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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.

Management innovation is particularly important today when technological convergence is becoming increasingly evident. Furthermore, as employees are the most important resource in work dynamic scheduling, the mobility of employees will bring extremely discrete regression particle swarm algorithm uncertainty to work projects, making its scheduling problem more complex. This paper first proposes to apply a discrete regression particle swarm algorithm based on refinement management as an enterprise management innovation to optimize three indicators of time, cost, and stability by implementing refinement personnel management. Secondly, we set constraints for dynamic tasks to implement management rescheduling to avoid overworking employees and thus maximize profits for the enterprise. Finally, the effectiveness of the discrete regression particle swarm algorithm is verified by selecting 27 benchmark examples to compare from three indicators of contribution and time and cost. The results show that the discrete regression particle swarm algorithm (S-PSO-VNS) has an average time reduction of 22.65%, a cost reduction of 15.21%, and a stability improvement of 35.74% compared to the other two control algorithms in terms of management algorithm quality and performance analysis. The research data illustrates that the discrete regression algorithm proposed in this paper can have high efficiency and effectiveness, and the discrete regression particle swarm algorithm can provide managers of SMEs with more informed decisions on work schedules, thus maximizing profits and achieving more profitability.

In this paper, the overall architecture design of the platform is proposed, and the data architecture and business architecture of the platform are discussed. On this basis, the logistics path planning method of digital collaboration is proposed, including the aspects of path demand splitting and distribution vehicle spatio-temporal network, and the demand splittable logistics path planning model is considered. Finally, the simulation of the workshop logistics dynamic scheduling example is designed, and the dynamic scheduling optimization results are analyzed and verified. The results show that: through optimization, its time penalty cost f1 = 801.38, the total distance traveled by the delivery trolley f2 = 1709m, and the total distance of empty load is 692.15 m, and the dispatching scheme increases the distance of empty load by 59 m . The research results of this paper can provide a reference for digital management and collaboration platform for civil aircraft manufacturing enterprises and provide a reference for research in related fields.

Abstract To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.

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.

This paper presents a simulation-based environment for verification of static task scheduling methodology in a time predictable system. Different types of processed tasks are distinguished and presented a unified system design methodology consisting of the selection of real time system configuration, task mapping, scheduling, and generation of a sequence of task identifiers to control the interleaved pipeline. An original Worst Case Timing Analyzer (WCTA) has been developed to automate the design process. The methodology was introduced into the original PRET (PREcision Timed) architecture recently presented (Antolak and Pulka, 2020), (Antolak and Pulka, 2021). The PRET system was implemented on a Virtex7 FPGA (Field Programmable Gate Array) platform. A dedicated verification environment is proposed that allows on-line real time system monitoring, analysis of timing parameters, and comparing the results with initial requirements and design constraints. The practical experiments presented in the paper proved the correct operation of the author’s hardware architecture. The obtained results confirmed the validity of the proposed scheduling method and the concept of calculating the execution times of tasks before they are started, which allows for optimal hardware matching to the tasks to be performed.

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.

This thesis combines the ABC classification method and hierarchical analysis method to propose the evaluation method of logistics spatial layout and applies it to actual automobile manufacturing enterprises. The spatial layout scheme of logistics nodes in the park has been adjusted and optimized to achieve dynamic inventory classification and improve inventory management. When the demand of the decision-making body decreases, the optimal production and transportation scheduling plan for the automobile industry supply chain is formulated using the dynamic scheduling decision-making model. The results show that the spatial layout optimization scheme of supply chain logistics nodes in the automobile industrial park is RDC-B3, VDC-B5, and PDC-B1. The total cost of supply chain logistics after optimization is reduced by 3.8 × 108 yuan. The inventory items in the storage center are divided into 3 categories: A category for inventory items numbered 1-6, B category for 7-9, and C category for 10-11, to improve the turnover rate of the whole inventory and reduce the impact of inventory risk. When the demand side of the supply chain is reduced, the total cost of scheduling the optimal solution is 138.01 million. The research results of this paper have some reference significance.

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.

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.

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.

