Dynamic Manufacturing System Research Articles

Mathematical formulation and a hybrid evolution algorithm for solving an extended row facility layout problem of a dynamic manufacturing system

• An extended row facility layout problem is investigated considering dynamic manufacturing environment. • Specific row layout changes over multiple periods and a mixed integer linear programming model is built for it. • A hybrid evolution approach combining linear programming and evolutionary operations is developed. • A special decoding approach for the extended row facility layout problem is designed. • Comparison with other methods indicates that the proposed algorithm achieves highly preferable results. In this study, a dynamic extended row facility layout problem is investigated where the dynamic manufacturing process is considered. This problem involves the rearrangement cost when the location of the department changes over adjacent periods and it is aimed at minimising the sum of total material handling and rearrangement costs. The specific number of rows in each period is not predetermined in this study. Based on the product processing in practice, the essential clearance between departments and floor boundaries is also considered when calculating relative positions and exact locations of departments. A mixed-integer linear programming model is established for this problem. A hybrid evolution algorithm (HEA) that combines the exhaustive pairwise swap and variation operations is proposed to resolve this problem. First, a decoding strategy is designed to represent the department sequence and the optimal exact locations are solved using the linear programming method. Second, an exhaustive pairwise swap operation is addressed to search for the neighbourhood space without repeated calculations. This operation terminates when all swap pairs run out or preferable solution is found. Therefore, the variation operation starts immediately to switch to a new neighbourhood space. The HEA is tested with many instances and its good performance has been proven by comparing its results with existing solutions of fixed-row, flexible-row facility layout problems, and results of exact approach (CPLEX).

Behavior of decision forest classification in dynamic manufacturing systems

Managers of manufacturing systems are constantly looking for ways to predict future production conditions. Due to the system’s complexity, modelling is effortful and never completed. So-called random forests of decision trees seem to be a promising machine-learning tool to forecast key figures of manufacturing systems. The selection of data to teach such classifiers significantly influences the quality of the prediction. However, quality of data and prediction decreases in case of a dynamic system. This paper deals with one possible way of data handling for decision forests in changing manufacturing system.

Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor

ABSTRACT In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods.

Approximations for dynamic multi-class manufacturing systems with priorities and finite buffers

Capacity planning models for tactical to operational decisions in manufacturing systems require a performance evaluation component that relates demand processes with production resources and system state. Steady-state queueing models are widely used for such performance evaluations. However, these models typically assume stationary demand processes. With shorter new product development and life cycles, increasing customization, and constantly evolving customer preferences the assumption of stationary demand processes is not always reasonable. Nonstationary demand processes can capture the dynamic nature of modern manufacturing systems. The ability to analyze dynamic manufacturing systems with multiple products and finite buffers is essential for estimating throughput rates, throughput times, and work-in-process levels as well as evaluating the impact of proposed capacity plans and resource allocations. In this article, we present computationally efficient numerical approximations for the performance evaluation of dynamic multi-product manufacturing systems with priorities and finite buffers. The approximation breaks time into short periods, estimates throughput and arrival rates based on system status and current arrival processes, and then pieces the periods together through flow balance equations. The dynamic nature of product demands is modeled through non-homogeneous Poisson processes. The performance of these approximations is presented for practically sized flowshops and jobshops.

A dynamic scheduling mechanism of part feeding for mixed-model assembly lines based on the modified neural network and knowledge base

Inspired by the manufacturing costs proportion of part feeding in automotive mixed-model assembly lines (MMALs) being up to 20–35%, this paper takes the dynamic scheduling of part feeding for automotive MMALs as a crucial and complex problem. Therefore, a dynamic scheduling mechanism basing on the knowledge base (KB) and fruit fly optimization algorithm (FOA) with variable step sizes and logistic chaos (VSCFOA)-enhanced general regression neural network (VSCFOA-GRNN) is proposed to tackle the real-time part feeding scheduling problem of tow trains under the dynamic manufacturing system. A mathematical model is developed to illustrate the problem, where the throughput of the assembly line and the material delivery distance are determined as components of the objective function. Subsequently, samples of the MMAL are generated by the plant simulation software and used to train the VSCFOA-GRNN model off-line. Afterward, the trained model and KB are adopted in the real-time scheduling process to determine the optimal scheduling rule combination. Finally, the effectiveness, feasibility and accuracy of the novel scheduling mechanism are validated by computational results, especially in dynamic scheduling processes. It can cope well with changes in the dynamic environment, thus effectively realizing the higher productivity of assembly lines and better system performance.

