Assembly Shop Research Articles

A multi-objective energy-efficient scheduling algorithm for solving hybrid flow workshops with parallel heterospeed machines

Abstract. In hybrid flow assembly workshops, minimizing completion time and reducing energy consumption are two key indicators of efficient workshop management, which are often inversely related. This study investigates how parallel heterospeed machines with adjustable times can reduce energy costs and assembly times at the production system level. To address these issues, we propose the MO-HFSP-Batch algorithm based on the enhanced NSGA-II algorithm, aimed at improving the algorithm's optimization capability. Given the trade-offs between optimization objectives and the high computational complexity of the proposed multi-objective mixed integer programming, a three-tier chromosome encoding structure was introduced during the algorithm design phase. This structure meets the triple requirements of batching assembly tasks, matching operations with parallel machines, and sequencing operations in actual hybrid flow assembly workshops. Extensive data analysis proves that our proposed algorithm effectively solves the scheduling issues in hybrid flow workshops and performs better in solving completion time indicators and stability of non-dominated solution sets than other algorithms.

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop scheduling problem dominant in real-world printed circuit board assembly shops. A novel multi-objective hyper-heuristic combining Q-learning, i.e., two-stage improved spider monkey optimization (TS-ISMO), is tailored to address the complexities of the flow shop scheduling problems. The proposed method aims to simultaneously optimize conflicting objectives such as minimizing makespan, total energy consumption, and total tardiness time while incorporating lot streaming considerations. For multi-objective hyper-heuristic techniques, the algorithm dynamically selects and adapts a diverse set of low-level heuristics to explore the solution space comprehensively and strike a balance among competing objectives. The proposed TS-ISMO algorithm incorporates several significant features aimed at enhancing its performance. These features encompass hybrid heuristics for solution initialization, a contribution value method for comprehensive convergence and diversity assessment, diverse evolutionary state judgments to promote the algorithm’s balance between exploration and exploitation capabilities, and a Q-learning strategy for self-adaptive parameter tuning. The integration of Q-learning facilitates intelligent parameter control, enabling the algorithm to autonomously adjust its behavior based on past experiences and evolution dynamics. This adaptive mechanism enhances convergence speed and solution quality by effectively guiding the search process toward promising regions of the solution space. Extensive computational experiments are conducted on benchmark instances of hybrid flow shop scheduling problems with lot streaming to evaluate the performance of the proposed algorithm. Comparative analyses against state-of-the-art approaches demonstrate its superior solution quality and computational efficiency.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

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

Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops

In the automobile manufacturing process, the complexity of the production environment and the different objectives of the upstream and downstream shops necessitate the resequencing of the car sequences using buffers. This paper addresses a car resequencing problem in automotive assembly shops by utilising selectivity banks to reshuffle the given upstream sequences to minimise the number of violations of sequencing rules in the downstream sequence. A new resequencing approach combining heuristics and deep reinforcement learning is proposed to solve the problem. Heuristics solve the automobile storage problem, while a deep reinforcement learning method based on proximal policy optimisation addresses the automobile release problem. Three state matrices, agent actions, and the reward function are designed based on the characteristics of the objective and selectivity banks. Additionally, a novel network structure combining convolutional neural networks and long short-term memory networks is proposed to train the agent. The results demonstrate that the proposed method outperforms two efficient deep reinforcement learning algorithms and two heuristics and obtains better solutions in handling dynamic events.

Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm's optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5-6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm.

IMPROVING ORGANIZATIONAL PROCEDURES TRAINING FOR CNC MACHINE OPERATORS

The article is devoted to the issues of improving methodological tools and procedures for conducting organizational training of workers at primary sites in the conditions of modernization of production processes. The introduction of modern, high-tech equipment, digitalization of production, transition to new work standards, as well as optimization of the technological process create the need for a more rapid response to the requests of performers, in particular operators of numerically controlled machine tools. Based on the analysis of theoretical sources, the features of traditional and learning organizations are considered, their distinctive characteristics are presented, the factors influencing the need to invest in in-house training are studied, the sequence of stages in the implementation of in-house training of production workers is shown, and their brief characteristics are given. The study of the existing in-house training system at the enterprise made it possible to identify its shortcomings and propose a number of measures to improve the procedure and methodological tools. In particular, it is proposed to create a training sector on the basis of a mechanical assembly shop for operators of numerically controlled machine tools, which will minimize existing problems with organizational training for this category of workers and reduce the load on the training center, the personnel service of the enterprise, and also presents draft improved training program. The proposed activities will ultimately contribute to the fact that operators of machine tools with numerical control will receive deeper knowledge and skills related to the specifics of software and machining stages on new type equipment, as well as in the process of work will be able to more fully analyze the situation and select the best machining modes, taking into account the available tools.