In the production process of aerospace structural parts, there coexist batch production tasks and research and development (R&D) tasks. Personalized small-batch R&D and production tasks lead to frequent emergency insertion orders. In order to ensure that the task is completed on schedule and to solve the flexible job shop dynamic scheduling problem, this paper takes minimization of equipment average load and total completion time as optimization goals, and proposes a dual-loop deep Q network (DL-DQN) method driven by a perception-cognition dual system. Based on the knowledge graph, the perception system realizes the representation of workshop knowledge and the generation of multi-dimensional information matrix. The cognitive system abstracts the scheduling process into two stages: resource allocation agent and process sequencing agent, corresponding to two optimization goals respectively. The workshop status matrix is designed to describe the problems and constraints. In scheduling decision, action instructions are introduced step by step. Finally, the reward function is designed to realize the evaluation of resource allocation decision and process sequence decision. Application of the proposed method in the aerospace shell processing of an aerospace institute and comparative analysis of different algorithms verify the superiority of the proposed method.

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).

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.

This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed to solve dynamic scheduling under perturbations. The uncertainty sources considered in this study are the expiration date of product components and production delays. The proposed Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACO) take into consideration two objective functions: minimizing the makespan and reducing the number of perishable products. The algorithms are tested on a flow-shop agri-food system.

To adapt to the “fine” and “extensive” management characteristics of railway signal equipment operation and maintenance, achieving real-time and interactive monitoring of signal equipment operation status, and developing an integrated approach to equipment operation and maintenance, this paper takes a comprehensive management perspective. To create a lightweight BIM model, the Garland folding algorithm is utilized to simplify the IFC file format. Building on this approach, the data are divided based on building component division standards to obtain separate files containing geometric information and semantic attributes. The geometric information files are converted to a 3D Tiles format, combining BIM semantic attributes with semantic attribute files through an intermediate format. Dynamic data management is achieved by setting the octree space index structure in combination with a view-frustum culling algorithm. Then, the 3D Tiles target file is imported into the Cesium platform, and Node.js is used to achieve three-dimensional visualization of railway signal operation and maintenance. The proposed method is verified using an inbound signal as an example to assess its feasibility. The results demonstrate the potential of the proposed method to achieve stable integration between BIM equipment full lifecycle maintenance and GIS geographical space display. Railway signal equipment is endowed with comprehensive one-click information query functions for equipment positioning and spatial analysis, improving the efficiency and scientific decision-making level of equipment operation and maintenance.

Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives.

“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.

In yard-crane scheduling problems, as loading operations take priority over unloading, the delivery sequence of unloading from the quaysides to the yard is uncertain. The delivery sequence changes may make crane scheduling more difficult. As a result, the crane operations schedules developed statically become suboptimal or even infeasible. In this paper, we propose a dynamic scheduling problem considering uncertain delivery sequences. A mixed-integer linear program is developed to assign tasks to cranes and minimize the makespan of crane operations. We propose an iterative solution framework in which the schedules are re-optimized whenever the delivery sequence change is revealed. A genetic algorithm is proposed to solve the problem, and a greedy algorithm is designed to re-optimize and update the solution. To make the updated solution take effect as soon as possible, regarding batch-based task assignment, the tasks in the scheduling period are divided into several batches. In this case, the instant requests arising from the delivery sequence change are added to corresponding batch tasks and re-optimized together with the tasks of this batch. In addition, a relaxation model is formulated to derive a lower bound for demonstrating the performance of the proposed algorithm. Experimental results show that the average gap between the algorithm and the lower bound does not exceed 5%. The greedy insertion algorithm can re-optimize the instant requests in time. Therefore, the proposed iterative re-optimization framework is feasible and has the advantages (necessity) of batch-based task assignment.

Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.

The paper presents balanced heuristic techniques of static tasks scheduling in multi-core real-time system architecture. The main objective was to minimize the energy consumed by the system without causing deadlines to be missed. The authors proposed a few scheduling scenarios based on modifications of different parameters of the system and defined appropriate algorithms. The methodology was verified and tested on the original hardware platform of the system developed by the authors. The system consists of the reconfigurable set of cores based on an interleaved pipeline processing scheme. The entire system was modeled as original IP at the RTL level in VERILOG and implemented on a Virtex7 FPGA platform. The tasks were executed as a set of programs based on Mälardalen WCET benchmarks; commonly used by the PRET community for validation time predictable systems and worst-case analyses. Many series of experiments were carried out and the results validated the approach and showed that it is possible to radically reduce the energy consumed by the system while retaining timing conditions (i.e., deadlines). The authors gathered and discussed results and formulated a set of recommendations for the safety real-time system’s design flowchart aimed to minimize the energy consumed by the system.

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed.

