Manufacturing System Configuration Research Articles

RESILIENT CIRCULARITY IN MANUFACTURING: SYNERGIES BETWEEN CIRCULAR ECONOMY AND RECONFIGURABLE MANUFACTURING

Abstract Reconfigurability in manufacturing signifies a system's capacity to promptly adapt to evolving needs. This adaptability is critical for markets to maintain operations during unexpected disruptions, including weather anomalies, cyber-attacks, and physical obstructions. Concurrently, the concept of a circular economy is gaining popularity in manufacturing to mitigate waste and optimize resource utilization. Circular economy principles aim to reduce environmental impacts while maximizing economic benefits by emphasizing the reuse of goods and resource byproducts. The nexus between reconfigurability and the circular economy stems from their shared pursuit of sustainability and resilience. Interestingly, biological ecosystems also exhibit these traits, showcasing exceptional adaptability to disturbances alongside the ability to effectively utilize available resources during normal operations. This study explores various manufacturing system configurations to assess both their adaptability and connection to circular economy principles. 44 configurations are categorized based on layout (e.g., job shop, flow line, cellular) and analyzed using convertibility, cyclicity, and Degree of System Order metrics. A significant positive correlation (R2 = 0.655) is found between high convertibility and ecologically similar levels of structural cycling, suggesting that effective resource utilization supports adaptability in manufacturing systems. Furthermore, this paper proposes the existence of a possible “window of vitality” for cyclicity, as it demonstrates a significant correlation (R2 = 0.855) between the Degree of System Order and cyclicity. Identifying systems that strike a balance between redundancy, efficiency, convertibility, and cyclicity can aid manufacturing system designers and decision-makers in making choices that address increasing requirements for both sustainability and resilience.

Multi-factor integrated configuration model and three-layer hybrid optimization algorithm framework: turnkey project-oriented rapid manufacturing system configuration

Multi-factor integrated configuration model and three-layer hybrid optimization algorithm framework: turnkey project-oriented rapid manufacturing system configuration

Realisation of responsive and sustainable reconfigurable manufacturing systems

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing.

Industry 4.0-Oriented Turnkey Project: Rapid Configuration and Intelligent Operation of Manufacturing Systems

More extensive personalized product requirements and shorter product life cycles have put forward higher requirements for the rapid establishment, commissioning, and operation of corresponding manufacturing systems. However, the traditional manufacturing system development process is complicated, resulting in a longer delivery time. Many manufacturing enterprises, especially small and micro enterprises, may not have the necessary manufacturing knowledge or capabilities to meet these requirements. Therefore, it is essential to promote the construction of turnkey projects under the paradigm of Industry 4.0, parallelizing and integrating the existing manufacturing system development process based on mass manufacturing equipment to quickly provide turnkey solutions for manufacturing systems’ configuration and implementation for these enterprises. This paper aims to extract and refine the configuration and operation key views of the Industry 4.0-oriented Turnkey Project (I4TP) from Reference Architecture Model Industrie 4.0 (RAMI4.0) and use it to guide the development of key functional processes of turnkey projects to achieve rapid configuration and efficient operation management of manufacturing systems. The turnkey project platform in the Advanced Manufacturing Technology Center (AMTC) is taken as a demonstration case to provide a reference idea for the rapid configuration and intelligent operation of the turnkey manufacturing system.

Online Improvement of Condition-Baesd Maintenance Policy via Monte Carlo Tree Search.

