Scheduling Of Manufacturing Systems Research Articles

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode

As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing system in which no-idle flow-line and flexible job-shop production modes are hybridized, a mixed integer linear programming model is formulated and an enhanced adaptive multi-objective evolutionary algorithm is developed. In the proposed algorithm, a decision tree-based rule combination selector is developed to adaptively select the most appropriate rule combination fit for the given production scenario to generate a high-quality initial population. Three types of crossover operators and six objective-oriented local search operators are designed to increase the exploration and exploitation capability. An adaptive balance mechanism of exploration and exploitation is trained by Q-learning to maximize search efficiency. In addition, an adaptive adjustment mechanism of population size is designed to ensure diversity and speed up convergence. The comparative study demonstrates that the three proposed adaptive mechanisms are effective and the proposed algorithm with three adaptive mechanisms significantly outperforms other comparison algorithms in solving the studied problem.

Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems

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.

A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning

Short-term energy consumption forecasting is the foundation of anomaly detection, scheduling and energy-saving control in manufacturing system. Existing methods do not reasonably distinguish between energy consumption nodes, but rather adopt overall prediction, resulting in poor prediction of target nodes. Consequently, this paper proposes a short-term energy consumption prediction method for the Attention Mechanism based on spatio-temporal deep learning (ST-Attention). Convolutional Neural Network (CNN) is utilized to extract feature extraction to acquire local high-dimensional features; Local long-term trend features are extracted using Gate Recurrent Unit (GRU) to reduce the inaccuracy of prediction due to the mixing of raw data;Finally, the Attention Mechanism is utilized to dynamically assign the weight of energy consumption nodes attribute to effectively deals with the difference, anisotropy and correlation between energy consumption nodes. The attention mechanism fuses extracted temporal and spatial features through weight assignment to enable short-term energy consumption prediction for manufacturing systems. The actual data collected from the aluminum profile factory is used to verify that the ST-Attention model providing better performance for short-term energy consumption prediction of the manufacturing system.

A Branch and Bound Algorithm for Scheduling of Flexible Manufacturing Systems

A Branch and Bound Algorithm for Scheduling of Flexible Manufacturing Systems

Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

Optimum Scheduling of a Multi-Machine Flexible Manufacturing System Considering Job and Tool Transfer Times without Tool Delay

In order to minimize makespan (Cmax) without causing tool delay with the fewest copies of each tool type, this study investigates the concurrent scheduling of automated guided vehicles (AGVs), machines (MCs), tool transporter (TT), and tools in a multi-machine flexible manufacturing system (FMS). The tools are housed in a central tool magazine (CTM), accessible to and utilized by several machines. AGVs and the tool transporter (TT) move jobs and tools between machines. Since it involves allocating tool copies and AGVs to job operations, sequencing job operations on machines, and related trip operations, such as AGVs’ and TT’s empty trip and loaded trip times, this simultaneous scheduling problem is highly complicated. This issue is resolved using the symbiotic organisms search algorithm (SOSA), based on the symbiotic interaction strategies that organisms adapt to survive in the ecosystem. This study proposes a mixed nonlinear integer programming formulation to address this problem. Verification is performed using an industrial problem from a manufacturing organization. The results show that employing two copies for two tool types out of 22 tool kinds and one copy for the remaining tool types results in no tool delay, which causes a reduction in the Cmax as well as cost. The industries that can benefit directly from this study are consumer electronics manufacturers, original equipment manufacturers, automobile manufacturers, and textile machine producers. The results demonstrate that the SOSA provides promising results compared to the flower pollination algorithm (FPA).

Hybrid flow-shop scheduling in collaborative manufacturing with a multi-crossover-operator genetic algorithm

Collaborative manufacturing systems have become a key component of Industry 4.0, supported by Industrial Information Integration Engineering (IIIE) applications. Efficient scheduling and coordination of manufacturing tasks are vital for these systems, directly impacting factory operational efficiency. One of the most prominent optimization problems in collaborative manufacturing is the hybrid flow shop scheduling problem with multiprocessor task (HFSPMT). This study proposes an improved genetic algorithm with multi-crossover-operator, called MCO-GA. MCO-GA introduces a novel crossover operator named SX, which demonstrates superior convergence efficiency compared to classical crossover operators. Furthermore,MCO-GA utilizes a probability selection method to autonomously choose between classical crossover operators and SX during the crossover stage. The effectiveness of MCO-GA is demonstrated through its successful application in solving a scheduling problem in a real-life wood manufacturing factory. Comparing MCO-GA with the state-of-the-art metaheuristic algorithms (HSA, OBL_HSA, and MGLS), it is observed that MCO-GA achieves significantly better average results in over 60% of instances. Additionally, MCO-GA outperforms OBL_HSA and MGLS in terms of computation time. These results highlight the effectiveness and efficiency of MCO-GA in solving the HFSPMT problem and its potential for improving scheduling and coordination in collaborative manufacturing systems.