A multi-level action coupling reinforcement learning approach for online two-stage flexible assembly flow shop scheduling

Multi-product centralized delivery and kitting assembly present significant challenges to hierarchical co-processing in multi-stage manufacturing systems. The combinations of priority dispatching rules at each level are transiently adaptive, and the performance in online scheduling deteriorates rapidly with changing environment. This paper investigates the selection of rule combinations for sustained high-performance responsive scheduling in two-stage flexible assembly flow shop scheduling problem with asynchronous execution and complex decision correlation. A Multi-Level Action Coupling Deep Q-Network (MALC-DQN) approach is proposed for adaptive integrated scheduling in hybrid processing and assembly shops. Firstly, the problem is skillfully established as an event-triggered integrated decision markov decision process. The prioritized batch experience replay mechanism is employed to retain the complete correlation information of key decision sequences. Then, coupling and sequence feature extraction modules are developed to enhance the agent’s ability to perceive execution process and the environment. Furthermore, the multi-level wait-limit mechanism and efficient action filtering mechanism are designed to mitigate ineffective waiting waste and action space explosion during learning. Finally, a series of sophisticated experiments are conducted to validate the effectiveness of the proposed methodology. In 20 actual instances of different sizes, MLAC-DQN outperformed its closest competitor, with a 26.6% improvement in average tardiness. Moreover, extraordinary robustness is demonstrated in 16 sets of experiments involving different configurations of resources, orders, and arrival concentration levels.

An Improved Ant Colony Algorithm with Deep Reinforcement Learning for the Robust Multiobjective AGV Routing Problem in Assembly Workshops

Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed.

Distributed assembly shop scheduling problem for complex products considering multiskilled worker assignment and transportation time

The distributed assembly hybrid flow shop scheduling problem (DAHFSP) is a type of distributed shop scheduling problem, and each distributed shop can be regarded as a hybrid flow shop. In distributed assembly processes for complex products such as satellites and missiles, transportation time and worker assignment have important effects on production scheduling. Based on real production situations, this study proposes a novel DAHFSP considering worker assignment and transportation time, aiming to minimise the makespan and the imbalance degree of worker workloads. First, to solve these problems, we construct a mathematical model and design a two-layer chromosome coding scheme including worker assignment and task sequence. Then, in the local search stage, we propose a mutation-based search method and an elite search method. On that basis, we propose a multi-objective evolutionary algorithm with reinforced elite retention strategy (MOEA-RERS). Finally, based on a set of 12 test instances generated by actual enterprise production data, we compare the MOEA-RERS algorithm with five multi-objective evolutionary algorithms. The results show that the MOEA-RERS algorithm is superior to other algorithms in terms of solution quality and distribution.

Analysis Of The Implementation Of Material Flow Cost Accounting At Pt Xyz (Car Manufacturer)

This research discusses implementation analysis Material Flow Cost Accounting (MFCA) at PT XYZ which is one of the car manufacturers in Indonesia. The formulation and aim of this research is to analyze potential calculations material loss and material waste produced by the method Material Flow Cost Accounting (MFCA) and evaluate management's response to management material loss and material waste from the calculations carried out. The research method used is quantitative descriptive method and case study. Resource persons in this study include: 1) Press Shop Manager 2) Body Shop Manager 3) Paint Shop Manager and 4) General Assembly Shop Manager. The data used includes primary data collected by observation and interviews. The data analysis method is carried out through five implementation stages Material Flow Cost Accounting (MFCA) in ISO 14051 and thematic analysis from sources as well as drawing conclusions and recommendations. The research results show that with implementation Material Flow Cost Accounting (MFCA) PT XYZ is able to map all material flows in detail and provide information regarding waste that can be used to increase efficiency and support environmental sustainability in the car production process.

IMA Genome – F19

The pace at which Next Generation Sequence data is being produced continues to accelerate as technology improves. As a result, such data are increasingly becoming accessible to biologists outside of the field of bioinformatics. In contrast, access to training in the methods of genome assembly and annotation are not growing at a similar rate. In this issue, we report on a Genome Assembly Workshop for Mycologists that was held at the Forestry and Agricultural Biotechnology Institute (FABI) at the University of Pretoria, South Africa and make available the 12 draft genome sequences emanating from the event. With the aim of making the process of genome assembly and annotation more accessible to biologists, we provide a step-by-step guide to both genome assembly and annotation, intended to encourage and empower mycologists to use genome data in their research.

Construction of Intelligent Perception Network Based on Rocket Assembly Workshop

The rocket assembly is a crucial link in the rocket production process. With the increase of national space missions, how to quickly adapt to the high-density launch stage and improve the efficiency and quality assurance of the assembly workshop has become an urgent problem to be solved. Aiming at the problems of the low production efficiency caused by low information level of production site and the low intelligent degree of material management and distribution, an intelligent perception network based on the rocket assembly workshop is proposed in this paper. This intelligent perception network is equipped with functionalities such as intelligent perception, adaptive adjustment, data communication, remote monitoring, and management. It is designed to not only increase the intelligence level of assembly workshops but also effectively address the issue of low production efficiency.