Material distribution method in discrete manufacturing systems and a case study from engine builders

In a discrete manufacturing enterprise, it is difficult to satisfy the fast matching and accurate supply of production materials or the demand of modern dynamic production systems using passive material supply. With a focus on engine builders, this study explores the active material configuration and systematic material distribution approaches. Due to the material characteristics and the granularity of logistics demand, the material distribution is arranged in reverse chronological order, and the corresponding mathematical modeling of the distributing period is proposed to achieve lean manufacturing. To ensure exact material distribution time, the allocation material configuration model that works best for the dynamic manufacturing system is presented by means of system modeling. Next, a production material distribution method for the general assembly line and the sub-assembly line with a closely related production sequence is proposed to achieve the exact match of manufacturing and material resources through the analysis of data fusion and logistics resource matching. Finally, a simulation is conducted using production data gathered from engine builders. The results indicate the effectiveness of the proposed active material configuration method. The outcome of this study can be used as a guide for the time planning of material flow in a dynamic manufacturing system and can provide a new research perspective on production logistics or distribution in a production line.

Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models

Modeling complex systems as networks of interacting elements has gained increased attention in recent years. So far, network modeling in manufacturing and logistics has often focused on the description of system properties. In the data-driven world of smart manufacturing, creating material flow network models becomes a lot easier due to the ubiquitous availability of shop floor and transportation data. At the same time, these highly flexible and continuously changing smart manufacturing systems become less predictive and thus less controllable. This article investigates how the stochastic block model (SBM), a network model with a stochastic description of interconnections, can be applied to model and predict material flows in manufacturing systems. We show how to utilize its properties to forecast the dynamic development of the structure of such systems. The complete process from network modeling using material flow data to the prediction of the future development of the network is demonstrated. Different SBM variants are tested using six company data sets and evaluated in competition with classical machine learning methods for prediction. Our results show that selected SBM variants achieve the best performance in prediction in most scenarios and thus have the potential to play an important role in the management of future dynamic manufacturing systems.

A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance

The operational quality and reliability of a manufacturing system is greatly influenced by uncertain or variable environments, therefore robustness is one of the most important indicators for measuring the operational quality of the non-repetitive dynamic manufacturing system. Controlling the order release to limit work in process at a stable level and protect throughput from variation is crucial to achieving robustness of manufacturing system performance. To deal with the influences of bottleneck severity and variable resource on system performance, a refined order release method is presented, which releases order periodically based on the corrected aggregate load and continuously based on the bottleneck buffer load. The operational quality of this method with the classical order release method under non-repetitive dynamic manufacturing system is compared by modeling and simulation. The results show that the refined order release method is more robust for general flow shop with higher protective capacity and resource variability.

The model-based product agent: A control oriented architecture for intelligent products in multi-agent manufacturing systems

The multi-agent control strategy has been previously shown to improve the flexibility of complex, dynamic manufacturing systems. One key component of this strategy is the product agent. The product agent is responsible for autonomously guiding a physical part in the manufacturing system based on its production goals. Though the product agent has been described in previous works, a fully developed software architecture for the product agent that uses a model-based optimization approach has not been proposed. In this work, a product agent architecture with the capabilities to explore the local environment, plan and schedule events based on its knowledge, and request desired actions from the resources in the system is presented and tested.

Machine Reliability in a Dynamic Cellular Manufacturing System: A Comprehensive Approach to a Cell Layout Problem

Machine Reliability in a Dynamic Cellular Manufacturing System: A Comprehensive Approach to a Cell Layout Problem

Data-driven production control for complex and dynamic manufacturing systems

Digitalization allows for production control based on the current state of the manufacturing system. Thereof, this paper proposes and applies a data-driven adaptive planning and control approach that uses simulation-based optimization to determine most suitable dispatching rules in real-time under varying conditions. The data integration between the real manufacturing system and the simulation model is implemented through a data-exchange framework. The approach is evaluated in a scenario of a Brazilian manufacturer of mechanical components for the automotive industry, achieving better operational performance compared to the procedure previously applied by the company as well as in comparison to static dispatching rules.

Мультиагентне середовище поетапного синтезу системи динамічного керування у гнучкій виробничій системі

In this research an approach to automating the process of synthesis of systems of dynamic control flexible manufacturing systems using a multi-agent model with built-in relational mechanism. FIMAS containing AOS for each property of the SDCC, interpretative model of choice adequate DCS is the basis for the implementation of the automation of the process. Automating this process allows to avoid the influence of subjective factors related to the competence of the operator and implemented in the form of automated logic synthesis phased DCS

A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems

The increasing customisation of products, which leads to higher numbers of product variants with smaller lot sizes, requires a high flexibility of manufacturing systems. These systems are subject to dynamic influences and need increasing effort for the generation of the production schedules and for the control of the processes. This paper presents an approach that addresses these challenges. First, scheduling is done by coupling an optimisation heuristic with a simulation model to handle complex and stochastic manufacturing systems. Second, the simulation model is continuously adapted by real-time data from the shop floor. If, e.g., a machine breakdown or a rush order appears, the simulation model and consequently the scheduling model is updated and the optimisation heuristic adjusts an existing schedule or generates a new one. This approach uses real-time data provided by future cyber-physical systems to integrate scheduling and control and to manage the dynamics of highly flexible manufacturing systems.