Hierarchical-clustering-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems

Both the spare part provision and the maintenance scheduling are important in ensuring the production of the Serial-Parallel Multi-Station System (SPMMS). In the multi-specification and small-batch production, the production plan, which renews batch by batch, leads to variable and uncertain working conditions. The existing Dynamic Opportunistic Maintenance (DOM), which separately optimizes each cycle, ignores the aftereffects of both the spare part provision and the Preventive Maintenance (PM) on the following PM scheduling. This paper studies the dynamic joint optimization of spare part provision and maintenance scheduling for the SPMMS, where the aftereffects of the information are especially investigated. During the failure rate modelling, the aftereffect of the historical working conditions is described with the accelerated failure-time model. In the system-level maintenance modelling, a Hierarchical-Clustering-Based Opportunistic Maintenance (HCBOM) policy, which uses a non-parametrical clustering, is introduced to incorporate the aftereffects of the PM. The case study and the policy comparisons verify the above-mentioned aftereffects. The proposed HCBOM is more cost-effective than the tradition Opportunistic Maintenance (OM) frame, the DOM, the group maintenance and the station-level maintenance. From the perspective of clustering, this work provides an innovative OM policy, which can guide practitioners to formulate cost-effective PM scheme without time-expensive calculations.

Reconfigurable timed graphs for the design of optimal scheduling in uncertain environments based on transition-timed Petri net

This paper is about the design of firing sequences of minimal time for a class of timed discrete event systems that behave in uncertain environment including unexpected events. Such systems are modelled using partially controllable Transition-Timed Petri nets that encompass controllable deterministic, controllable stochastic and uncontrollable stochastic transitions. The main contribution of the work is to embed the timing aspects of such systems in Reconfigurable Timed Extended Reachability Graphs. As a consequence, the scheduling problem is solved with a standard optimization method. An application to the reactive scheduling of manufacturing systems highlights the advantages of the approach.

Coordinated production and delivery scheduling for service-oriented manufacturing systems with deterioration effect

Service-oriented manufacturing (SOM) is a new industrial model that combines manufacturing with service. To enhance the competitiveness of manufacturers in SOM systems, we investigate a coordinated production and delivery scheduling problem considering a third-party logistics (3PL) provider. The considered problem can be divided into two phases, including production phase and delivery phase. In the production phase, jobs are processed by multiple manufacturers located in different geographical zones. Jobs are with deteriorating processing time and set-up time. Several structural properties of the problem in the production phase are proposed, based on which a heuristic algorithm is designed to make decisions on job batching and batch sequencing. After being processed, the finished jobs are immediately delivered to a customer by manufacturers’ own vehicles or the 3PL provider. A dynamic programming algorithm is developed to solve the problem in the delivery phase. The integrated scheduling problem is proved to be NP-hard. Hence, we develop a general variable neighbourhood search (GVNS) to solve the problem in a reasonable time. The basic variable neighbourhood descent (BVND) is used as a local search method in the GVNS. Finally, the computational experiments are conducted and the results verify the effectiveness and stability of the proposed approach.

A new application of multi-criteria decision-making methods for the scheduling of flexible manufacturing systems: A case study

A new application of multi-criteria decision-making methods for the scheduling of flexible manufacturing systems: A case study

A Job-Shop Scheduling Decision-Making Model for Sustainable Production Planning With Power Constraint

Improving environmental sustainability and reducing energy cost are becoming central topics of decision-making in manufacturing. Increasingly manufactures are looking for new solutions that allow them to become more energy efficient and sustainable. Regarding energy cost issue, it is well known that the electricity costs in manufacturing systems strongly depend on the reached peak power. On the other hand, the power required by a manufacturing system affects the adoption of renewable energy sources. The use of renewables, indeed, encompasses fluctuations in energy supply and requires adapting production processes to a power constraint. The article proposes a decision model for the scheduling in a job-shop manufacturing system that simultaneously deals with the power constraint and the variable speed of machine tools. Defining the maximum value of the usable power, decisions can be taken on production scheduling, by adapting the processing speed of several machines that work simultaneously, to the power availability. The model allows decision makers to plan production, taking into account the impossibility of exceeding a certain power and assuming the minimization of the makespan as an objective function. Managers can utilize the scheduling model in all the different contexts, where there is a power limit. The test cases show how the proposed model allows to obtain an efficient scheduling under a constant and variable power constraint. The findings highlight that the model can be employed to reduce the peak power and encourage the use of renewables in manufacturing systems, thus obtaining an efficient scheduling and a more sustainable production.

Energy cost efficient scheduling in flexible job-shop manufacturing systems

This paper studies the problem of determining energy efficient schedules in a flexible job shop. The goal is to minimize the total energy cost, given a time-of-use pricing scheme, while ensuring that the schedule does not violate a maximum makespan. The problem is first formalized as a mixed integer program. Because it is already difficult to solve, the simpler problem with a fixed sequence of operations is then extensively studied. Some properties are derived for the specific problem with a fixed sequence. These properties show that the complexity of the problem depends on the structure of the energy pricing scheme. They are also used to propose two heuristic approaches. Relying on these heuristics, we further develop an iterative tabu search for the general problem. Extensive computational experiments are carried out to evaluate the solution methods and the potential gains on the total energy cost, depending on the flexibility associated to the maximum allowed makespan and on the time-of-use structures.