During the real production system, the scheduling scheme change is mostly changed by dynamic events or new tasks. Due to the different urgency degrees of dynamic events, the corresponding scheduling methods should be adopted to ensure the production efficiency of enterprises. In this paper, an event-driven dynamic workshop scheduling model is established based on Ant Colony System (ACS), and two scheduling methods are designed to deal with dynamic events, namely parallel scheduling and parallel priority scheduling, respectively. The goal of parallel scheduling is to minimize the total makespan, while that of parallel priority scheduling is to minimize the delivery time of dynamic events. Additionally, a selective scheduling strategy is designed to determine the optimal scheduling method according to the urgency degree of dynamic events. Finally, the feasibility of the selective scheduling strategy in solving the dual-objective dynamic job shop scheduling problem (DJSP) is verified by an example experiment on DJSP as well as a large scale problem test set.

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.

Mimic defense is an active defense theory, which aims to fundamentally change the “easy to attack and difficult to defend” situation of network security. In this paper, we propose an evaluation method based on the probability of being attack successfully, and improve the evaluation scheme of historical confidence. We combine the two evaluation schemes with the TOPSIS (technique for order performance by similarity to ideal solution) algorithm, and finally form a complete heterogeneous variant dynamic scheduling model. Different from traditional multi-mode voting algorithms, the effect of the heterogeneous degree in voting is considered, and we use Bayesian estimation to obtain the optimal result in the probabilistic sense. Finally, simulation results show that the proposed algorithm can effectively enhance the dynamic and security of the mimic defense model, and give full play to the characteristics of mimic defense.

Reverse time migration (RTM) is an algorithm widely used in the oil and gas industry to process seismic data. It is a computationally intensive task that suits well in parallel computers. Methods such as RTM can be parallelized in shared memory systems through scheduling iterations of parallel loops to threads. However, several aspects, such as memory size and hierarchy, number of cores, and input size, make optimal scheduling very challenging. In this paper, we introduce a run-time strategy to automatically tune the dynamic scheduling of parallel loops iterations in iterative applications, such as the RTM, in multicore systems. The proposed method aims to reduce the execution time of such applications. To find the optimal granularity, we propose a coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size of work that OpenMP parallel loops assign dynamically to worker threads during the initialization of a 3D RTM application. Experiments performed with different computational systems and input sizes show that the proposed method is consistently better than the default OpenMP schedulers, static, auto, and guided, causing the application to be up to 33% faster. We show that the possible reason for this performance is the reduction of cache misses, mainly level L3, and low overhead, inferior to 2%. Having shown to be robust and scalable for the 3D RTM, the proposed method could also improve the performance of similar wave-based algorithms, such as full-waveform inversion (FWI) and other iterative applications.

At present, discrete workshops demand higher transportation efficiency, but the traditional scheduling strategy of the logistics systems can no longer meet the requirements. In a transportation system with multiple automated guided vehicles (multi-AGVs), AGV path conflicts directly affect the efficiency and coordination of the whole system. At the same time, the uncertainty of the number and speed of AGVs will lead to excessive cost. To solve these problems, an AGVs Multi-Objective Dynamic Scheduling (AMODS) method is proposed which is based on the digital twin of the workshop. The digital twin of the workshop is built in the virtual space, and a two-way exchange and real-time control framework based on dynamic data is established. The digital twin system is adopted to exchange data in real time, create a real-time updated dynamic task list, determine the number of AGVs and the speed of AGVs under different working conditions, and effectively improve the efficiency of the logistics system. Compared with the traditional scheduling strategy, this paper is of practical significance for the scheduling of the discrete workshop logistics systems to improve the production efficiency, utilization rate of resources, and dynamic response capability.

In this paper, we introduce a new formulation of the multivehicle point-to-point network problem to be utilised in urban air mobility (UAM) vertiport-to-vertiport network operations. Vehicle routing problems (VRPs) and their variants have previously been studied and applied in real-world situations, but these problems require additional depot locations, and not all the vehicles can travel to all the locations. In UAM operations, additional depot locations may not be required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to the management of UAMs. Therefore, we propose a new formulation for UAM vertiport-to-vertiport operation by introducing new constraints. In addition, we integrate dynamic scheduling with the flight mission by controlling cruise speed and waiting in each UAM at each vertiport location to generate an arrival and departure schedule for different vertiports that can avoid collisions and increase the number of vehicles. A computational experiment is conducted using an MILP model, and the results show that although our formulation satisfies the problem definition, the computation time increases exponentially with an increase in the problem size. A case study is conducted in the Seoul area involving five vertiports, with 10- and 15-vehicle scenarios studied. This case study shows that the cruise speed variable is active only for the lower and upper bounds under dynamic scheduling, whereas the waiting time variable can be controlled between user-defined limits that can be applied to the management of vertiport-to-vertiport UAM operations.

