Optimizing reconfigurable manufacturing systems: A Simulation-based Multi-objective Optimization approach

Díaz, Carlos Alberto Barrera; Fathi, Masood; Aslam, Tehseen; Ng, Amos H. C.

doi:10.1016/j.procir.2021.11.310

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…When it comes to RMS problems, meta-heuristic methods such as genetic algorithms have become very popular in the literature because they have shown better performance in generating near-optimal solutions [7]. In addition, simulation has been a satisfactory tool to support the modeling and analysis of manufacturing systems for many years [13]. Because of the complexity and dynamism inherent in manufacturing systems, engineers and DMs supported by simulation tools can perform better analysis and, therefore, obtain a better understanding of the real-world systems [14].…”

Section: Simulation and Optimization In Manufacturing Systemsmentioning

confidence: 99%

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Smedberg

Díaz

Nourmohammadi

et al. 2022

MCA

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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.

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Section: Simulation and Optimization In Manufacturing Systemsmentioning

confidence: 99%

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Smedberg

Díaz

Nourmohammadi

et al. 2022

MCA

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“…Differently, when DES becomes impractical due to the size and complexity of the RMS, modelling several different configurations becomes unattainable. This problem has been overcome in studies such as [20] and [21], combining DES and optimization enabling the advantages of both techniques.…”

Section: Reconfigurable Manufacturing Systems and Discrete-event Simu...mentioning

confidence: 99%

Reconfiguration Assessment for Production Volume Changes Using Discrete-Event Simulation: A Large-Size Highly-Customized Product Case Study

Zúñiga

Díaz

Navarro

et al. 2022

Advances in Transdisciplinary Engineering

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Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems. For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements. Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different what-if scenarios, the focus is on the capacity reconfiguration using Discrete-Event Simulation. The results can serve as a guideline for decision-makers and stakeholders working with complex mass customization manufacturing systems and space-constrained facility layouts.

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“…It is worth mentioning that BAP is part of a larger manufacturing problem, dealing with the selection of optimal configurations for manufacturing systems. Hence, works in this field include also extended optimization problems with respect to decision variables and objective function, such as joint selection of machine and buffers with productivity performance [22], energy-efficient performance [23], transfer line [24,25] and assembly line [26] balancing, CONWIP policies [27], and time buffers [28] for cost optimization. Since the proposed work deals with BAP in serial lines, a set of relevant works is classified in Table 1.…”

Section: Minimization Of the Average Work-in-process Inventorymentioning

confidence: 99%

“…5 and 6 respectively. The optimal solution n OPT = [8,16,22,19,18,19,17,9] is found in 52 iterations, hence by evaluating 52 different configurations.…”

Section: Analysis Of Iterationsmentioning

confidence: 99%

A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines

Magnanini

Terkaj²,

Tolio

2022

Int J Adv Manuf Technol

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The Buffer Allocation Problem is a well-known optimization problem aiming at determining the optimal buffer sizes in a manufacturing system composed by various machines decoupled by buffers. This problem still has scientific relevance because of problem complexity and trade-off between conflicting goals. Moreover, it assumes industrial relevance in reconfigurable manufacturing lines, where buffer sizes can be easily adapted to the production scenario. This work proposes a novel algorithm integrating performance evaluation and optimization by means of throughput cuts based on a linear approximation. Numerical results show the validity of the proposed approach with respect to the traditional gradient-based method. Moreover, an industrial case study integrating the proposed approach into a decision-support system for the buffer allocation and reallocation is analyzed.

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Optimizing reconfigurable manufacturing systems: A Simulation-based Multi-objective Optimization approach

Cited by 12 publications

References 25 publications

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Reconfiguration Assessment for Production Volume Changes Using Discrete-Event Simulation: A Large-Size Highly-Customized Product Case Study

A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines

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