Design and Analysis of Lifting Carriage of 4T Overhead Stacker Crane

The Overhead Stacker Crane is a hybrid device that combines the features and constructions of "Industrial Forklifts" and "Electric Overhead Cranes." Stacker cranes are equipment with varied levels of automation that are used to store materials. They travel along the aisles of the operating areas, where they enter, position, or extract the material. This type of crane is widely used in assembly shops, production stores, warehouses, etc. Each application needs customizing. The lifting carriage of different overhead stacker crane design is studied in this study. The new design has two advantages: the lifting carriage is bracketed to the mast of the overhead stacker, and the new lifting mechanism is more compact. The most common structure configurations are single-mast and twin-mast constructions. Single-mast structures are used for investigation in our research. A live example has been chosen, and several design engineering stages have been implemented, with the goal of achieving compliance with safety-related standards as well as possible enhancements for future consideration. All the concerned parts have been 3D modelled, analyzed using classical means as well as advanced means of FEA. The goal of using design engineering concepts is to provide a verified design with documentation that can be manufactured with or without indicated improvements. Static analysis of identified parts is performed using both hand calculation and the FEA approach.

Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness (TTD) and total energy consumption (TEC) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize TEC. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.

An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cells

The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is a classical and important research direction. However, current research usually considers the case where each manufacturing cell has a fixed and constant process capability. There are often situations in non-machining shops where each manufacturing cell is capable of capability reconfiguration and performs many different types of process operations, such as assembly shops and test shops. The variability of the manufacturing cell's capabilities increases the complexity of the problem compared to traditional FJSP. In this paper, we study the dynamic flexible job shop scheduling problem considering reconfigurable manufacturing cells (DFJSP-RMCs) with completion time, delay time and reconfiguration time as optimization objectives, and propose an improved Genetic Programming Hyper-Heuristic (GPHH) method to solve it. The method weighs the solution efficiency and the effectiveness of the results. In addition, an individual simplification policy (ISP) is proposed to reduce the evaluation time of the heuristic. Finally, random instances were generated under three production conditions and 10 independent runs were performed for each. Experiments show that the proposed method significantly reduces the time consumption while ensuring the quality of the results.

Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

The energy crisis and environmental pollution are receiving increasing attention from governments and communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources from end-of-life products and produce as-new products. Since remanufacturing systems involve a series of disassembly, processing and assembly operations, remanufacturing schedule integrates disassembly, processing and assembly shops. A multi-objective scheduling of remanufacturing systems is proposed, considering workstation use, energy consumption and customer satisfaction simultaneously. A chance-constrained programming model is established to minimize makespan and energy consumption while satisfying total tardiness requirements. A hybrid method is developed, using group teaching optimization and a discrete event simulation system, which can seek and evaluate potentially favourable solutions. The approach is validated on a group of test instances using well-known methods. The results reveal that this method can find non-dominated solutions with well-converged and well-diversified performance, verifying its advantages in providing informed decisions for managers and engineers.

Assembly workshops in virtual reality as an integral part of the assembly planning process

Within the assembly planning process for automotive assembly lines, virtual reality (VR) systems are used mainly on a project-by-project basis. So far, there is no information about the knowledge gained from such workshops and the transferability of the results. Integration of VR as a standard process within the assembly planning has considerable potential. This paper addresses the question of how shopfloor employees and assembly planners can work efficiently in virtual environments on a solution for a concrete planning problem. Existing concepts for virtual workshops are analyzed and a multi-stage procedure for assembly workshops is derived. The assembly workshops in VR are used as a communication platform for different stakeholders, with a focus on the exchange with shopfloor employees. An implementation of the concept concerning its functionality is carried out based on a use case of the design of assembly tools for an automobile assembly line. For this purpose, an assembly platform for the accessibility of an assembly point inside the rear trunk of an automobile is designed in a joint VR workshop and transferred to an operational assembly line. The results show a broad acceptance of the developed solution among the employees.

An optimization approach for assembly job shop order release based on clearing functions

As an integral part of production planning control, order release management is critical to enhance the competitiveness and production efficiency of companies. Previous literature shows limited application of optimization-based models in assembly job shops, primarily due to the intricate nature of product structures and assembly operations. Therefore, based on the idea of the allocated clearing function (ACF) model, we introduce material flow constraints and complex assembly structure constraints during the assembly stage, proposing the assembly job shop allocated clearing function (AACF) model. The performance of the AACF model and the rule-based mechanisms in terms of cost and timing measures are compared through experiments containing 6 factors and 96 scenarios. The results show that the AACF model performs better in terms of cost management, service level and order due date deviation. In addition, a sensitivity analysis of the objective function parameters is performed to confirm the robustness of the AACF model. Finally, a case application in a real assembly shop illustrates the feasibility and validity of the proposed AACF model.

Augmented reality guided autonomous assembly system: A novel framework for assembly sequence input validations and creation of virtual content for AR instructions development

In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user’s view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.