Reconfigurable Manufacturing Systems Research Articles

Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models

Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.

Reconfigurable Manufacturing Systems and Industry 4.0: Transforming the Future of Manufacturing

Reconfigurable Manufacturing Systems and Industry 4.0: Transforming the Future of Manufacturing

Optimizing reconfigurable manufacturing system configuration selection with multi-objective grey wolf optimization

Optimizing reconfigurable manufacturing system configuration selection with multi-objective grey wolf optimization

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system

The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.

Evaluating environmental sustainability factors in implementation of reconfigurable manufacturing systems (RMS) using DEMATEL approach

Evaluating environmental sustainability factors in implementation of reconfigurable manufacturing systems (RMS) using DEMATEL approach

Optimal configuration selection in reconfigurable manufacturing system considering variable production rate

Various manufacturing companies produce different components, parts, or entire products based on market demands. These needs are met by a variety of manufacturing systems. However, in today's dynamic environment where production capacities and company capabilities are subject to change, reconfigurable manufacturing systems (RMS) have emerged as crucial resources. At the heart of RMS lies the reconfigurable machine tool (RMT), which is designed with modular components to adapt to varying requirements. Evaluating the performance of RMS requires an index. This paper employs weighted sum theory to map various characteristics of RMS and develop a cumulative reconfigurability index. To illustrate the applicability of this methodology, a case study is presented, examining both machine and system levels. There are four configurations having eight RMTs arranged in different manner. The cumulative reconfigurability index is calculated for each configuration and the optimal configuration is selected. From this research, it has been observed that the reconfigurability index of system (d) is the highest, with system (a) having a value close to that of system (d).

Introducing reconfigurable manufacturing systems to agriculture

Introducing reconfigurable manufacturing systems to agriculture

Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation

Reconfigurable manufacturing system is considered as a promising next-generation manufacturing paradigm. However, limited equipment and complex product processes add additional coupled scheduling problems, including resource allocation, batch processing and worker cooperation. Meanwhile, dynamic events bring uncertainty. Traditional scheduling methods are difficult to obtain good solutions quickly. To this end, this paper proposes a multi-agent deep reinforcement learning (DRL) based method for dynamic reconfigurable shop scheduling problem considering batch processing and worker cooperation to minimize the total tardiness cost. Specifically, a dual-agent DRL-based scheduling framework is first designed. Then, a multi-agent DRL-based training algorithm is developed, where two high-quality end-to-end action spaces are designed using rule adjustment, and an estimated tardiness cost driven reward function is proposed for order-level scheduling problem. Moreover, a multi-resource allocation heuristics is designed for the reasonable assignment of equipment and workers, and a batch processing rule is designed to determine the action of manufacturing cell based on workshop state. Finally, a strategy is proposed for handling new order arrivals, equipment breakdown and job reworks. Experimental results on 140 instances show that the proposed method is superior to scheduling rules, genetic programming, and two popular DRL-based methods, and can effectively deal with various disturbance events. Furthermore, a real-world assembly and debugging workshop case is studied to show that the proposed method is applicable to solve the complex reconfigurable shop scheduling problems.

Comparing sequential and integrated models in Reconfigurable Manufacturing Systems optimization

Comparing sequential and integrated models in Reconfigurable Manufacturing Systems optimization

A configuration optimization approach for reconfigurable manufacturing system based on column-generation combined with graph neural network

Reconfigurable manufacturing systems (RMS) offer the potential to improve systemic responsiveness and flexibility to better cope with dynamic environments. However, the inherent modularity of RMS and dynamic environments pose challenges in optimising system configurations. To address this issue, a two-stage stochastic programming model is established to minimise configuration cost, reconfiguration cost, expected inventory and back-order cost. To efficiently handle a large number of variables, a set-covering model is obtained by using Danzig-Wolfe (DW) decomposition along with its corresponding pricing subproblem. This paper proposes a solution algorithm based on the column generation framework, which can quickly obtain a good feasible solution. To further improve the algorithm performance for larger instances, a column selection method is introduced to identify additional columns that have the potential to reduce the objective function value of the integer solution during the column generation iterations. These columns are then added to the set-covering model. The process of column selection is accelerated by employing the Graph Neural Network (GNN) algorithm. Furthermore, GNN trained on data from small instances can be directly applied to larger instances as well. The effectiveness of the proposed model and algorithm is verified by numerical experiments.

