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Industries require, nowadays, to be more adaptable to unforeseen real-time events as well as to the rapid evolution of their market (e.g. multiplication of customers, increasingly personalized and unpredictable demand, etc.). To meet these challenges, manufacturers need new solutions to update their production plan when a change in the production system or its environment occurs. In this context, our research work deals with a dynamic scheduling problem of a real Hybrid Flow Shop considering the specific constraints of a perfume manufacturing company. This paper proposes a Digital Twin-driven dynamic scheduling approach based on the combination of both optimization and simulation. For the optimization, we have developed a mixed integer linear programming (MILP) scheduling model taking into account the main specific scheduling requirements of our case study. Regarding the simulation approach, a 3D shop floor model has been developed including the additional stochastic aspects and constraints which are difficult or impossible to model with a MILP approach. These two models are connected with the real shop floor to create a digital twin (DT). The developed DT allows the re-scheduling of production according to internal and external events. Finally, validation scenarios on a perfume case study have been designed and implemented in order to demonstrate the feasibility and the relevance of the proposed digital twin-driven dynamic scheduling approach. [ABSTRACT FROM AUTHOR]

Dynamic task scheduling problem in cloud manufacturing (CMfg) is always challenging because of changing manufacturing requirements and services. To make instant decisions for task requirements, deep reinforcement learning-based (DRL-based) methods have been broadly applied to learn the scheduling policies of service providers. However, the current DRL-based scheduling methods struggle to fine-tune a pre-trained policy effectively. The resulting training from scratch takes more time and may easily overfit the environment. Additionally, most DRL-based methods with uneven action distribution and inefficient output masks largely reduce the training efficiency, thus degrading the solution quality. To this end, this paper proposes an improved DRL-based approach for dynamic task scheduling in CMfg. First, the paper uncovers the causes behind the inadequate fine-tuning ability and low training efficiency observed in existing DRL-based scheduling methods. Subsequently, a novel approach is proposed to address these issues by updating the scheduling policy while considering the distribution distance between the pre-training dataset and the in-training policy. Uncertainty weights are introduced to the loss function, and the output mask is extended to the updating procedures. Numerical experiments on thirty actual scheduling instances validate that the solution quality and generalization of the proposed approach surpass other DRL-based methods at most by 32.8% and 28.6%, respectively. Additionally, our method can effectively fine-tune a pre-trained scheduling policy, resulting in an average reward increase of up to 23.8%. [ABSTRACT FROM AUTHOR]

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with "a priori" and "a posteriori" optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, "interactive" and "Pareto pruning" methods, as well as the consideration of human factors in flexible production systems, remain under-explored. [ABSTRACT FROM AUTHOR]

In many industrial real-world environments, scheduling necessitates continual reactive adjustments due to unpredictable perturbations, leading to the dynamic transformation of predefined static schedules. In this paper, we introduce a new framework named a two-level evolutionary algorithm (2LEA) as a comprehensive approach for addressing the dynamic flexible job shop scheduling problem. The 2LEA is based on a bi-level optimization design, where the upper level is dedicated to solving the general flexible job shop scheduling problem, and the lower level is used as a new evolutionary operator guided by a probability rate in the upper level, focusing on the optimization of operation sequences. This framework is capable of handling four dynamic events job insertion, job cancellation, machine breakdown, and job replacement using a predictive-reactive rescheduling strategy. By addressing the previously unexplored dynamic event of job replacement, this paper fills a significant gap in the literature and opens avenues for further research. Extensive computational experiments conducted on well-known benchmark instances from the Brandimarte and Hurink datasets demonstrate the effectiveness and efficiency of our proposed scheduling algorithm. Our results showcase the superior performance of 2LEA over state-of-the-art approaches in terms of solution quality, affirming its potential as a leading solution for both static and dynamic scheduling challenges. [ABSTRACT FROM AUTHOR]

Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the disturbance factors affect the smooth development of berth hoisting, a hoisting planning architecture based on digital twin is proposed. The berth hoisting sequence planning model and its implementation technology are studied. The model includes three parts: the digital space for formulating the hoisting scheme, the physical space for executing the block hoisting, and the information interaction between the two parts. This paper discusses the berth hoisting planning method based on digital twin, constructs the information exchange platform of digital space and physical space, develops the berth hoisting information exchange platform based on digital twin, and simulates the block hoisting planning by using DELMIA software. An example is given to simulate the operation of the hoisting planning platform. The results show that the work of this paper has a certain reference significance to solve the influence of disturbance factors on berth hoisting sequence planning. [ABSTRACT FROM AUTHOR]

