Manufacturing Scheduling Research Articles

Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning

Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

AbstractSelf-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates interoperable communication from adaptive manufacturing control as isolated areas of study. To address this gap, this paper introduces Knowledge Graph-enhanced Multi-Agent Reinforcement Learning (MARL) method that integrates interoperable communication via Knowledge Graphs with adaptive manufacturing control through Reinforcement Learning. We hypothesize that implicit domain knowledge obtained from historical production job allocation records can guide each agent to learn more effective scheduling policies with accelerated learning rates. This is based on the premise that machine assignment preferences effectively could reduce the Reinforcement Learning search space. Specifically, we redesign machine agents with new observation, action, reward, and cooperation mechanisms considering the preference of machines, building upon our previous MARL base model. The scheduling policies are trained under extensive simulation experiments that consider manufacturing requirements. During the training process, our approach demonstrates improved training speed compared with individual Reinforcement Learning methods under the same training hyperparameters. The obtained scheduling policies generated by our Knowledge Graph-enhanced MARL also outperform both individual Reinforcement Learning methods and heuristic rules under dynamic manufacturing settings.

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

Combinatorial Benders decomposition for single machine scheduling in additive manufacturing with two-dimensional packing constraints

This paper addresses a single-machine scheduling problem in additive manufacturing (AM). We focus on the direct metal laser sintering (DMLS) technology of an AM process, where parts can be produced simultaneously in a batch. In this problem, we consider simultaneously the assignment of parts to batches and the packing of parts onto two-dimensional surfaces of the build chamber to minimize the makespan objective. To solve this problem, a mixed integer linear programming model is formulated, and an approximation algorithm is proposed, followed by an exact algorithm based on combinatorial Benders decomposition method with various Benders cuts and acceleration strategies (Algorithm CBD). Finally, we conduct a comprehensive computational study to verify the efficiency of our proposed Algorithm CBD. The computational results show that our proposed Algorithm CBD can effectively solve the test instances with different sizes.

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling processes. The report discusses the importance of production scheduling in achieving operational efficiency and meeting customer demands. Various strategies, such as lean manufacturing, just-in- time (JIT) production, and advanced planning and scheduling (APS) systems, are analyzed for their effectiveness in improving production scheduling. Additionally, the report explores technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) that can enhance production scheduling capabilities. Case studies and examples are used to illustrate how these strategies and technologies have been implemented successfully in real-world manufacturing settings. The report concludes with recommendations for manufacturers looking to optimize their production scheduling processes.

Re-Entrant Green Scheduling Problem of Bearing Production Shops Considering Job Reworking

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling.

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

An ensemble of brain storm optimization and Q-learning methods for distributed flexible job shop scheduling problems with distribution operations

Distributed manufacturing scheduling problems have attracted much concern from both industrial and academic areas. Nevertheless, distributed scheduling problems with distribution operations are seldom studied. This work proposes a distributed flexible job shop scheduling problem with distribution operations. A set of jobs is handled at distributed flexible job shops, and then the finished jobs are transported to their corresponding customers following given due dates. First, a mixed integer programming model is established to minimize total tardiness. Second, an ensemble of brain storm optimization and Q-learning methods is developed to solve the formulated model. Six heuristics are hybridized to generate a high-quality initial population. A Q-learning method is devised by fully employing found search information to guide subsequent search processes instead of using fixed parameters as basic brain storm optimization. A variable neighborhood search method combining problem-specific knowledge is designed to further refine the found best individual. At last, the formulated model and method are compared with three state-of-the-art metaheuristics and a mathematical programming solver CPLEX via using a group of problem instances. The results and analysis demonstrate that the developed model and algorithm have more powerful competitiveness in addressing the studied problem.

Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems

This paper addresses the challenges associated with nesting and production scheduling in additive manufacturing (AM). The problem studied consists of grouping a set of parts into batches, which are then assigned to and sequenced across the available machines, guaranteeing the production of all parts. This work stands out by proposing exact methods for the AM nesting and scheduling problem considering irregular-shaped parts with specific release dates, processing times, and due dates, with the aim of minimizing the cumulative tardiness. The proposed approaches include two logic-based Benders decompositions: one combining Mixed Integer Programming (MIP) and Constraint Programming (CP), and the other relying solely on CP. To deal with the sub-problems, a strategic procedure was developed to reduce the solution space while maintaining low resolution times per iteration. Problem-specific cuts are also generated to improve the efficiency of these approaches. Computational experiments show that both decompositions significantly outperform a prior monolithic CP model, with the decomposition based solely on CP yielding the best results. Moreover, the results show that this approach has the potential to achieve similar computational performance of non-exact approaches that are currently considered state-of-the-art. A set of instances is provided to serve as a benchmark for future studies.

Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence

Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence

Job shop smart manufacturing scheduling by deep reinforcement learning

Smart manufacturing scheduling (SMS) requires a high degree of flexibility to successfully cope with changes in operational decision level planning processes in today's production environments, which are usually subject to high uncertainty. In such a unique and complex scenario as the real job shop, the modelling of SMS as a Markov decision process (MDP), and its approach by deep reinforcement learning (DRL), is a research field of growing interest given its characteristics. It allows us to consider achieving high flexibility levels by promoting process automation, autonomy in decision making, and the ability to act in real time when faced with disturbances and disruptions in a highly dynamic environment. This paper addresses the problem of scheduling a quasi-realistic job shop environment characterised by machines receiving jobs from buffers that accumulate numerous jobs using a wide variety of parts and multimachine routes with a diverse number of operation phases by developing a digital twin of the job shop based on a MDP with the DRL methodology. This is approached by: modelling the job shop scheduling environment with OpenAI Gym; designing an observation space with 18 job features; designing an action space composed of three priority heuristic rules; shaping a single reward function with a multi-objective characteristic; using the implementation of the proximal policy optimisation (PPO) algorithm from the Stable Baselines 3 library. This modelling approach, dubbed as job shop smart manufacturing scheduling (JS-SMS), is characterised by deterministic formulation and implementation. The model is subjected to validation by comparing it to several of the best-known heuristic priority rules. The main findings of this methodology allow to replicate, to a great extent, the positive aspects of heuristic rules and to mitigate the negative ones, which achieves more balanced behaviour in most of the measures established as performance indicators and outperforms heuristic rules from this multi-objective perspective. Finally, further research is oriented to dynamic and stochastic approaches to address the job shop reality in an Industry 4.0 context.

A production scheduling case study solved for Electron Beam Powder Bed Fusion

Additive Manufacturing (AM) represents a pillar of the modern industrial revolution. Its peculiar characteristics make AM largely differ from conventional manufacturing; thus, the approach to the production planning and control must change and be adapted to such technology. While for conventional manufacturing nesting and scheduling decisions are taken at different planning stages, they are both made in daily operations when AM technology is considered. This paper proposes a case study applied to the powder bed fusion process with electron beam (PBF-EB) and involving the problem of scheduling parts in a distributed production system composed of many parallel identical facilities. According to the typical PBF-EB production stages, each facility also has a second stage for cleaning and support removal operations, and only a subset of facilities has a third stage for surface finishing and post-heat treatments. In the case study, the scheduling problem is solved by a proposed genetic algorithm to minimize the makespan.

Lithography reticle scheduling in semiconductor manufacturing

The lithography process in semiconductor dynamic random-access memory (DRAM) fabrication plants (fabs) is usually a major bottleneck, and reticle scheduling is complicated by process-specific constraints such as diversity of the product mix and rapidly changing deadlines. This study proposes a three-phase scheduling framework to solve the multi-objective reticle scheduling problem, reducing capacity loss, maintaining line balance and meeting customer demand. The genetic algorithm (GA) and the technique for order preference by similarity to ideal solution (TOPSIS) are used to optimize the scheduling with a trade-off among the multiple objectives. This study tests the proposed framework based on a real semiconductor DRAM (fab) in Taiwan, which has two unrelated parallel machines in its lithography process. The study finds that the proposed framework results in better scheduling reports, reduced computation times and improvements in the overall performance of lithography equipment.

