-quality and cost-effective job shop scheduling (i.e., comparable to that of centralized methods) but also superior performance in terms of the makespan and average flow time compared with other negotiation models for agent-based manufacturing scheduling. As a result, the proposed AFCN mechanism is flexible and useful for distributed manufacturing scheduling with unforeseen disturbances. [ABSTRACT FROM AUTHOR]

In this paper, a simulated annealing and seven dispatching rule based complete rescheduling approaches are proposed for the dynamic m identical parallel machines scheduling problem with a common server to generate new schedules depending on the hybrid rescheduling policy. A priority based performance measure is proposed to minimize the number of tardy jobs as primary goal and the square root of the mean-squared deviation for due dates as secondary goal. The proposed complete rescheduling approaches are executed in a hypothetical simulation case to minimize the proposed performance measures under different scheduling frequencies and due date tightness factors. The rankings of the proposed approaches are compared by using simple additive weighting method under different weighting scenarios. The utility results indicate that simulated annealing based complete rescheduling method produces better scheduling performance when compared to dispatching rule based complete rescheduling methods in general. [ABSTRACT FROM AUTHOR]

• Dynamic flexible job shop scheduling problem with machine breakdown is studied. • A two-stage algorithm based on convolutional neural network is proposed. • The improved imperialist competition algorithm is proposed to generate schedules. • A predictive model is proposed to predict the robustness of scheduling. In the actual manufacturing process, the environment of the job shop is complex. There will be many kinds of uncertainties such as random job arrivals, machine breakdowns, order cancellations and other dynamic events. In this paper, an effective two-stage algorithm based on convolutional neural network is proposed to solve the flexible job shop scheduling problem (FJSP) with machine breakdown. A bi-objective dynamic flexible job shop scheduling problem (DFJSP) model with the objective of maximum completion time and robustness is established. In the two-stage algorithm, the first stage is to train the prediction model by convolutional neural network (CNN). The second stage is to predict the robustness of scheduling through the model trained in the first stage. First, an improved imperialist competition algorithm (ICA) is proposed to generate training data. Then, a predictive model constructed by CNN was proposed, and an alternative metric called RMn was developed to evaluate robustness. RMn evaluates that the float time has an effect on the robustness through the information of machine breakdown, workload and float time of the operation. The experimental results show that the proposed two-stage algorithm is effective for solving DFJSP, and RMn can evaluate the robustness of scheduling more quickly, efficiently and accurately. [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: Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization. [Copyright &y& Elsevier]

Abstract: This paper presents a dynamic scheduling for real-time tasks in multicore processors to tolerate single and multiple transient faults. The scheduling is performed based on three important issues: (1) current released tasks, (2) current available processor cores, and (3) consideration of the number of faults and their occurrences. Using tasks utilization along with a defined criticality threshold in the proposed scheduling method, current ready tasks are divided into critical- and noncritical ones. Based on whether a task is critical or noncritical, an appropriate fault-tolerance policy is exploited. Moreover, scheduling decisions are made to fulfill two key goals: (1) increasing scheduling feasibility and (2) decreasing the total tasks execution time. Several simulation experiments are carried out to compare the proposed method with two well-known methods, called checkpointing with rollback recovery and hardware replication. Experimental results reveal that in the presence of multiple transient faults, the feasibility rate of the proposed method is considerably higher than the other well-known fault-tolerance methods. Moreover, the average timing overhead of this method is lower than the traditional methods. [Copyright &y& Elsevier]