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method. [ABSTRACT FROM AUTHOR]

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production. [ABSTRACT FROM AUTHOR]

Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm is proposed for FMS part input sequencing to handle machine breakage. Evaluation is performed for the proposed algorithm by simulation experiments and result analyses. Finally, conclusions are summarized with managerial implications discussed and further research works suggested. [ABSTRACT FROM AUTHOR]

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team's operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of "one spray and three defenses" for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system. [ABSTRACT FROM AUTHOR]

In contemporary manufacturing and supply chain environments, there are various challenges that companies have to face. Many companies are facing challenges to respond quickly to customers' requirements and to provide customised products cost effectively. Mass customisation (MC) can help companies to provide customised products and services quickly at a low price. Integrated decision-making can obtain performance improvement in many situations. This paper investigates the problem of scheduling integration of flexible manufacturing systems (FMSs) for mass customisation. A mathematical model is formulated for the problem. A set-based dynamic algorithm is developed. Numerical studies are made on the proposed algorithm by simulation and by statistical analysis. Conclusions, managerial implications, and future research suggestions are provided. [ABSTRACT FROM AUTHOR]

In this paper, the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm is proposed to minimize the maximum completion time in a dynamic scheduling problem of hybrid flow shops. In hybrid flow shops, machines require flexible preventive maintenance and jobs arrive randomly with uncertain processing times. The NEAT-based approach is experimentally compared with the SPT and FIFO scheduling rules by designing problem instances. The results show that the NEAT-based scheduling method can obtain solutions with better convergence while responding quickly compared to the scheduling rules. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization approach for day-ahead reactive power planning to improve voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. As the size of the MP-ORPD problem increases significantly with increasing number of contingencies and time periods, efficiency is crucial for practical applications. In this paper, a decomposition technique based on consensus and alternating optimization, where integer variable targets are obtained via MILP, is used to partition the MP-ORPD problem into a set of subproblems, which can be solved in parallel to reduce the computation time. The proposed MP-ORPD problem and its solution algorithm are integrated into the EPRI-VCA software. The results of various power networks of large electric utilities in the Eastern interconnection demonstrate the effectiveness of the proposed algorithm in providing preventive control schedules. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives. [ABSTRACT FROM AUTHOR]

In the real world of manufacturing systems, production planning is crucial for organizing and optimizing various manufacturing process components. The objective of this paper is to present a methodology for both static scheduling and dynamic scheduling. In the proposed method, a hybrid algorithm is utilized to optimize the static flexible job-shop scheduling problem (FJSP) and dynamic flexible job-shop scheduling problem (DFJSP). This algorithm integrates the genetic algorithm (GA) as a global optimization technique with a simulated annealing (SA) algorithm serving as a local search optimization approach to accelerate convergence and prevent getting stuck in local minima. Additionally, variable neighborhood search (VNS) is utilized for efficient neighborhood search within this hybrid algorithm framework. For the FJSP, the proposed hybrid algorithm is simulated on a 40-benchmark dataset to evaluate its performance. Comparisons among the proposed hybrid algorithm and other algorithms are provided to show the effectiveness of the proposed algorithm, ensuring that the proposed hybrid algorithm can efficiently solve the FJSP, with 38 out of 40 instances demonstrating better results. The primary objective of this study is to perform dynamic scheduling on two datasets, including both single-purpose machine and multi-purpose machine datasets, using the proposed hybrid algorithm with a rescheduling strategy. By observing the results of the DFJSP, dynamic events such as a single machine breakdown, a single job arrival, multiple machine breakdowns, and multiple job arrivals demonstrate that the proposed hybrid algorithm with the rescheduling strategy achieves significant improvement and the proposed method obtains the best new solution, resulting in a significant decrease in makespan. [ABSTRACT FROM AUTHOR]

The data-driven method has been widely used to mine knowledge from the smart shop floors' production data to guide dynamic scheduling. However, the mined knowledge may be invalid when the production scene changes. In order to address this problem and ensure the validity of the knowledge, this paper studies a data-driven scheduling knowledge life-cycle management (SKLM) method for the smart shop floor. The proposed method includes four phases: knowledge generation, knowledge application, online knowledge evaluation, and knowledge update. Specifically, the extreme learning machine (ELM) is applied to learn knowledge based on the composite scheduling rules. The quality control theory is used to evaluate the quality of scheduling knowledge. And the online sequential ELM (OS-ELM) is adopted to update the knowledge. Knowledge life-cycle management is implemented through the iterative knowledge update. The proposed method is validated on the MIMAC6, which is a simulation model of the semiconductor production line. Experimental results show that the proposed method could improve the effectiveness of scheduling knowledge and further optimize the performance of the smart shop floor. [ABSTRACT FROM AUTHOR]

Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins' configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

According to [Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer.], advancing tactical levels in production systems has led to the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. [Proth, Jean Marie. 2007. "Scheduling: New Trends in Industrial Environment." Annual Reviews in Control 31 (1): 157–166. .] emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains. [ABSTRACT FROM AUTHOR]

The recent trend of globalisation of the economy has been accelerated thanks to emerging new communication technologies. This forces some companies to be adapted to rapidly changing market requirements utilising a multi-factory production network. Job scheduling in such a distributed manufacturing system, is significantly complicated especially in the presence of dynamic events. Furthermore, production systems need to be flexible to timely react to the imposed changes. Hence, reconfigurable machine tools (RMTs) can be used as a resource for flexibility in manufacturing systems. This paper deals with a distributed job-shop rescheduling problem, in which the facilities benefit from reconfigurable machines. Firstly, the problem is mathematically formulated to minimise total weighted lateness in a static state. Then, the dynamic version is extent based on a designed conceptual framework of rescheduling module to update the current schedule. Since the problem is NP-hard, a self-adaptive hybrid equilibrium optimiser algorithm is proposed. The experiments show that the proposed EO algorithm is extremely efficient. Finally, a simulation-optimisation model is developed to evaluate the performance of the manufacturing system facing stochastic arriving jobs. The obtained results show that the production system can be very flexible relying on its distributed facilities and reconfigurable machines. [ABSTRACT FROM AUTHOR]

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. [ABSTRACT FROM AUTHOR]

In contemporary manufacturing environments, companies face various challenges in trying to meet customer demand in a timely manner. This paper investigates simultaneous part input sequencing and robot scheduling of flexible manufacturing systems for mass customisation. A mathematical model is formulated for this simultaneous and continuous time-based decision-making problem. A segment set-based approach is proposed to solve the problem. Numerical studies are performed by simulation and statistical analyses to evaluate the approach. The proposed approach is compared to different approaches and analysed under various conditions. Conclusions, managerial implications, and future research suggestions are provided. [ABSTRACT FROM AUTHOR]

Mobile edge computing has been an important supplement for traditional cloud computing architecture to offer low-delay computing services to mobile users. However, it is in general impossible for edge service providers to overdeploy so much edge resources to satisfy the rapidly increasing while diverse user demands. In this paper, we study a mobile edge computing system consisting of a service platform, cloudlets joining the system, and mobile users. In this study, we focus on a profit-driven perspective such that the service platform purchases computation resource from the resource-rich cloudlets and makes profit by processing tasks from user side. The design objective is to maximize the platform profit subject to budget constraint and stringent delay requirements for task processing. We formulate this problem as a mixed integer programming problem. Due to the NP-hardness of the problem, we design a logic based Benders decomposition algorithm as the offline solution. We further study the scenario where the task arrivals from user side and resource availability at the cloudlets are both stochastic and unknown in advance. We accordingly propose a Multi-Armed Bandit learning based resource purchasing and greedy task scheduling algorithm for the online scenario. Simulations results show the high performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