Production scheduling is the key to manufacturing process decision support, which directly affects the efficiency and competitiveness of enterprises. The production process of discrete workshops is complex and changeable, and it is usually difficult to make adjustments quickly and accurately in response to disturbance events. In this paper, a workshop production scheduling method based on digital twin is proposed and applied to the manufacturing workshop of an aerospace factory. Combined with the advantages of real-time virtual real interaction fusion of digital twin technology, the dynamic scheduling problem under fault disturbance factors is studied. A high-fidelity digital twin workshop is established to realize the mapping and interaction between the real production and the virtual factory. Based on the vibration data of machine tool spindle, a fault prediction method of learning vector quantization neural network is proposed. The dynamic scheduling strategy of workshop production based on digital twin is constructed and compared with the scheduling results without digital twin under fault disturbance. The results show that the scheduling method based on digital twin can effectively deal with disturbances and improve workshop productivity. This study can be used for the application of digital twin and production scheduling in practical factories.

The Minimum Cost Flow (MCF) problem is a well-known problem in the area of network optimisation. To tackle this problem, Network Simplex Algorithm (NSA) is the fastest solution method. NSA has three extensions, namely Network Simplex plus Algorithm (NSA+), Dynamic Network Simplex Algorithm (DNSA) and Dynamic Network Simplex plus Algorithm (DNSA+). The objectives of the research reported in this paper are to simulate and investigate the advantages and disadvantages of NSA compared with those of the three extensions in practical situations. To perform the evaluation, an application of these algorithms to scheduling problem of automated guided vehicles in container terminal is used. In the experiments, the number of iterations, CPU-time required to solve problems, overheads and complexity are considered.

In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems.

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.

Nowadays, industries cope with a wide range of situations and/or perturbations that endanger the manufacturing productivity. Traditionally, manufacturing control systems are responsible for managing the manufacturing scheduling and execution, as these have the capability of maintaining the production operations regardless of a given perturbation. Still, the challenge of these systems is to achieve an optimal performance after the perturbations occur. For this reason, manufacturing control systems must incorporate a mechanism with intelligent capabilities to look for optimal performance and operation reactivity regardless of any scenario. This paper proposes a generic control strategy for a manufacturing control system for piloting the execution of a dynamic scheduling problem, considering a new job arrival as the manufacturing perturbation. The study explores a predictive-reactive approach that couples a genetic algorithm for the predictive scheduling and an adaptive genetic algorithm for reactivity control aiming to minimize the weighted tardiness in a dynamic manufacturing scenario. The results obtained from this proposal verify that the effectiveness was improved by using adaptive metaheuristic in a dynamic scheduling problem, considering absorbing the degradation caused by the perturbation.

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

The implementation of quality processes is essential for an academic setting to meet the standards of different accreditation bodies. However, processes are complex because they involve several steps and several entities. Manual implementation (i.e., using paperwork), which many institutions use, has difficulty following up the progress and closing the cycle. It becomes more challenging when more processes are in place, especially when an academic department runs more than one program. Having n programs per department means that the work is replicated n times. Our proposal in this study is to use the concept of the Tomasulo algorithm to schedule all processes of an academic institution dynamically. Because of the similarities between computer tasks and the processes of workplaces, applying this method enhances work efficiencies and reduces efforts. Further, the method provides a mechanism to secure the integrity of the reports of these processes. In this paper, we provided an educational institution case study to understand the mechanism of this method and how it can be applied in an actual workplace. The case study included operational activities that are implemented to assure the program’s quality.

Last mile handover is the most problematic phase in the delivery process, while real-time communication and dynamic scheduling are major problem areas associated with attended last mile handovers. These problem areas need to be addressed holistically to facilitate efficient last mile handovers. The aim of this paper is to report the design and functionalities of a decision-support system which holistically addresses these problem areas. The functionalities of decision-support system which addresses dynamic scheduling and real-time communication problem areas are discussed using case studies. We conclude that a holistic decision-support system with multiparty communication among the stakeholders facilitates improving customer satisfaction, business opportunities and reducing operational costs for logistics companies.