Sequence generation for multi-task scheduling in cloud manufacturing with deep reinforcement learning

Cloud manufacturing is a manufacturing model that aims to deliver on-demand manufacturing services to consumers. Scheduling is an important problem that needs to be addressed carefully and effectively for cloud manufacturing to achieve that aim. Cloud manufacturing allows consumers to submit their requirements to the cloud platform simultaneously and therefore requires cloud manufacturing scheduling systems to be able to handle multiple tasks effectively. It is further complicated when multiple composite tasks are submitted to the system and to be addressed. A vast majority of existing studies have proposed various algorithms, including meta-heuristics, heuristics, and reinforcement learning algorithms, to address cloud manufacturing scheduling (CMfg-Sch) problems, but only a very small fraction of them deal with scheduling of multiple composition tasks with deep reinforcement learning. In this work, we leverage DRL coupled with sequence generation for addressing CMfg-Sch problems. Different from all existing works, we first propose two sequence generation algorithms for generating scheduling sequences of multiple composite tasks prior to scheduling. Coupled with this a Deep Q-Networks (DQN) and a Double DQN-based scheduling algorithms are proposed, respectively. Performance of the proposed algorithms is compared against seven baseline algorithms using makespan, cost, and reliability as evaluation metrics. Comparison indicates that sequence generation algorithm II (SGA-II) overall has a greater advantage over algorithm I (SGA-I), especially in terms of the makespan, and the Double DQN-based scheduling algorithm outperforms the DQN-based algorithm, which in turn performs better than other baseline algorithms.

Production scheduling in a reconfigurable manufacturing system benefiting from human-robot collaboration

Nowadays, the manufacturing sector needs higher levels of flexibility to confront the extremely volatile market. Accordingly, exploiting both machine and workforce reconfigurability as two critical sources of flexibility is advantageous. In this regard, for the first time, this paper explores an integrated production scheduling and workforce planning problem in a Reconfigurable Manufacturing System (RMS) benefiting from reconfigurable machines and human-robot collaboration. A new Mixed-Integer Linear Programming (MILP) model and an efficient Constraint Programming (CP) model are developed to formulate the problem, minimising the makespan as the performance metric. Due to the high complexity of the problem, the MILP model cannot handle large-sized instances. Hence, to evaluate the performance of the CP model in large-sized instances, a lower bound is derived based on the relaxation of the problem. Finally, extensive computational experiments are carried out to assess the performance of the devised MILP and CP models and provide general recommendations for managers dealing with such a complex problem. The results reveal the superiority of the CP model over the MILP model in small- and medium-sized instances. Moreover, the CP model can find high-quality solutions for large-sized instances within a reasonable computational time.

Integration of sub-task scheduling and logistics in cloud manufacturing systems under setup time and different task arrival times

ABSTRACT With the development of new technologies, cloud manufacturing system allows users to request different services by providing a customer-oriented structure with the possibility of access to distributed manufacturing resources. By departing from literature, this study relaxes the unreasonable assumption that all tasks are available at the first moment of scheduling. In addition, two factors are considered to get the model closer to reality: (1) The time/cost of sub-tasks transfer among different services of enterprises in diverse geographical locations; and (2) Service setup time/cost to perform diverse sub-tasks on a given service consecutively. Taking into account these characteristics, a model including three objects of cost imposed on the cloud manufacturing system, task completion time and service quality is developed. The results confirm the importance of considering task arrival time, logistics time/cost and setup time/cost to get more realistic solutions. GAMS software is employed to solve the problems with small and medium scales while a solution approach method based on a genetic algorithm is developed to solve the problems on a large scale. And finally, a sensitivity analysis is undertaken to gain insight into the impact of time, cost and user’s requirements on the final solution.

A new algorithm of the scheduling of a flexible manufacturing system based on genetic algorithm

In the flexible manufacturing system, a reasonable production scheduling is crucial in shortening the processing completion time and improving the equipment utilization. Traditional manual scheduling cannot effectively solve the complex workshop scheduling problems and cannot provide a scheduling solution that meets the requirements in a short period of time, which can lead to a decrease in processing efficiency. Aiming at the complex job shop scheduling problem, the genetic algorithm is used to find the optimal scheduling solution in this study, taking the number of overdue jobs, the total overdue time, the job completion time, the comprehensive load rate and the maximum load rate of the machine tool as the performance indicators of the scheduling algorithm. The chromosomes are designed as process gene chain and equipment gene chain to improve the diversity and the robustness to scheduling problems of chromosome through crossover, variation, selection and other processes. The impact of different parameter settings on the performance indicators of each scheduling algorithm is researched by adjusting the four algorithm-related parameters, and there has been a certain improvement in the results of the scheduling problems. This study provides a reference for the design and optimization of production scheduling algorithm based on genetic algorithm.

An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.

Special Issue: “The Planning and Scheduling of Manufacturing Systems”

The “Fourth Industrial Revolution” (alternatively known as “Industry 4 [...]