Advanced Outfitting Techniques for Shipyard Development: A 2000 GT Shipbuilding Case Study

The shipbuilding sector is currently facing issues due to long manufacturing schedules and delays in ship construction completion. These challenges come from national shipyards' conventional onboard outfitting process for equipment installation, which takes a lengthy time to complete. To address this issue, this study proposes implementing the advanced outfitting system (AOS) approach, which combines the zone outfitting system with the hull block construction method, in building a 2000 GT ship at PT. X in Surabaya, Indonesia. Technical analysis is performed to plan the shipbuilding process using the hull block construction method, and the efficiency of man-hours is calculated by implementing the AOS approach. The study also conducts an analysis of shipbuilding costs and the required shipyard development investment. The technical analysis shows that the AOS approach improves efficiency factors by enhancing material handling and testing activities. The AOS method has an efficiency factor of 50% for on-unit outfitting and 62% for on-block outfitting, compared to on-board outfitting. The construction of a 2000 GT ship employing the AOS approach represents a reduction of approximately 9.6% in working days and a cost-saving of approximately 4.3% when compared to the onboard outfitting method, resulting in savings of IDR 1,933,277,000 or about USD 124,175. To implement the AOS approach, an investment of IDR 3,040,776,000 or about USD 195,310 is required for construction facilities and human resources development. The return on investment is obtained after two 2000 GT ships are completed. As a result, the AOS approach can improve shipbuilding productivity by reducing construction time and costs. This study provides insights into the benefits of implementing modern shipbuilding techniques in the industry.

The Problem of Machine Part Operations Optimal Scheduling in the Production Industry Based on a Customer’s Order

This research focuses on small- and medium-sized businesses that provide machining or other process services but do not produce their own products. Their daily manufacturing schedule varies according to client needs. Small- and medium-sized businesses strive to operate in these circumstances by extending their customer base and creating adequate production planning targets. Their resources are limited, including the technical and technological components of their equipment, tools, people resources, time, and capacities. As a result, planning operations with the present resources of small- and medium-sized businesses in the midst of the global economic crisis is a widespread issue that must be addressed. This study seeks to offer a novel mathematical optimization model based on a genetic algorithm to address job shop scheduling and capacity planning difficulties in small- and medium-sized businesses, therefore improving performance management and production planning procedures. On the basis of the created optimization model, an appropriate software solution, and quantitative data concerning the job shop scheduling and capacity planning challenges of manufacturing operations in small- and medium-sized businesses, the study findings will be obtained. The practical implications include the establishment and development of a decision support system based on the genetic algorithm optimization method, which may improve the effectiveness of the flexible job shop scheduling problem and capacity planning in the production planning process. The given model and the application of the differential precedence preservative crossover operator within genetic algorithms are what constitute the novelty of this study.

Integrated optimization of process planning and scheduling problems based on complex networks

Industrial information integration can promote modern manufacturing enterprises to achieve efficient information processing and data integration in manufacturing systems, providing a good framework for solving process planning and workshop scheduling integration problems. To explore optimization issues in an intelligent manufacturing environment, find a better way to solve the integration of process planning and scheduling, and analyze basic features of manufacturing task-driven workshop scheduling network. The paper constructs a multi-process relational network model based on processes and machines in accordance with the complex network theory. Moreover, this paper proposes a multi-objective integrated model that minimizes maximum makespan, minimum machine load, and minimum machine energy consumption with the constraints of the workpiece, process, and machine. This paper uses the NSGA-II algorithm strategy to analyze the proposed model and issue. The feasibility of the model and algorithm proposed is demonstrated by solving various optimization objectives in actual workshop manufacturing tasks. Then, it analyses and evaluates the network structure characteristic index of the production and manufacturing scheduling process, which provides a decision basis for the task processing process design and scheduling planning in the intelligent manufacturing.