As an important branch of production scheduling, the flexible job shop scheduling problem (FJSP) is a typical NP-hard problem. Researchers have adopted many intelligent algorithms to solve the FJSP problem, nonetheless, the task of dynamically adapting its essential parameters during the computational process is a significant challenge, resulting in the solution efficiency and quality failing to meet the production requirements. To this end, this paper proposes an adaptive gray wolf fast optimization algorithm (SS-GWO), which adopts the gray wolf algorithm (GWO) as the basic optimization method, and the algorithm adaptively selects the global search or local search according to the degree of agglomeration of individuals. Firstly, a non-linear convergence factor strategy is employed to control the global exploration and local exploitation capabilities of the algorithm at different stages. This enhances optimization precision and accelerates convergence speed, achieving a dynamic balance between the two. Secondly, the spiral search mechanism of Whale Optimization Algorithm is used in GWO to improve the exploration capability of Gray Wolf Optimization Algorithm. Finally, the effectiveness of SS-GWO model is verified by comparison experiments. The comparison demonstrates the superiority of SS-GWO over the other five state-of-the-art algorithms in solving the 22 classical benchmark test functions. SS-GWO is applied to solve FJSP by means of the standard test function bandimarte calculus. The optimal solution and performance of SS-GWO for solving FJSP are compared with other algorithms. The experimental results show that the SS-GWO algorithm has good optimization performance, and the maximum completion time is reduced by 19% and 37% compared with that of IGWO and GWO, respectively, and the proposed SS-GWO algorithm achieves a better solution effect on flexible job shop scheduling instances, which can satisfy the actual production scheduling needs. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Conventional computing resource trading over mobile networks generally faces many challenges, e.g., excessive decision-making latency, undesired trading failures, and underutilization of dynamic resources, owing to the constraint of wireless networks. To improve resource utilization rate under dynamic network conditions, this paper introduces a novel computing resource provisioning mechanism empowered by overbooking, that allows the amount of booked resources to exceed the resource supply. Cloud-aided mobile edge networks are considered for the proposed framework, where an edge server can purchase more resources from a cloud server to offer computing services to multiple end-users with computation-intensive tasks. Specifically, the proposed mechanism relies on designing pre-signed forward trading contracts among edge and end-users, as well as between edge and cloud in advance to future practical trading; while encouraging an appropriate overbooking rate to improve resource utilization, via analyzing historical statistics associated with uncertainties such as dynamic resource supply/demand, and varying channel qualities. The contract design is formulated as a multi-objective optimization problem that aims to maximize the expected utilities of end-users, edge, and cloud, via evaluating potential risks; for which a two-phase multilateral negotiation scheme is proposed that facilitates the bargaining procedure among the three parties, to reach the final trading consensus (namely, contract terms). Experimental results demonstrate that the proposed mechanism achieves mutually beneficial utilities of three parties, while outperforming baseline methods on significant indicators such as task completion, trading failure, time efficiency, resource usage, etc., from various analytical angles. [ABSTRACT FROM AUTHOR]

Vehicular cloud computing has been emerged as a promising solution to fulfill users’ demands on processing computation-intensive applications in modern driving environments. Such applications are commonly represented by graphs consisting of components and edges. However, encouraging vehicles to share resources poses significant challenges owing to users’ selfishness. In this paper, an auction-based graph job allocation problem is studied in vehicular cloud-assisted networks considering resource reutilization. Our goal is to map each buyer (component) to a feasible seller (virtual machine) while maximizing the buyers’ utility-of-service, which concerns the execution time and commission cost. First, we formulate the auction-based graph job allocation as a 0-1 integer programming (0-1 IP) problem. Then, a Vickrey-Clarke-Groves based payment rule is proposed which satisfies the desired economical properties, truthfulness and individual rationality. We face two challenges: 1) the abovementioned 0-1 IP problem is NP-hard; 2) one constraint associated with the IP problem poses addressing the subgraph isomorphism problem. Thus, obtaining the optimal solution is practically infeasible in large-scale networks. Motivated by which, we develop a structure-preserved matching algorithm by maximizing the utility-of-service-gain, and the corresponding payment rule which offers economical properties and low computation complexity. Extensive simulations demonstrate that the proposed algorithm outperforms the contrast methods considering various problem sizes. [ABSTRACT FROM AUTHOR]

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request. [ABSTRACT FROM AUTHOR]

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service’s characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%. [ABSTRACT FROM AUTHOR]

The development of future wireless networks focuses on providing services with strict, dynamic, and diverse quality of service (QoS) requirements. In this sense, the network slicing paradigm arises as a critical piece on the efficient allocation and management of network resources, allowing for dividing the network into several logical networks with specific functionalities and performance. This paper aims at finding the best combination of access network and network slices over a heterogeneous environment to fulfill users’ requests and optimize network resources usage. We propose the Dynamic radio Access selection and Slice Allocation (DASA) algorithm, flexibly adapted to network conditions, user priorities, and mobility behavior. DASA is based on a multi-attribute decision making (MADM) and analytical hierarchy process (AHP) to face the complex problem of network selection. Moreover, it uses a cooperative game theory approach to handle load balancing during overload situations. This work presents an integral solution that combines software-defined network (SDN) and network function virtualization (NFV) technologies to improve network performance and user satisfaction. DASA algorithm is evaluated through network-level simulations, focusing on flexibility and the effective utilization of network resources during network selection and load balancing mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the maximum flow routing strategy with dynamic link allocation under a constraint on the number of transceivers for space information networks (SINs). Specifically, the time-expanded graph (TEG) is exploited to characterize the dynamic topology of SINs. Furthermore, although there exist multiple feasible links for SINs, only a limited number of them can be actually established due to the constraint on the number of transceivers. Traditionally the established link is fixed within one time interval in the TEG. In order to fully exploit the resource of multiple feasible links, we divide each time interval in the TEG into multiple fine-grained time periods and design the maximum flow routing strategy by jointly optimizing both the fine-grained time period duration and the link allocation as well as the amount of data transmitted on each transmission link and the amount of data stored in each caching link. This problem can be formulated as a mixed-integer quadratic program (MIQP), which is difficult to solve. To overcome this difficulty, we transform the MIQP into an equivalent mixed-integer linear program (MILP), which can be effectively solved by existing methods. Simulation results show that the proposed dynamic link allocation strategy can significantly outperform the fixed link allocation strategy within each time interval. [ABSTRACT FROM AUTHOR]