This article proposes a method of scheduling maintenance in complex manufacturing systems in scenarios where there is frequent competition for maintenance resources. We use a condition-based maintenance policy that prescribes maintenance actions based on a machine's current health. However, when several machines are due for maintenance, a maintenance technician must choose between multiple competing jobs. While a common approach is to establish rules that dictate how maintenance jobs should be prioritized, such as the first-in, first-out rule, the goal of this work is to improve upon static policies in real time. We do this by strategically evaluating sequences of maintenance actions and playing out many "what-if" scenarios to see how the system will behave in the future. Implementation of the proposed method relies on the construction of a simulation model of the target system. This model is capable of retrieving the current state of the physical system, including the degradation state of machines, the availability of maintenance resources, and the distribution of parts throughout buffers in the system. We present several simulation experiments that demonstrate the improvement in system performance that our approach provides. Future work will aim to improve the efficiency of maintenance prioritization through online learning as well as more accurately identify manufacturing system configurations that will yield the greatest benefit of these methods.

Deep reinforcement learning for dynamic scheduling of a flexible job shop

The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production system creates massive amounts of industrial data that needs to be mined and analysed in real-time. To facilitate such real-time control, this research proposes a hierarchical and distributed architecture to solve the dynamic flexible job shop scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agents, to capture the relationship between production information and scheduling objectives, and make real-time scheduling decisions for a flexible job shop with constant job arrivals. Specialised state and action representations are proposed to handle the variable specification of the problem in dynamic scheduling. Additionally, a surrogate reward-shaping technique to improve learning efficiency and scheduling effectiveness is developed. A simulation study is carried out to validate the performance of the proposed approach under different scenarios. Numerical results show that not only does the proposed approach deliver superior performance as compared to existing scheduling strategies, its advantages persist even if the manufacturing system configuration changes.

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB’s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field.

An integrated approach to optimize the configuration of mass-customized products and reconfigurable manufacturing systems

The current global unpredictable market is characterized by increasing demand for highly customized products. To thrive in this scenario, it becomes essential to establish a closer interaction between product and manufacturing system, keeping the main focus on the client. Modular product design (MPD) is the best strategy to produce a large product variety. MPD’s configuration phase represents a key step for mass customization because it allows customers to be integrated into the value-creation process. Reconfigurable manufacturing systems (RMS) seem to be the most appropriate manufacturing system to manufacture mass-customized products due to their ability to be quickly reconfigured, adjusting their production capacity and functionality to fit new market demands. Aiming to integrate individual customer needs with product and manufacturing decisions, this paper proposes a new 0-1 nonlinear integer programming model to concurrently optimize the configuration of modular products and RMS (including machine and layout configuration levels), driven by individual customer requirements. A genetic algorithm-based approach is proposed to solve this model, and its parameters are tuned with a 2-full factorial design. An example of customizable office chairs is used to illustrate the proposition, and different scenarios of customer requirements and RMS configurations were presented. Results evidence that varying initial machines’ configurations can highly affect the process plan and the overall manufacturing costs; however, there is no evidence that changes in initial layout configurations cause significant effects. In summary, this work confirms the relevance of integrating modular product and RMS configuration decisions for minimizing costs of producing highly mass-customized products.

Concurrent optimisation of modular product and Reconfigurable Manufacturing System configuration: a customer-oriented offer for mass customisation

ABSTRACT Reconfigurable Manufacturing Systems (RMS) emerged from companies’ needs to increase their responsiveness to an uncertain market, in which customers are increasingly demanding mass-customised products. Companies focused on mass customisation mainly use the modular product design (MPD) strategy to cost-effectively provide a large product variety. Hence, coupling the MPD with the manufacturing in RMS seems to be a good strategy to effectively provide mass-customised products with lower costs. However, few papers have concurrently optimised the modular products’ and RMS’s configurations for that end. Further, very few papers have explored the RMS’s layout configuration. In order to fill these gaps, this paper proposes a Nonlinear Integer Programming model that integrates the configuration of modular products and RMS, driven by individual customer requirements, to minimise manufacturing costs of mass-customised products. An approach combining a Modified Brute-Force Algorithm (MBFA) and a genetic algorithm (GA) is proposed and compared with a CPLEX-based approach for a small-sized problem, proving its ability to find an optimal solution in lower computation time. An illustrative example of modular smartphones confirms the MBFA-GA’s ability to solve medium/large-sized problems in a reasonable amount of time while ensuring an optimal product configuration that meets customer requirements.

