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Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturi...
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Reconfigurable Machine Tool (RMT) is the critical issue to realize both the flexibility and productivity of manufacturing systems and satisfy the mass-customization production. This machine (RMT) belongs to a great program in Reconfigurable Manufacturing Systems (RMS). The purpose of this paper is to present the design process of architectural stru...
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... In their groundbreaking study, the authors of [11] introduced a comprehensive multiobjective approach aimed at optimizing the design of reconfigurable manufacturing systems (RMSs). Their objectives encompass maximizing system modularity while concurrently minimizing the completion time and cost. ...
... The proposed methodology leverages a modularity-based technique employing adapted multi-objective simulated annealing (AMOSA), complemented by a sophisticated decision-making tool based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through an illustrative example and rigorous numerical analyses, the authors of [11] illustrated the practical applicability and effectiveness of their approach, underscoring its potential to revolutionize RMS design and performance optimization. ...
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.
... On the machine level, [17] developed a multi-objective approach for the configuration selection of a manufacturing system and its RMT for multi-product application. The work has been completed by an approach of configuration selection of machine modules considering modularity [19]. Bensmaine et al. [20] also propose an approach for machine selection based on tool configurations considering time and cost, while [22] consider maximizing the throughput and minimizing energy consumption. ...
Increasing product variety and fluctuating demand have led to the need of assembly systems that can adapt to multiple different products as the return of investment for dedicated assembly lines is more and more difficult to achieve. In response to this challenge, the paradigm of reconfigurable assembly systems has emerged. However, configuring and optimizing these systems still pose challenges in the industry. This paper proposes a new simple optimization approach for the configuration analysis and optimization of a reconfigurable multi-product assembly system in the automotive industry, using configuration selection, task allocation, and sequencing. Its effectiveness is validated throughout three real industrial study cases in the automotive supplier industry.
... Their potential to produce highly personalized and complicated items in any quantity and at any cost while taking advantage of mass manufacturing [1]. This paradigm provides excellent performance and reactivity to changes, particularly if system response to uncertainties or unknown cases and productivity are regarded as critical [2]. In order to ensure a reliable system design, it is crucial to integrate primary RMS features such as customization, convertibility, modularity, diagnosability, scalability, and integrability. ...
... The total cost during the planning horizon is represented by the objective function in Eq (1), including the RMTs exploitation cost, the reconfiguration cost, the inventory cost, and the purchasing raw materials cost. The objective function for energy consumption is denoted by the equation Eq (2). It considers the energy consumption of exploitation of RMTs as well as the energy required to change configurations. ...
... But for the other two segments, MRMT is selected for only one operation each; in those two cases, half of the configurational capability of the machine is utilized, which is not an efficient choice because of unused MRMT capability. -Case 2: Availability of only SRMT in RMS While in the works like [1,6,8,9], only SRMT are considered as the only RMS capability for machining the part. SRMT can perform only one operation per configuration without any configuration change. ...
A reconfigurable manufacturing system (RMS) is one of the next generation production systems widely used to meet uncertain market demands in the context of Industry 4.0. The design of the RMS aims to achieve sufficient responsiveness so that it can be quickly adopted to the changes required for a niche market of a customized product family. Most components of the RMS are designed to be modular. Reconfigurable machines are one of the main modular components of the RMS. In the design of the RMS, the problem of machine selection is of primary interest, as the modular machines, with their respective tools and configurations, are selected to perform a given part from the part family. Due to this trilogy of machine, tool, and configuration selection, only one type of machine is considered. To remedy this shortcoming, this work introduces a new concept of modular machine configuration capability, which leads to the selection of two types of machines, namely, the single-spindle modular reconfigurable machines (SRMT) and the multi-spindle reconfigurable machines (MRMT). This paper addresses the problem of machine selection and RMS design. Firstly, a bi-objective mathematical model is developed for the generation of the process plan and the selection of reconfigurable machines. The results obtained, together with an initial layout, are then used to generate the RMS design. Secondly, a new objective function is introduced to address the problem of under-utilization of reconfigurable multi-spindle machines. A NSGA-III (non-dominating sorting genetic algorithm III)-based approach is proposed to solve the proposed models. To help the decision maker, the pseudo-weight technique is used to determine the best process plan and the best machines to include in the new designed RMS.