An important element in the integration of the fourth industrial revolution is the development of efficient algorithms to deal with dynamic scheduling problems. In dynamic scheduling, jobs can be admitted during the execution of a given schedule, which necessitates appropriately planned rescheduling decisions for maintaining a high level of performance. In this paper, a dynamic case of the multiprocessor open shop scheduling problem is addressed. This problem appears in different contexts, particularly those involving diagnostic operations in maintenance and health care industries. Two objectives are considered simultaneously—the minimization of the makespan and the minimization of the mean weighted flow time. The former objective aims to sustain efficient utilization of the available resources, while the latter objective helps in maintaining a high customer satisfaction level. An exact algorithm is presented for generating optimal Pareto front solutions. Despite the fact that the studied problem is NP-hard for both objectives, the presented algorithm can be used to solve small instances. This is demonstrated through computational experiments on a testbed of 30 randomly generated instances. The presented algorithm can also be used to generate approximate Pareto front solutions in case computational time needed to find proven optimal solutions for generated sub-problems is found to be excessive. Furthermore, computational results are used to investigate the characteristics of the optimal Pareto front of the studied problem. Accordingly, some insights for future metaheuristic developments are drawn.

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Dynamic scheduling problem has been an attractive area for researches to investigate since a long time. Many techniques have been used to tackle such problems, but all of these techniques require high efforts to formulate the dynamic scheduling problems inorder to obtain optimal solution. Using Heuristic or dispatching rules to solve the dynamic scheduling problem is efficient and popular manner to reach an acceptable level of scheduling. In this paper hybrid techniques are used in the proposed developed model. First the switching of four dispatching rules (Earliest Due Date (EDD), Slack Time (SLACK), Slack / Remaining Operations (S/ROP) and Priority Index) with the aim of choosing minimum tardy jobs. Second, scheduling-rescheduling approach is used to tackle the dynamic environment of job-shop problem depending on three level algorithms. Third, proposing three level algorithms, these levels are resource level, process planning level to improve scheduling with the aim of minimizing tardy jobs and shorten "order-to-delivery", and job level to design rescheduling policy depending on identified factors of each job order. The developed model is applied to real data from the Heavy Engineering Equipment State Company/Baghdad, and considerable advantages are observed. Applying the proposed model lead to zero number of tardy jobs (NT) and zero mean tardiness (MT). It is obvious from the obtained results that by adopting such model, a better solution for job orders' due dates can be achieved; hence "order-to-delivery" time can be shortened

Bluetooth is a low power and cost-effective short range wireless network technology working in 2.4GHz ISM band. Bluetooth can be implemented either in piconet or Scatternet. In piconet the devices can communicate with each other forming a network with maximum of 8 nodes (1 master and 7 slaves). Two or more piconets can be connected through a common Bluetooth device (a gateway or bridge) to form a Scatternet. But the difficult thing is to achieve Quality of service (QoS) in Bluetooth particularly in the Scatternet. Quality of service refers to the efficient management of system resources, which includes the parameters like bandwidth, delay, jitter etc. This paper addresses guaranteed QoS in a Bluetooth scatternet by considering mainly two constraints namely Packet Loss and Waiting time .The above two constraints are met in a Bluetooth scatternet under high traffic congestion, which increases packet delay and causes channel wastage thereby affecting QoS. In the existing First In First Out (FIFO) scheme the slaves are served by the master in accordance with the arrival of the request offered by respective slaves in the form of ‘first come first serve’ technique. So a proposal to simulate a new priority based scheduling scheme like DST (Dynamic Scheduling Technique) has been implemented, which overcomes the pitfalls like packet loss, waiting time, and high congestion in the FIFO scheduling scheme. The results shows that the proposed DST scheduling that carries dynamic priorities for the requesting slaves by employing dynamic scheduling technique, which assures and guaranties the QoS parameters.

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

In this paper, we propose a technique for the communication between agents in the dynamic generation of a production plan in an autonomous production system using product agents, parts agents and assembly machine agents. A parts agent leads the promotion of processes in this type of assembly system. First, product agents create the assembly process model that contains the assembly sequence to complete the product. Next, product agents generate parts agents that have the assembly process model. Parts agents will be assembled by machines, with some communications between other agents. The proposed system is defined as an event driven architecture. The increase in the costs of communication between agents is polynomial in the number of the agents. Moreover, this system adjusts the production schedule dynamically using only local negotiation when conditions are changed. Finally, we show experimental results with simply examples.

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Furthermore, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e., parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.