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

Design of a Dynamic Additive Manufacturing System for Use on Free-Moving Human Anatomy1

Design of a Dynamic Additive Manufacturing System for Use on Free-Moving Human Anatomy1

Additive Manufacturing Cloud via Peer-Robot Collaboration

When building a 3D printing cloud manufacturing platform, self-sensing and collaboration on manufacturing resources present challenging problems. This paper proposes a peer-robot collaboration framework to deal with these issues. Each robot combines heterogeneous additive manufacturing hardware and software, acting as an intelligent agent. Through collaboration with other robots, it forms a dynamic and scalable integration manufacturing system. The entire distributed system is managed by rules that employ an internal rule engine, which supports rule conversion and conflict resolution. Two additive manufacturing service scenarios are designed to analyse the efficiency and scalability of the framework. Experiments show that the presented method performs well in tasks requiring large-scale access to resources and collaboration.

Optimization and Prediction Approaches for Flexible Routing in Dynamic Environment FMS

Recently, many production systems that work in dynamic environment are widely used in high volume industries. Increasing the product varieties in dynamic environment Flexible Manufacturing Systems (FMS) have gained more importance due to growing production lines' complexity. Increasing the customers demand to diversified products and rapid changing of the need to certain types of products require an FMS with high ability to adapt any change in production ratio during production. This paper introduces a Two Stages Approach (TSA) to increase the flexibility of the FMS to any change of the production ratios during production. The first stage of TSA is to propose a Genetic Algorithm (GA) based Production Simulator (GA-PS) to maximize the FMS throughput by optimizing the FLXible Routes (FLXR) for all products type at a given production ratio. The second stage is to propose a Neural Network (NN) approach to predict the routes of all products in FMS that can be used efficiently instead of a long, time-consuming GA-PS. TSA can improve the response of FMS to any change of the production ratio by finding a new FLXR that achieves the optimal throughput of the FMS within the new production ratios. Numerical examples will be applied to demonstrate applicability of the proposed approach. As a result, it could be ascertained that the proposed TSA is useful.

Design of a decentralized modular architecture for flexible and extensible production systems

In the European manufacturing industry, production batches are decreasing, resulting in an increase in required changeovers. Companies are pushed to respond quickly and cost-efficient to changing markets. One way for Small and Medium sized Enterprises (SMEs) to become more agile, is to incorporate industrial robots in their production processes; larger enterprises already use this versatile piece of equipment for large batch sizes. To do so, a new perspective and approach is required, tailored to dynamic manufacturing systems in which production systems and components can be easily reconfigured, altered, swapped or replaced. A modular system architecture was developed within project SInBot upon which challenges in dynamic cooperation between robots and humans were projected. The developed system uses decentralized control and distributed intelligence, linked through an extensible, flexible, and fault-tolerant communication architecture. The rapidly growing complexity of traditional systems is compared to its decentralized counterpart to illustrate some of the many advantages of this new system architecture. A manufacturing scenario analogue is discussed and the approach to verify the performance of inner-system components, as well as the benefits of this approach. Key challenges that are encountered in implementing the cooperation into the current industrial environments are identified and projected upon the verification system.

Layout design optimization of dynamic environment flexible manufacturing systems

The proper positioning of machine tools in flexible manufacturing system is one of the factors that lead to increase in production efficiency. Choosing the optimum position of machine tools curtails the total part handling cost between machine tools within the flexible manufacturing system. In this article, a two-stage approach is presented to investigate the best locations of the machine tools in flexible manufacturing system. The location of each machine tool is selected from the available specific and fixed locations in such a way that it will result in best throughput of the flexible manufacturing system. In the first stage of the two-stage approach, the throughput of randomly selected locations of the machine tool in flexible manufacturing system is computed by proposing a production simulation system. The production simulation system utilizes genetic algorithms to find the locations of the machine tools in flexible manufacturing system that achieve the maximum throughput of the flexible manufacturing system. In the second stage, the generated locations are fed into artificial neural network to find a relation between a machine tool’s location and the throughput that can be used to predict the throughput for any other set of locations. Artificial neural network will result in mitigating the computational time.

Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

Stochastic dynamic job shop scheduling problem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90% and setup times less than processing times. Results indicate that shortest setup time (SIMSET) rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date (JMEDD) rule provides the best performance for makespan, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.