... Wang et al. (2017) proposed a PROTHMEE-based configuration evaluation method considering key characteristics of RMS, which emphasise the significance of key characteristics through mathematical modelling. Similarly, Benderbal, Dahane, and Benyoucef (2018) proposed a configuration assessment method considering modularity. Puik et al. (2017) assessed the configuration design scheme of RMS from the perspectives of resources and lead time. ...
In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method.
... In this context, Newman and Girvan [25,26] proposed the Optimal Modularity method (Q), which is de ned as the fraction of connections within a community in the actual network minus expected fraction of connections in a random network. Haddou Benderbal et al. [27] explored the optimization of recon gurable manufacturing systems with the aims to maximize the system modularity and throughput rate and to minimize system completion time and system cost. Also, it is worth to mention other related research such as Singh et al [28] Fotsoh et al. [29], Tu et al. [30] that deals with modularity in context of throughput rate. ...
As mass customization becomes more pervasive in many sectors, researcher needs to update traditional approaches to optimization of critical performance and design parameters in order to help companies in their effort to implement this strategy. In general, implementation of mass customization from manufacturing perspective is frequently focused on shortening cycle times, reducing production cost, and increasing throughput rate of parts. In this paper, process structure modularity impact on manufacturing lead times and throughput rates is explored. An important precondition to explore these relationships is awareness that process modularity is conceptualized and quantified in an appropriate way. For this purpose, two independent modularity measures were employed to provide more reliable assessment of this system property. The relationships were investigated on the basis of simulation experiments using deterministic models of alternative process structures. The results from the experiments showed that there are strong correlations between process modularity and manufacturing lead time, as well as between process modularity and throughput rate.
... The following pie charts visualize by how many approaches and to what extend the subrequirements R1.2.1, R1.2.2 and R1.2.3 are taken into account. Overall, many approaches focus on conducting a (mere) production planning and scheduling from scratch rather than on concerning an existing production system and the reconfiguration of it such as [49,[59][60][61]. The respective following subsections will provide further detailed insights. ...
Driven by shorter innovation and product life cycles as well as economic volatility, the demand for reconfiguration of production systems is increasing. Thus, a systematic literature review on reconfiguration management in manufacturing is conducted within this work in order to determine by which degree this is addressed by the literature. To approach this, a definition of reconfiguration management is provided and key aspects of reconfigurable manufacturing systems as well as shortcomings of today’s manufacturing systems reconfiguration are depicted. These provide the basis to derive the requirements for answering the formulated research question. Consequently, the methodical procedure of the literature review is outlined, which is based on the assessment of the derived requirements. Finally, the obtained results are provided and noteworthy insights are given.
... Other research works, such as Stone et al. (2000), concentrated on the trade-off between product modularity and production cost by incorporating three heuristic methods to increase product modularity and innovation. The design of reconfigurable manufacturing systems (RMS) has been optimized using the multi-objective simulated annealing (SA) method by considering three objectives: 1) maximization of device modularity, 2) minimization of completion time, and 3) minimization of production cost (Benderbal et al. 2018). In Ripperda and Krause (2017), several methods for designing variety and modular product family structures, as well as a new method for quantifying their cost effects to aid model selection for decisionmaking during modular product family design, were presented. ...
In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE’s performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively.
... Although an RMS may be able to meet the dynamic requirements in the market, designing and configuring the RMS is no trivial task. Simulation techniques, particularly discrete event simulation, have proven to be a powerful tool for the manufacturing industry to assess the capabilities of their production systems [5,6]. Often, several conflicting objectives are used to simultaneously measure the quality of the system. ...
Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.
... Authors in [8]- [10] proposed an adapted non-dominated sorting genetic algorithm (NSGA-II) to solve the problem of machines selection and RMS process planning problem, where two objectives are taken into account; the total completion time and the total manufacturing cost. Authors in [13], adapted archived multi-objective simulated annealing (AMOSA) to address the issue of generating an integrated design and process plan for RMS. Modularity was introduced as a criterion by the authors in addition to the cost and time. ...