• Updating inspection and restoration plans in real time improves network resilience. • Inaccurate estimation of network damages leads to low levels of network resilience. • Different levels of dynamism affect the performance of the dynamic model. • Hybrid genetic algorithm effectively solves dynamic routing and scheduling problems. In the immediate aftermath of earthquakes, effective scheduling of emergency restoration for transportation networks depends fundamentally on information about damage to those networks, which for the most part can only be acquired via a lengthy process of inspection. This paper proceeds from the insight that, rather than waiting to commence restoration activities until after all inspection activities are completed, damage information revealed gradually via inspection efforts could be incorporated into parallel scheduling of inspection routes and restoration schedules, allowing inspection and restoration to occur simultaneously, thus more efficiently boosting transportation networks' resilience. To achieve this, however, it will be necessary to understand the real-time interaction between inspection and restoration, as well as such interaction's impacts on the inspection-routing and restoration-scheduling process. Assuming that multiple inspection and restoration crews operate simultaneously and that their optimal routes and schedules are updated dynamically whenever additional inspection information is obtained, this study proposes an integer program for modeling inspection-routing and restoration-scheduling problems and determining the optimal inspection routes and restoration schedules for damaged highway–bridge networks, with the specific aim of maximizing a resilience measure, network travel time. The results of a case study using the proposed method and data from the 2008 Wenchuan Earthquake in China show that, as compared to a traditional inspection-restoration model, simultaneously performing and dynamically scheduling inspection and restoration can significantly boost networks' resilience. [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: The resource-constrained project scheduling problem (RCPSP) is an extensively explored area. The existing RCPSP solutions tend to focus on single project scheduling problems without practical supports to address complex constraints, dynamic environments, and multi-project schedule coordination. This paper proposes a dynamic project scheduling algorithm based on partial task network heuristics. This algorithm takes time constraints, resource constraints, and particularly the changing task execution status into consideration. To coordinate conflicting schedules of multiple projects, we proposed an interactive decision support process and developed new algorithms for conflict detection, conflict resolution, and impact analysis. The proposed algorithms have been fully implemented and tested in a web-based aircraft maintenance management system and are being applied in construction for project scheduling and facilities maintenance management. [ABSTRACT FROM AUTHOR]

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]

Abstract: This paper focuses on the scheduling of tasks with hard and soft real-time constraints in open and dynamic real-time systems. It starts by presenting a capacity sharing and stealing (CSS) strategy that supports the coexistence of guaranteed and non-guaranteed bandwidth servers to efficiently handle soft tasks’ overloads by making additional capacity available from two sources: (i) reclaiming unused reserved capacity when jobs complete in less than their budgeted execution time and (ii) stealing reserved capacity from inactive non-isolated servers used to schedule best-effort jobs. CSS is then combined with the concept of bandwidth inheritance to efficiently exchange reserved bandwidth among sets of inter-dependent tasks which share resources and exhibit precedence constraints, assuming no previous information on critical sections and computation times is available. The proposed Capacity Exchange Protocol (CXP) has a better performance and a lower overhead when compared against other available solutions and introduces a novel approach to integrate precedence constraints among tasks of open real-time systems. [Copyright &y& Elsevier]

Abstract: This paper describes the design and initial implementation of a software framework for exploiting resizability in distributed-memory parallel applications. By “resizable” we mean the ability at run-time to expand or contract the number of processes participating in a parallel application. The ReSHAPE framework described here includes a cluster scheduler, a library supporting data redistribution and process remapping, and an application programming interface (API) which allows applications to interact with the scheduler and resizing library with only minor code modifications. Parallel applications executed using the ReSHAPE framework can expand to take advantage of additional free processors or contract to accommodate a high priority application without being suspended. Experimental results show that the ReSHAPE framework can significantly improve individual job turn-around time and overall system throughput, even with very simple application scheduling policies. In addition, the framework serves as a convenient platform for research into much more sophisticated cluster scheduling policies and methods. [Copyright &y& Elsevier]

Abstract: The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring. [Copyright &y& Elsevier]

Abstract: In the current day wide-issue processors, the size of the instruction scheduling window (also called Issue Queue (IQ)) is limited mainly by the hardware complexity to design the logic, and thus limits the number of instructions scanned every cycle to extract instruction level parallelism (ILP). To exacerbate the problems, instructions depending on long latency load operations continue to reside in the IQ until their source operands are ready. Thus, such delayed instructions block any new instructions from entering the IQ even if potentially they are ready for execution. The growing disparity in processor and memory speeds is further aggravating the delay in dislodging instructions from IQ. To alleviate the problem, in this paper we propose a novel technique to streamline instructions in separate buffers according to the chain of dependencies. Each instruction is streamlined behind a parent instruction while it waits for the source operand to be supplied by the long latency memory operations. These instructions are segregated from the IQ and thus the pressure on IQ is relieved which enables flow of potentially executable instructions in the pipeline. Our analysis of SPEC2000 programs reveals that instructions dependent on load cache misses or their dependents, typically have their first source operand ready within 5–15% of their total wait time in the IQ. Based on the observations, the long latency memory dependent instructions are streamlined into in-order buffers when their first operand is ready. In the proposed architecture, instructions from both the conventional IQ and the heads of the streamline buffers can be selected for execution, while the wakeup logic complexity remains same as in the conventional design. Our results show that the performance speedup of 32-entry IQ supplemented by 32 in-order buffers is 15.7% and 2% for FP and integer benchmark respectively, which is very much comparable to that of a conventional 64-entry IQ. A 64-entry IQ design can gain performance over a 32-entry IQ, albeit with a large overhead in circuit delay complexity of wakeup logic, while streamline buffers can gain performance over 32-entry IQ without any such overhead. [Copyright &y& Elsevier]