Optimisation of process plan generation for reconfigurable manufacturing systems: efficient heuristics and lower bounds

ABSTRACT We address the process plan generation problem in a reconfigurable manufacturing environment. The objective is to minimise the total production time of a given part which includes the processing times of operations, as well as the changeover times for machines, configurations, and tools. A new efficient 0–1 mathematical programming formulation is proposed, together with a heuristic and two lower bounds. The formulation is based on one main decision variable (1MDV). It outperforms a well known two-main decision variable formulation (2MDV) from the literature. Using a commercial solver, the 1MDV formulation is on average almost five times faster than the 2MDV formulation across all instances where optimality was achieved. The proposed heuristic is a decomposition mathematical programming-based heuristic. For small benchmark instances solved to optimality using 1MDV formulation, the heuristic obtains solutions at less than 1% distance on average from optimum in few seconds. The best of the two proposed lower bounds, LB2, is obtained in less than 0.56 seconds, across all tested instances, while the 1MDV model needs about 30 seconds on average to reach the same result. These lower bounds are used to measure the performance of the heuristics on large size instances

Optimizing modular equipment in the design of multi-product reconfigurable production lines

This work addresses the problem of the design and balancing of a modular multi-product line in a reconfigurable environment. The study emphasizes the importance of modularity in the reconfigurable manufacturing systems (RMS), which allows fast, efficient, and cost-effective adaptation to changes by rearranging, moving, or removing modules. The line consists of a fixed number of modular machines arranged linearly and connected by a material handling system. Each machine has a limited number of slots for modules. The line can handle several product types per batch, and each batch requires a specific line configuration to be produced. The objective consists in identifying the minimum number of modules needed and their arrangement in different configurations to produce each product type. To solve this problem, a compact integer linear programming (ILP) formulation is initially investigated. Then an ILP reformulation, based on a decomposition approach, as well as a heuristic that uses this ILP reformulation are developed. The results demonstrate the effectiveness of the proposed methods.

Variability propagation in manufacturing systems: the impact of the processing time distribution

ABSTRACT In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration.

A systematic methodology for changeable and reconfigurable manufacturing systems development

Pursuing manufacturing competitiveness in the dynamic industrial landscape necessitates implementing changeable and reconfigurable manufacturing systems (RMS) capable of rapid adaptation to varying functionalities and capacities. However, current manufacturing system development methods often overlook product-driven changes during the system's life cycle, hindering companies from effectively responding to shifting demands and technological advancements. Consequently, this research paper proposes a systematic methodology for designing and developing changeable and reconfigurable manufacturing systems to address this gap. The proposed methodology is derived from a synthesis of design theory, reconfigurability theory, and practical insights to guide the development process from conception to implementation. The four-step development method adopts a system life cycle-wide perspective, encompassing (i) identification and clarification of the need for reconfigurability, (ii) formulation of reconfigurable concepts, (iii) detailed design of the reconfigurable system, and (iv) successful implementation and utilization of reconfigurability. Crucially, the development method blends existing RMS development tools and novel tools co-created with industry partners, ensuring its pragmatic and holistic applicability. Each step incorporates specific activities and supporting tools, rendering the methodology flexible and adaptable to diverse manufacturing environments. The proposed methodology was validated through case studies in seven diverse manufacturing companies. The primary contributions of this research lie in integrating new and existing development tools into a comprehensive and practical development method, facilitating a system life cycle-wide approach to RMS design, and promoting industry-specific adaptability. The validation across multiple manufacturing companies ensures the effectiveness and broad applicability of the proposed methodology. Consequently, this paper is a valuable resource for manufacturing companies aiming to enhance competitiveness by adopting changeable and reconfigurable manufacturing systems.

Performance Evaluation of Reconfiguration Policy in Reconfigurable Manufacturing Systems including Multi-Spindle Machines: An Assessment by Simulation

Reconfigurable manufacturing systems (RMSs) are extensively studied and employed to address demand uncertainties. RMS machines are designed to be modular and adaptable to changing requirements. A recent innovation is the introduction of multi-spindle reconfigurable machines (MRMTs). This study evaluates the impact of MRMTs’ introduction into an RMS, considering factors such as the number of MRMT machines and reconfiguration policies. A simulation model incorporating failures, process time variability, and part inter-arrival supports the analysis. The numerical results aid decision makers in determining the optimal RMS configuration with MRMTs. The simulation outcomes indicate that a balanced number of multi-spindle machines can significantly enhance performance compared with an unbalanced distribution.

