Configuration Of Reconfigurable Manufacturing System Research Articles

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.

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.

Deadlock Control and Fault Detection and Treatment in Reconfigurable Manufacturing Systems Using Colored Resource-Oriented Petri Nets Based on Neural Network

A reconfigurable manufacturing system (RMS) means that it can be reconfigured and become more complex during its operation. In RMSs, deadlocks may occur because of sharing of reliable or unreliable resources. Various deadlock control techniques are proposed for RMSs with reliable and unreliable resources. However, when the system is large-sized, the complexity of these techniques will increase. To overcome this problem, this paper develops a four-step deadlock control policy for the detection and treatment of faults in an RMS. In the first step, a colored resource-oriented timed Petri net (CROTPN) is designed for rapid and effective reconfiguration of the RMS without considering resource failures. In the second step, “sufficient and necessary conditions” for the liveness of a CROTPN are introduced to guarantee that the model is live. The third step considers the problems of failures of all resources in the CROTPN model and guarantees that the model is reliable by designing a common recovery subnet and adding it to the obtained CROTPN model at the second step. The fourth step designs a new hybrid method that combines the CROTPN with neural networks for fault detection and treatment. A simulation is performed using the GPenSIM tool to evaluate the proposed policy under the RMS configuration changes and the results are compared with the existing approaches in the literature. It is shown that the proposed approach can handle any complex RMS configurations, solve the deadlock problem in an RMS, and detect and treat failures. Furthermore, is simpler in its structure.

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.

Design of reconfigurable flow lines using MOPSO and maximum deviation theory

Reconfigurable manufacturing system (RMS) is a state-of-the-art technology, which offers the exact capacity and functionality needed, through rapid and cost-effective reconfigurations in its modular structure. The reconfigurable machine tools (RMTs) can perform a variety of operations in its existing configuration and can further be reconfigured to change the operational capabilities. Thus, the selection of a machine configuration plays a vital role not only in the cost-effectiveness but in the future reconfiguration effort requirements. In the present study, authors have attempted a multiple-objective optimization of RMS flow line configuration, taking the cost, machine utilization, operational capability, and configuration convertibility as the conflicting objectives. The cost and machine utilization represent the economic justification while the operational capability and the configuration convertibility portray the ability of rapid and cost-effective reconfiguration ability. The emphasis of this research is to design the economic RMS configuration, which can rapidly and cost-effectively respond to the volatile market conditions. Multiple-objective particle swarm optimization (MOPSO) has been implemented to handle the discrete and discontinuous search space. The large number of Pareto frontiers is further ranked to avoid subjectiveness and imprecision in the decision making, based on the composite scores obtained through maximum deviation theory. A numerical illustration is presented to illustrate the developed methodology of reconfigurable flow line configuration design.

Applying Swarm Intelligence to Design the Reconfigurable Flow Lines

Abstract Reconfigurable Manufacturing System (RMS) justifies the need of hour by combining the high throughput of dedicated manufacturing system with the flexibility of flexible manufacturing systems. At the heart of RMS lies the Reconfigurable Machine Tools which are capable of performing multiple operations in its existing configurations and can further be reconfigured into more configurations which makes the configuration selection an arduous task. In the present research work the design of single part reconfigurable flow line has been attempted considering multiple objectives i.e. cost and machine utilization. A methodology is proposed for multiple objective optimization of RMS configuration based on machine utilization and cost by applying Multiple Objective Particle Swarm Optimization (MOPSO). A case study has been taken to illustrate the developed approach of flow line optimization applying MOPSO. (Received in February 2012, accepted in August 2012. This paper was with the authors 1 month for 1 revision.)

A model and optimisation approach for reconfigurable manufacturing system configuration design

During the expected lifetime, evolving economical and optimum reconfigurable manufacturing system (RMS) configuration in a desired period to give the exact functionality and the exact capacity needed, when it is needed, is an important optimisation problem. This paper presents RMS configuration design methodology in a desired period with optimisation of the present worth of total cost of RMS including capital cost of machine investment, reconfiguration costs, operating costs, maintenance costs and salvage value over time. The configuration decision considers flow line features such as multi-product, number of stages, and functionality blocks in each stage, etc., with various constraints satisfaction. Phase 1 aims at RMS evolution scenario and the mathematical modelling. Phase 2 evolves RMS configuration alternatives. Phase 3 aims at RMS configuration alternative optimisation using mathematical model and artificial immune system. A case study demonstrates the methodology, the effectiveness of the methodology and also some superiority of the proposed methodology compared to existing approaches.

Research and optimization of reconfigurable manufacturing system configuration based on system reliability

PurposeThe purpose of this paper is to research the impact of hybrid series‐parallel and parallel‐series system configurations on system performances based on system reliability and to develop a configuration model to meet the requirement of reconfigurable manufacturing system (RMS).Design/methodology/approachBased on the criterion of system reliability, a RMS configuration model is presented – the hybrid parallel‐series model with waiting system characteristics. The configuration model is evaluated from reliability, productivity, and cost by combining system engineering theory, Boolean algebra methodology with statistical analysis theory. The model reliability has been used to ameliorate by adopting the integrated algorithm based on Shrama and Misra optimization algorithm.FindingsThe need for application of this method and model – some constraints must be limited, the hybrid parallel‐series configuration is superior and the integrated algorithm is effective to RMS system configuration.Research limitations/implicationsCost constraints, equipment weight constraints, and function independency of equipment are main limitations.Practical implicationsThe model and method have been used to ameliorate the reconfigurable automobile parts product line in SH automobile motor company of Shanghai. The operation result illustrates the validity of this configuration model and algorithm.Originality/valueThe new RMSs configuration model has been proposed. The new algorithm is proposed to ameliorate and optimize a reconfigurable product line with the integrated algorithm based on Shrama and Misra algorithm. The actual running effect is significant.

Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA

To facilitate the configuration selection of reconfigurable manufacturing systems (RMS) at the beginning of every demand period, it needs to generate K (predefined number) best configurations as candidates. This paper presents a GA-based approach for optimising multi-part flow-line (MPFL) configurations of RMS for a part family. The parameters of the MPFL configuration comprise the number of workstations, the number of paralleling machines and machine type as well as assigned operation setups (OSs) for each workstation. Input requirements include an operation precedence graph for each part, relationships between operations and OSs as well as machine options for each OS. The objective is to minimise the capital cost of MPFL configurations. A 0-1 nonlinear programming model is developed to handle sharing machine utilisation over consecutive OSs for each part which is ignored in the existing approach. Then a novel GA-based approach is proposed to identify K economical solutions within a refined solution space comprising the optimal configurations associated with all feasible OS assignments. A case study shows that the best solution found by GA is better than the optimum obtained by the existing approach. The solution comparisons between the proposed GA and a particle swarm optimisation algorithm further illustrate the effectiveness and efficiency of the proposed GA approach.

Hierarchical Petri net modelling of reconfigurable manufacturing systems with improved net rewriting systems

This paper presents an improved net rewriting system (INRS)-based method for constructing a class of modular and reconfigurable Petri net (PN) models intended for supervisory control of reconfigurable manufacturing systems (RMS). First of all, the INRS is introduced that can implement dynamical adjustments to the structure of a PN model and maintain its important behavioural properties. Subsequently, the formalisms of graph and set are incorporated to provide a formal representation of a RMS configuration. According to the hierarchy of RMS configuration, hierarchical design of PN models with an INRS approach is proposed. Using the proposed method, a modular PN model can be obtained. Such a PN model is not only reconfigurable but also valid, i.e. live, bounded (or safe), and reversible. A reconfigurable manufacturing cell serves as an example to illustrate the main principles and procedures of PN model design, applying the presented method.

Automatic Reconfiguration of Petri Net Controllers for Reconfigurable Manufacturing Systems With an Improved Net Rewriting System-Based Approach

The advent of reconfigurable manufacturing systems (RMSs) has given rise to a challenging problem, i.e., how to reconfigure rapidly and validly a RMS supervisory controller in response to frequent changes in the manufacturing system configuration driven by fluctuating market. This paper presents an improved net rewriting system (INRS)-based method for automatic reconfiguration of Petri net (PN) supervisory controllers for RMS. We begin with presenting the INRS which overcomes the limitations of the net rewriting system and can dynamically change the structure of a PN without damaging its important behavioral properties. Based on INRS, a method for design reconfigurable PN controllers of RMS is introduced. Subsequently, we presented an INRS-based method for rapidly automatic reconfiguration of this class of PN controllers. In the reconfiguration method, changes in a RMS configuration can be formalized and act on an existing controller to make it reconfigure rapidly into a new one. Noticeably, no matter the design or reconfiguration, the expected behavioral properties of the resultant PN controllers are guaranteed. Thus, efforts for verification of the results can be avoided naturally. We also illustrate the reconfiguration of a PN controller for a reconfigurable manufacturing cell.

Improved net rewriting system-based approach to model reconfiguration of reconfigurable manufacturing systems

This work aims at presenting a method for rapidly reconfiguring Petri net (PN) models of reconfigurable manufacturing systems (RMS). We start with introducing the model of improved net rewriting systems (INRS), which supports dynamic structural changes in a PN model without losing its important behavioral properties, i.e., liveness, boundedness (or safeness), and reversibility. Based on INRS, the method for rapid reconfiguration of Petri net models for RMS is proposed and an implementation algorithm is designed as well. In our method, changes in a RMS configuration adjusted with production demands can be rapidly formalized into graph rewriting rules of an INRS called model reconfigurator whose underlying Petri net is the existing Petri net model. Subsequently, by applying these rewriting rules, the existing Petri net model can be reconfigured rapidly into a new one for the RMS with a new configuration. Validity of the resulting Petri net model can be guaranteed naturally throughout the whole reconfiguration process and thereby there is no need for verification. A case study is provided that demonstrates the reconfiguration process of a Petri net model for a reconfigurable manufacturing cell. The given case study shows that the proposed method is capable for reconfiguring PN models of RMS in a rapid and successive manner, without requiring verification.

Optimal configuration selection for Reconfigurable Manufacturing Systems

The selection of Reconfigurable Manufacturing Systems (RMS) configurations that include arrangement of machines, equipment selection, and assignment of operations, has a significant impact on their performance. This paper reviews the relevant literature and highlights the gaps that exist in this area of research. A novel “RMS Configuration Selection Approach” is introduced. It consists of two phases; the first deals with the selection of the near-optimal alternative configurations for each possible demand scenario over the considered configuration periods. It uses a constraint satisfaction procedure and powerful meta-heuristics, real-coded Genetic Algorithms (GAs) and Tabu Search (TS), for the continuous optimization of capital cost and system availability. The second phase utilizes integer-coded GAs and TS to determine the alternatives, from those produced in the first phase, that would optimize the degree of transition smoothness over the planning horizon. It uses a stochastic model of the level of reconfiguration smoothness (RS) across all the configuration periods in the planning horizon according to the anticipated demand scenarios. This model is based on a RS metric and a reconfiguration planning procedure that guide the development of execution plans for reconfiguration. The developed approach is demonstrated and validated using a case study. It was shown that it is possible to provide the manufacturing capacity and functionality needed when needed while minimizing the reconfiguration effort. The proposed approach can provide decision support for management in selecting RMS configurations at the beginning of each configuration period.