Abstract: Flexible manufacturing systems (FMSs) are highly automated and require effective scheduling approaches to improve the system performance and integrate various control decision-making activities. In this paper, a multi-agent approach integrated with a filtered-beam-search (FBS)-based heuristic algorithm is proposed to study the dynamic scheduling problem in a FMS shop floor consisting of multiple manufacturing cells. The approach is based on a hybrid architecture which is composed of a set of distributed agents each using local information to generate real-time schedules. The FBS-based algorithm is embedded into agents as the scheduling engine, which makes improvement in the generation procedure of branches to obtain optimal or sub-optimal schedules quickly. A FIPA CNIP-based negotiation mechanism is utilized to realize coordination of agents to achieve a globally good schedule. To demonstrate the feasibility and effectiveness of the proposed approach, a proof-of-concept prototype system has been implemented in the JADE™ platform. Computational experiments of the approach are also presented in comparison with those of other methods. [Copyright &y& Elsevier]

Abstract: Scheduling product batches in pipelines is a very complex task with many constraints to be considered. Several papers have been published on the subject during the last decade. Most of them are based on large-size MILP discrete time scheduling models whose computational efficiency greatly diminishes for rather long time horizons. Recently, an MILP continuous problem representation in both time and volume providing better schedules at much lower computational cost has been published. However, all model-based scheduling techniques were applied to examples assuming a static market environment, a short single-period time horizon and a unique due-date for all deliveries at the horizon end. In contrast, pipeline operators generally use a monthly planning horizon divided into a number of equal-length periods and a cyclic scheduling strategy to fulfill terminal demands at period ends. Moreover, the rerouting of shipments and time-dependent product requirements at distribution terminals force the scheduler to continuously update pipeline operations. To address such big challenges facing the pipeline industry, this work presents an efficient MILP continuous-time framework for the dynamic scheduling of pipelines over a multiperiod moving horizon. At the completion time of the current period, the planning horizon moves forward and the re-scheduling process based on updated problem data is triggered again over the new horizon. Pumping runs may extend over two or more periods and a different sequence of batches may be injected at each one. The approach has successfully solved a real-world pipeline scheduling problem involving the transportation of four products to five destinations over a rolling horizon always comprising four 1-week periods. [Copyright &y& Elsevier]

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]

Abstract: In construction production field, inasmuch as various types of disturbance factor existed, it is difficult to manage related information by using conventional model. In this paper, a new concept of “parts and packets unified architecture” is proposed. Data or information related to a product are carried by product itself and can be handled to manage the whole system. It is a unified controller system which operates parts and packets together. [Copyright &y& Elsevier]

Abstract: This paper describes a negotiation protocol proposed for inter-agent cooperation in a multi-agent system that we developed for optimisation and dynamic integrated scheduling within steel production. The negotiation protocol is a two-level bidding mechanism based on the Contract Net Protocol. The purpose of this protocol is to allow the agents to cooperate and coordinate their local schedules in order to find globally near-optimal robust schedules, whilst minimising the disruption caused by the occurrence of unexpected real-time events. We conduct several experiments to investigate the performance of this negotiation protocol to coordinate the agents in generating good quality robust schedules. This performance is evaluated in terms of stability and utility measures used to evaluate the robustness of the steel production processes in the presence of real-time events. [Copyright &y& Elsevier]

This paper presents a simulation model of a flexible manufacturing system (FMS) which subjects to minimization three performance criteria simultaneously such as mean flow time, mean tardiness, and mean earliness. The dispatching rule will be changed at a frequency that is varied by the number of outputs produced by the system. Therefore, the dynamic nature is event-triggered rather than changing the rule at a regular time interval, which is passive. Three indexes are calculated to represent the three criteria under monitoring and the indexes are ranked in descending order: the greater the index, the worse is the situation of the criterion in the system. Thus, an appropriate rule will be selected for the next operation in order to tackle that criterion with the largest index. This mechanism is so called pre-emptive method. Furthermore, the indexes can be biased so that particular criterion can have a greater weighting as set by the decision-maker. Results show that a solution (range of frequency) can always be determined to change the dispatching rule so that the system is better than just using fixed FMS scheduling rule. [Copyright &y& Elsevier]

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]