In view of the machine failure in the manufacturing process of the plate custom furniture mixed flow workshop, in this paper, a comparative analysis of three typical machine failure scenarios was made, and three rescheduling schemes were generated. On this basis, a dynamic scheduling model considering fault recoverability was constructed with the minimum completion time as the goal, and a method combining empire competition algorithm and complete rescheduling strategy was adopted to solve the problem. The results showed that the model and method proposed in this paper can effectively reduce the impact of machine faults on production, and the scheduling efficiency of the three fault scenarios can be increased by 8%, 16.3% and 14.5% respectively, so as to improve the efficiency of shop scheduling and shorten the completion time of sheet parts. This study could provide guidance for optimizing the production scheduling problem of plate customization furniture enterprises under machine fault. [ABSTRACT FROM AUTHOR]

Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP). [ABSTRACT FROM AUTHOR]

The efficiency of a large-scale edge computing system primarily depends on three aspects: i) edge server provision, ii) task migration, and iii) computing resource configuration. In this paper, we study the dynamic resource configuration for hybrid edge server provision under two decentralized task migration schemes. We formulate the dynamic resource configuration as an online cost minimization problem, aiming to jointly minimize performance degradation and operation expenditure. Due to the stochastic nature, it is an online learning problem with partial feedback. To address it, we derive a deterministic mean field model to approximate the stochastic edge computing system. We show that the mean field model provides the increasingly accurate full feedback as the system scales. We then propose a learning policy based on the mean field model, and show that our proposed policy performs asymptotically as well as the offline optimal configuration. We provide two ways of setting the policy parameters, which achieve a constant competitive ratio (under certain mild conditions) and a sub-linear regret, respectively. Numerical results show that the mean field model significantly improves the convergence speed. Moreover, our proposed policy under the decentralized task migration schemes considerably reduces the operating cost (by 23%) and incurs little communication overhead. [ABSTRACT FROM AUTHOR]

Emerging virtual network functions (VNFs) bring new opportunities to network services on the edge within customers’ premises. Network services are realized by chained up VNFs, which are called service function chains (SFCs). These services are deployed on commercial edge servers for higher flexibility and scalability. Despite such promises, it is still unclear how to provide highly available and cost-effective SFCs under edge resource limitations and time-varying VNF failures. In this paper, we propose a novel Reliability-aware Adaptive Deployment scheme named RAD to efficiently place and back up SFCs over both the edge and the cloud. Specifically, RAD first deploys SFCs to fully utilize edge resources. It then uses both static backups and dynamic ones created on the fly to guarantee the availability under the resource limitation of edge networks. RAD does not assume failure rates of VNFs but instead strives to find the sweet spot between the desired availability of SFCs and the backup cost. Theoretical performance bounds, extensive simulations, and small-scale experiments highlight that RAD provides significantly higher availability with lower backup costs compared with existing baselines. [ABSTRACT FROM AUTHOR]

The flow of data across nodes has become the dominant feature of data sharing in distributed environments with increasingly blurred boundaries, where it is crucial to maintain data access dynamic, trusted, and efficient. However, traditional centralized access control models are not only difficult to apply in distributed environments but also ignore trusted verification of authorized entities. What’s worse, existing access control models rarely consider themselves security, lack independence, at a high risk of being bypassed or tampered with. Thus, we propose in this paper a distributed, dynamic, and trusted access control model, DDTAC-BSS, where the standard Attribute-Based Access Control (ABAC) architecture is modified and extended. To reduce the attack surface, we separate policy enforcement point (PEP) from other core components, they are located in the node system and access control system, respectively. Then, the access control entry point (ACEP) is added as the only interface for the node system to interact with the access control system. Subsequently, the model introduces the entity trusted assessment mechanism to improve the trustworthiness of access control services. Driven by the dynamic attributes, our model can achieve dynamic trusted authorization and fine-grained access control. Moreover, we implement a lightweight, independent, and distributed security subsystem to achieve unified management of policies and decision-making autonomy by message-driven. By considering the independence of the security subsystem, a trusted operating environment is built based on Trusted Execution Environment (TEE) to ensure the security of the access control mechanism itself. The security of our model is proved rigorously based on the non-interference theory. Comprehensive experiments and comparisons have demonstrated the superior functionality, comparable performance, and strong security of our model. [ABSTRACT FROM AUTHOR]