Robust optimization of manufacturing systems flexibility

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order to be robust to changes in the production scenario. Therefore, manufacturing companies face a relevant risk when taking strategic decisions about which system resources should be acquired. This risk can be mitigated by exploiting performance evaluation models, such as analytical models and Discrete Event Simulation, that are effectively adopted to estimate the performance of possible system configurations. However, current decision-support tools for optimizing system configurations can be only loosely coupled with performance evaluation models, hence undermining the actual optimization of the system itself, even more if production requirements may evolve in the future.This work presents an analytical methodology to support the optimization of manufacturing systems configuration and reconfiguration subject to evolving production requirements. The methodology integrates a stochastic analytical model for performance evaluation of manufacturing lines into a mixed integer programming problem, by means of performance linearization. The advantage of using the proposed methodology is shown on a line configuration problem, where buffer capacities and machine capabilities have to be jointly optimized, in order to minimize costs and satisfy the target performance.

Coexistent scheduling in the tandem flow path configuration of a flexible manufacturing system by using an advanced grey wolf optimizer

The use of material handling robots (MHRs) for efficient material handling operations in the flexible manufacturing systems (FMS) has gained wide popularity and acceptability across the automated production industries. The coexistent scheduling between jobs and MHRs improves the overall efficiency of the FMS significantly. In the present study, the coexistent scheduling between the MHRs and the jobs under production in the FMS is carried out by using an advanced grey wolf optimization (AGWO) algorithm. The proposed FMS layout is made up of the tandem flow path configurations for the movements of MHRs. The FMS constitutes six flexible manufacturing cells (FMCs) partitioned in six zones and served by six MHRs deployed in each partitioned zone for efficient material handling operations. To develop the coexistent schedule between MHRs and jobs, a combined objective function is formulated by combining the two diverging objectives and solved by using the AGWO algorithm. The combined objective function yield for coexistent production scheduling in FMS, operating with nineteen work centers (WC) and six MHRs to produce thirty-six types of jobs and sixty-six types of jobs in varying batch production quantities is also reported in the paper.

Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.

Formal modelling of release control policies as a plug-in for performance evaluation of manufacturing systems

Control policies significantly affect the performance of manufacturing systems, driving the need to assess their impact during both the design and operational phases. Performance evaluation tools can provide a relevant support, but their full exploitation is hindered by the difficulty of considering the huge variety of control decisions that are interwoven with manufacturing system configurations. Herein, a formal modelling approach is presented to jointly describe a manufacturing system and its release control policies, thus enabling the definition of performance evaluation models in terms of different policies. An application case is provided for the automatic generation of discrete event simulation models to assess the viability of the approach for assembly lines.

Extending the POLCA production control system with centralized job release

Abstract Paper aims POLCA is a production control system that forwards to gateway manufacturing cells jobs that arrive to a manufacturing system. However, Workload Control literature shows benefits from using a centralized release pre-shop pool. Thus, the paper aims at investigating the performance impact of incorporating centralized job release into POLCA, in high-variety make-to-order production. Originality This has not been investigated before. Research method Simulation was used for experimentation. Main findings In the case of the general flow shop, a manufacturing system configuration aligned with many real-world production systems, the investigation shows that adding centralized job release to the POLCA system, together with job release based on job workload, leads to a markedly improvement of job delivery performance. Implications for theory and practice The results show theoretical and practical relevance of applying central job release to POLCA system, in high-variety production, due to its markedly positive impact on delivery performance.

Bi-objective robust optimization model for configuring cellular manufacturing system with variable machine reliability and parts demand: A real case study

In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front. Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.