Tactical Order Allocation in International Manufacturing Networks

Abstract Manufacturing networks are subject to various operational disruptions, requiring rapid long-term planning adjustments. Reconfigurable manufacturing systems allow the network plants’ dynamic adaptation, enabling flexible manufacturing volume shifts. Therefore, the present paper introduces a novel approach to tactically optimize order allocations in manufacturing networks. With the help of an extended lot sizing and scheduling problem, orders are assigned in a multi-plant, multi-product manufacturing network.

Ensuring quick changeability of the working bodies of technological machines in the construction and maintenance of the landscape, road-building, and agricultural industries

The relevance of the subject matter is conditioned by the high-efficiency use of reconfigurable manufacturing systems during construction work and the associated need to develop and implement technological machines with replaceable working bodies. The purpose of this study is to analyze the invariance of the equipment used by types of work in landscape and landscape construction. The study used theoretical methods of analysis and synthesis of information on the principles of the practical application of a universal machine with a wide range of mounted technological working bodies. The results obtained indicate the high relevance of the issue of the rapid changeability of mounted technological working bodies. Its effective solution lies in the creation of reconfigurable production machines based on a universal wheelbase with replaceable working bodies of variable layout by type of work, for which a new design of an inter-module basing and fastening device is proposed. The possibility of its practical application in the BobCat loader system is considered. It is established that, in general, the use of the BobTach mounting frame during various technological operations ensures an increase in the efficiency of this technological equipment. In addition, it significantly increases the performance of the entire model range of equipment and testifies to the versatility of the specified mounting frame. The practical significance of the results obtained is conditioned by the possibility of their application in the development and implementation of modern technological machines with rapidly replaceable working bodies in the agricultural sector and road construction.

Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin

AbstractTo meet the requirement of product variety and short production cycle, reconfigurable manufacturing system is considered as an effective solution in addressing current challenges, such as increasing customisation, high flexibility and dynamic market demand. Dynamic factory layout design and optimisation are the crucial factors in response to rapid change in the mechanical structure, software and hardware integration, as well as production capability and functionality adjustment. Nevertheless, in the current research, the layout design for reconfigurable manufacturing systems is usually simplified with autonomous devices being regarded as 2D shapes. Issues such as overlapping and transportation distance are also addressed in an approximate form. In this paper, we present a novel multi-agent cooperative swarm learning framework for dynamic layout optimisation of reconfigurable robotic assembly cells. Based on its digital twin established in the proposed learning environment (constructed in Visual Components and controlled by TWINCAT), the optimisation framework uses 3D digital representation of the facility models with minimal approximation. Moreover, instead of using a traditional centralised learning manner, multi-agent system could provide an alternative way to address the layout issues combined with the proposed decentralised multi-agent cooperative swarm learning. In order to verify the application feasibility of the learning framework, two aerospace manufacturing use cases were implemented. In the first use case, the layout compactness is reduced by 3.8 times compared with the initial layout setting, the simulated production time is reduced by 2.3 times, and the rearrangement cost decreased by 33.4$$\%$$ % . In addition, all manufacturing activity within the cell can be achieved with a feasible robot path, meaning without any joint limits, reachability or singularity issue at each key assembly point. In the second use case, we demonstrated that with the proposed dynamic layout optimisation framework, it is possible to flexibly adjust learning objectives by selecting various weight parameters among layout compactness, rearrangement cost and production time.

Operational strategy of reconfigurable membrane process for bio-based amino acid production

Reconfigurable manufacturing systems can rapidly and economically transform production facilities, allowing for the simultaneous realization of diverse customized product manufacturing and high productivity. In this study, the concept of reconfigurable manufacturing systems was introduced to the counter-current diafiltration membrane process for bio-based amino acid production. A superstructure model adaptable to any counter-current diafiltration cascade configuration was developed. This superstructure model optimizes the objective cost function, minimizing fluctuation costs associated with yield and utility. Case studies such as the production of L-lysine, L-arginine, and L-tryptophan were considered. Sensitivity analysis indicated that when membrane capacity becomes limited due to increased overall feed flow rates and decreased permeate flux, the role of the counter-current diafiltration step grows in importance. Circumstances caused by diverse product manufacturing, natural disturbance in bioprocesses, and shifts in productivity and cost priorities based on market dynamics indicate the promising economic potential of reconfigurable membrane systems. Since scalability, diagnosability, customization, modularity, and integrability are high, the reconfigurable membrane system has proven its capability to flexibly adapt to continuously changing conditions. All superstructure modeling and simulations in this study were executed using in-house graphical user interface software.