Reconfigurable Manufacturing Systems Research Articles

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.

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.

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.

Intelligent and self- reconfigurable manufacturing system

Customer demand and profit potential have pushed companies to offer diverse customization options in their products and move towards mass customization. To move towards mass customization reconfigurable manufacturing systems (RMS) have been prosed as a solution. However, it comes with challenges, including labor, costs, time, and complexity. Introducing intelligence, smartness and state-of-the-art industry 4.0 technologies into reconfigurable manufacturing systems (RMS) can be a solution to these challenges. Combining these concepts can be used to develop an intelligent RMS that can be automatically reconfigured without any human intervention. In this paper, a novel architecture has been proposed that explains all the details from the idealization phase to design and physical implementation. The architecture covers individual elements of the RMS, introduces the technologies that enable intelligence and follows through the steps that add up to the complete functionality of the system. In addition, we propose an intelligent process that automates the reconfiguration phase of our proposal. Based on this architecture and intelligent recon-figuration, a physical demonstration is also presented that shows how the system can be implemented. The constructed system is demonstrated as an intelligent RMS capable of mass customization without human effort.

Towards a Modular Approach for Workforce Qualification in Reconfigurable Manufacturing Systems

The perpetual changes affecting manufacturing systems caused by highly fluctuating demand and customer requirements make quality management of manufacturing systems more complex. In this context, the implementation of Reconfigurable Manufacturing Systems (RMS) offers decision-makers an efficient solution to cope with these changes. However, these systems require a large number of modifications and changes handled by workforces who must have an adequate level of qualification and skills. The aim of this paper is to propose a workforce qualification framework in RMS to ensure the skills needed for performing the activities. First, new framework is proposed to define the main phases of workforce qualification in a system requiring several changes. Second, new approach is proposed for workforce skills management and the building of training modules linked to the proposed qualification phases, using a modular design. This work provides decision-makers with a solution for the qualification and management of workforce skills in RMS.

Multi-Objective Workforce and Process Planning For Socio-Economic Sustainable RMS: Lp-metric vs Epsilon Constraint

As a new manufacturing paradigm, reconfigurable manufacturing system (RMS) has shown promising results when dealing with market changes. This study explores the issue of integrating workforce planning and process planning within RMS. The idea is to consider socio-economic sustainable manufacturing by investigating new KPIs from the social aspect. The choice of workforce flexible work hours and the accident risk are concurrently viewed as social aspects. This challenge has been approached by using a new mixed integer linear model. Furthermore, the model considers other objectives, including operational cost and total completion time. The ϵconstraint and Lp-metric are used to solve the multi-objective model for five small and medium-sized instances. The findings demonstrate a 60% variation in reconfiguration time, and processing time contributes to 5% and 7.8% changes in makespan and 25% and 56% in total cost. Finally, some in-depth analysis is performed to illustrate and verify the performance of the suggested solution approach.

Optimization of Cellular Reconfigurable Manufacturing System under Constraints of Machinery Layout

Optimization of Cellular Reconfigurable Manufacturing System under Constraints of Machinery Layout

Optimal Management of Reconfigurable Manufacturing Systems

The market demands more and more varied and personalized products and services. The work aims to improve the conceptual control of reconfigurable manufacturing systems, available to companies that work on the basis of orders, generically called "make-to-order" MTO, companies that require the most efficient reconfigurable manufacturing systems. Also, increasing the economic performance of these reconfigurable manufacturing systems, already in operation, is a goal of this work.

Line balancing and task scheduling to minimise power peak of reconfigurable manufacturing systems

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints.

An integrated differential evolution algorithm for reconfigurable manufacturing systems

Product platforms consider an effective strategy to empower mass customization (MC) in a reconfigurable manufacturing system (RMS) to have the capacity to reconfigure the hardware and manage resources at all organisational and functional levels. This enables quick modification of production functions in reaction to unforeseen changes in the market or regulatory requirements. Although several platform formation and assembly line techniques have emerged to satisfy the increased demand for MC and enable the manufacture of products with high variety, they also increase the system’s complexity and make it less cost-efficient as variety increases. This study aims to minimize the complexity of a production system by reducing the number of separate components in different production processes. An efficient approach that integrates the Median-Joining Phylogenetic Networks (MJPN) classification tool and differential evolution (DE) in one unified framework is developed to do that. The proposed framework uses the concept of the package to group separate components that are often employed jointly to produce particular products and allows the use of the ‘all-unit discount policy’, which applies discounts to the purchasing costs of a specific quantity of materials/components. Finally, two case studies are applied to validate the proposed MJPN-DE in different dimensions. The experimental results show that applying the proposed package model saves 57.5% of the purchasing costs of components and 41.35% average setup costs compared to MJPN- and non-platform-based models and increases the postponement effectiveness of the obtained layout by 41.51% and 74.11% compared with two other layout strategies. In algorithmic comparisons, the results of comparing MJPN-DE with standard DE and genetic algorithm (GA) show an enhanced performance of MJPN-DE where the total production cost is reduced in the small case study by 63.34% and 38.52%, and in the large case study by 16.90% and 11.51%, respectively.