In this paper, we study Space-Air-Ground integrated Vehicular Network (SAGVN), and propose an online control framework to dynamically slice the SAG spectrum resource for isolated vehicular services provisioning. In particular, at a given time slot, the system makes online decisions on the request admission and scheduling, UAV dispatching, and resource slicing for different services. To characterize the impact of those parameters, we construct a time-averaged queue stability criteria by taking queue backlogs of all services into consideration, and formulate a system revenue function which incorporates the time-averaged system throughput and UAV dispatching cost. The objective is to maximize the system revenue while stabilizing the time-averaged queue, which falls into the scope of Lyapunov optimization theory. By bounding the drift-plus-penalty, the original problem can be decoupled into four independent subproblems, each of which is readily solved. The merits of our control framework are three-fold: 1) the system is able to admit and process as many requests as possible (i.e., maximizing the time-averaged throughput); 2) the time-averaged UAV dispatching cost is minimized; and 3) service queues are stabilized in the long-term. Extensive simulations are carried out, and the results demonstrate that the control framework can effectively achieve the system revenue maximization and queueing stabilization. Moreover, it can balance the trade-off among system throughput, UAV dispatching cost, and queueing states via parameter tuning. Compared with the fixed slicing, our dynamic slicing can react to the vehicular environment rapidly and achieve an average 26% of throughput improvement. [ABSTRACT FROM AUTHOR]

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNE-RFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks. [ABSTRACT FROM AUTHOR]

The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on the optimal ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT, slowdown, and impoliteness in the M/G/1 setting. Hence we formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. We also take into account the proportional fairness and temporal fairness constraints based on the maximal slowdown and impoliteness, respectively. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT. [ABSTRACT FROM AUTHOR]

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system. [ABSTRACT FROM AUTHOR]

Optimal dispatch in power systems is a complex mathematical model of nonlinear programming with many physical constraints, which is difficult to solve by conventional methods. Thus, intelligent algorithms are now viable options for resolving the nonlinear scheduling issues of microgrids. In this paper, we propose a double-layer optimization strategy based on the multi-point improved gray wolf algorithm (MPIGWO). The inner layer optimizes load profiles with time-of-use tariffs. The outer one achieves a fast search for the optimal solution and prevents getting stuck in a local optimum, which improves the gray wolf algorithm significantly. First, the Bernoulli map is used to randomly generate the initial population. Second, the efficiency of optimization can be improved by modifying the attenuation factor based on the Sin function and then updating the exact weight factor of the position to reasonably select the best position. Finally, an improved dimensional learning-based hunting (IDLH) search strategy is employed to determine the optimal solution. The numerical case study shows that the proposed double-layer optimization strategy can implement dynamic scheduling for distributed power sources while lowering the costs of economic operation and environmental protection significantly. [ABSTRACT FROM AUTHOR]

Effective dynamic scheduling of twin Automated Stacking Cranes (ASCs) is essential for improving the efficiency of automated storage yards. While Deep Reinforcement Learning (DRL) has shown promise in a variety of scheduling problems, the dynamic twin ASCs scheduling problem is challenging owing to its unique attributes, including the dynamic arrival of containers, sequence-dependent setup and potential ASC interference. A novel DRL method is proposed in this paper to minimize the ASC run time and traffic congestion in the yard. Considering the information interference from ineligible containers, dynamic masked self-attention (DMA) is designed to capture the location-related relationship between containers. Additionally, we propose local information complementary attention (LICA) to supplement congestion-related information for decision making. The embeddings grasped by the LICA-DMA neural architecture can effectively represent the system state. Extensive experiments show that the agent can learn high-quality scheduling policies. Compared with rule-based heuristics, the learned policies have significantly better performance with reasonable time costs. The policies also exhibit impressive generalization ability in unseen scenarios with various scales or distributions. [ABSTRACT FROM AUTHOR]

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]