TD-OCBA: Optimal computing budget allocation and time dilation for simulation optimization of manufacturing systems

Discrete event simulation has been widely applied to study the behavior of stochastic manufacturing systems. This is due to the fact that manufacturing systems are usually too complex to obtain a closed-form analytical model that can accurately predict their performance. This becomes particularly critical when the optimization of these systems is of concern. In fact, Simulation optimization techniques are employed to identify the manufacturing system configuration which can maximize the expected system performance when this can only be estimated by running a simulator. In this article, we look into simulation-based optimization when a finite number of solutions are available and we have to identify the best. In particular, we propose, for the first time, the integration of Optimal Computing Budget Allocation (OCBA), which is based on independent measures from each simulation experiment, and Time Dilation (TD), which is a single-run simulation optimization algorithm. As a result, the optimization problem is solved when only one experiment of the system is performed by changing the “speed” of the simulation at each configuration in order to control the computational effort. The challenge is how to iteratively select such a speed. We solve this problem by proposing TD-OCBA, which integrates TD and OCBA while relying on standardized time series variance estimators. Numerical experiments have been conducted to study the performance of the algorithm when the response is generated from a time series. This provides the possibility to test the robustness of TD-OCBA. Comparison between TD-OCBA and the original TD method was performed by simulating a job shop system reported in the literature. Finally, an application involving semiconductors remote diagnostics is used to compare the TD-OCBA method and what is known as the equal allocation method.

Model of decision support for the configuration of manufacturing system

Abstract Manufacturing enterprises today have to face the volatility of markets, characterized by a decreasing production volume and an increasing number of product variants to meet customer expectations. Traditional dedicated manufacturing lines (DML) have been replaced by flexible manufacturing systems (FMS) and, recently, by the systems that combine features of DML and FMS, i.e., by reconfigurable manufacturing systems (RMS). Appropriate manufacturing support and optimization systems for FMS and RMS will advance the quality and effectiveness of reconfigurable manufacturing. This paper proposes an original mixed integer linear programming (MILP) model for decision support in configuration and reconfiguration of the manufacturing system. The modeled problem is a certain development of the known machine loading problem MLP. In this approach, we generate a highly parameterized model based on a set of constraints and a set of questions. Different mathematical programming (MP) - based solvers are proposed to solve this model.

A cloud-based manufacturing control system with data integration from multiple autonomous agents

The paper describes a semi-heterarchical manufacturing control solution based on a private cloud infrastructure which collects data in real-time from intelligent devices associated to shop-floor entities. The entities consist of industrial resources and mobile devices embedding the work in process on products during their manufacturing cycle. The proposed control system is developed using a common database in the cloud which handles operation synchronization and production control logic. The database component tables and the update processes are described in the article. The main functionalities of the control system are: manufacturing system configuration, control, monitoring, and optimization, and storage of historic data. An implementation framework and experimental results for the evaluation of consumed energy are reported.

Simulation Tool for Assessing the Performance of a Flexible Manufacturing System

The flexible manufacturing system (FMS) is as complex whole, raises concerns regarding the configuration and dimensioning, as well as on the conditions for implementation. The scientific researches in this paper, are directed towards the study and analysis of flexible manufacturing systems, in order to know their behaviour and performances as well, and if possible, even before their physical realization, and to establish the scientific basis for sizing, configuration and simulation patterns of flexile manufacturing systems. The ARENA simulation software can describe the complex interactions between resources and processes that take place within a FMS, a system of discrete events. In the simulation phase, evaluation of performance criteria allows the introduction of planning strategies in the use of system resources. After developing these models the verification and validation of the models, confirms their validity. The researches will be also extended in the domain of flexible manufacturing systems management and control in real and virtual environment.

Optimization of the configuration of manufacturing systems

Software for optimizing the configuration of manufacturing systems so as to reduce expenditures in all stages of the product life cycle is considered. Mathematical expressions are given for the three crossover operators used in the genetic optimization algorithm. A computational experiment indicates the most effective crossover operator.