Evaluating Industry 4.0 Manufacturing Configurations: An Entropy-Based Grey Relational Analysis Approach

Worldwide manufacturing and service sectors are choosing to transform the existing manufacturing sector, particularly reconfigurable manufacturing systems using the technologies of the next generation Industry 4.0. In order to satisfy the demands of the fourth industrial revolution, model evaluation and assessing various candidate configurations in reconfigurable manufacturing systems was developed. The proposed model considers evolving consumer demands and evaluates manufacturing configurations using a gray relational approach. For the case at hand, it is evident that considering all possible dynamic market scenarios 1 to 6, the current manufacturing configuration, i.e., alternative 1, has 89% utilization, total 475 h of earliness and 185 h of lateness in the order demand delivery to the market, and a total of 248 throughput hours and around 1143 bottleneck hours. The main challenge is to make a perfect match between the market demands, variations in product geometry, manufacturing processes and several reconfiguration strategies/alternatives. Furthermore, it is evident that alternative 1 should be reconfigured and that alternative 3 is the best choice. Alternative 3 exhibits 86% system utilization, a total of 926 h of earliness and 521 h of lateness in the order demand delivery to the market, and a total of 127 throughput hours and around 853 bottleneck hours. A simulation framework is used to demonstrate the efficacy of each possible reconfigurable production setup. The sensitivity analysis is also carried out by adjusting the weights through principal component analysis and validating the acquired ranking order. Thus, if the decision makers want to provide a preference to all criteria, the order of the choices of configurations is found to be alternative 3, alternative 1, alternative 4, alternative 2 and alternative 5.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

Evaluation of the reconfigurability of manufacturing systems based on fuzzy logic taking into account the links between the characteristics of the RMS

Evaluation of the reconfigurability of manufacturing systems based on fuzzy logic taking into account the links between the characteristics of the RMS

Semantic models and knowledge graphs as manufacturing system reconfiguration enablers

Reconfigurable Manufacturing System (RMS) provides a cost-effective approach for manufacturers to adapt to fluctuating market demands by reconfiguring assets through automated analysis of asset utilization and resource allocation. Achieving this automation necessitates a clear understanding, formalization, and documentation of asset capabilities and capacity utilization. This paper introduces a unified model employing semantic modeling to delineate the manufacturing sector’s capabilities, capacity, and reconfiguration potential. The model illustrates the integration of these three components to facilitate efficient system reconfiguration. Additionally, semantic modeling allows for the capture of historical experiences, thus enhancing long-term system reconfiguration through a knowledge graph. Two use cases are presented: capability matching and reconfiguration solution recommendation based on the proposed model. A thorough explication of the methodology and outcomes is provided, underscoring the advantages of this approach in terms of heightened efficiency, diminished costs, and augmented productivity.

An integrated lot-sizing and scheduling problem in a reconfigurable manufacturing system under workforce constraints

ABSTRACT Nowadays, achieving higher levels of flexibility in manufacturing systems is necessary to maintain and enhance competitiveness. Accordingly, a new generation of production machine, namely Reconfigurable Machine Tool (RMT), has recently been introduced that can be effectively adapted to changes. Nevertheless, such systems are more worker-reliant, and neglecting workforce aspects results in suboptimal or even infeasible production schedules. In this regard, this study investigates an integrated lot-sizing and scheduling problem benefiting from RMTs under workforce constraints. First, a novel Mixed-Integer Linear Programming (MILP) model is provided to formulate the problem. Afterward, to confront the high complexity of the problem, an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised. A combination of MILP and Constraint Programming (CP) is employed in the DH to model the relevant master and sub-problems, respectively. Finally, the performance of the DH compared to the MILP model and an extended lower bound is evaluated. Moreover, the advantages of utilizing RMTs in the system are explored based on four defined key performance indicators.