Reconfigurability in cellular manufacturing systems: a design model and multi-scenario analysis

Bortolini, Marco; Galizia, Francesco Gabriele; Mora, Cristina; Pilati, Francesco

doi:10.1007/s00170-019-04179-y

Cited by 35 publications

(31 citation statements)

References 36 publications

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“…Since commercial software, like LINGO, are capable to find a global optimum for ILP and MILP problems, some papers compared their results with those obtained by approximate [5,53,54] or hybrid methods [51] to verify the reliability of those methods. Others just used these solvers singly [28,47,48]. Linear problems were also solved by different exact approaches, like enumerative [2,24] and iterative techniques [11,41,44].…”

Section: Modelling and Optimizing The Rms Configurationmentioning

confidence: 99%

Optimization of Reconfigurable Manufacturing Systems Configuration: A Literature Review

Sabioni

Daaboul

Duigou

2021

Lecture Notes in Mechanical Engineering

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Reconfigurable Manufacturing Systems (RMS) have gained importance in the current context of increasing high variety demand, Mass Customization (MC) and market instability, due to their ability of being quickly modified to adjust their production capacity to attain sudden fluctuations in market demands as well as to accommodate operations of new products. RMS can be configured at system and machine levels. Many papers have described the RMS configuration as combinatorial optimization problems and proposed several techniques to optimize them in terms of different responses of interest. This paper presents a literature review that seeks to understand how RMS configuration has been addressed in terms of configuration level, optimization problem modelling and techniques applied to solve it. This work aims to assist researchers working on RMS configuration to identify trends and new research opportunities.

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Section: Modelling and Optimizing The Rms Configurationmentioning

confidence: 99%

Optimization of Reconfigurable Manufacturing Systems Configuration: A Literature Review

Sabioni

Daaboul

Duigou

2021

Lecture Notes in Mechanical Engineering

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“…Such assumptions are derived by relevant existing studies proposing optimization model formulations for CRMSs design and management [17,24,25]. The aim is to reproduce a realistic industrial setting having a defined layout (re-layout actions are usually time and cost consuming) and in which basic data about parts, auxiliary modules, and machines are collected and available.…”

Section: Optimization Model Design: Assumptions Notations and Analytic Formulationmentioning

confidence: 99%

“…The authors solved this model using an archived multi-objective simulated annealing method. Bortolini et al [9,17] defined an optimization model for the RMS design and management balancing the trade-off between the machine reconfiguration and the inter-cell part travel time.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Bi-Objective Design and Management of Reconfigurable Manufacturing Systems to Optimize Technical and Ergonomic Performances

et al. 2020

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In the last decades, Reconfigurable Manufacturing Systems (RMSs) rose as an emerging manufacturing strategy matching the modern industrial and market requirements asking for a wide variety of products in flexible batches. A traditional reconfigurable manufacturing environment consists of dynamic cells, called Reconfigurable Machine Cells (RMCs), including a set of machines called Reconfigurable Machine Tools (RMTs). Such machines are characterized by fixed elements, i.e., basic modules, and dynamic elements, i.e., auxiliary modules, allowing them to perform different operations. Despite their automation level, these systems require the intervention of the human operators in performing specific tasks, e.g., handling of the auxiliary modules from the warehouse to the RMTs and their assembly/disassembly to/from the RMTs. This issue rises relevant ergonomic and safety questions due to the human–machine collaboration. Following this stream, this paper proposes and applies a bi-objective optimization model for the design and management of RMSs. The technical objective function minimizes the reconfiguration time, i.e., the time needed to equip the RMTs with the required auxiliary modules, and the part and auxiliary module travel time among the RMCs. The ergonomic objective function minimizes the repetitive movements performed by the human operators during the working activities according to the ISO 11228-3 standard. Results show the existence of a good trade-off between the two objective functions, proving the possibility to improve the ergonomic conditions of the human operators without excessively increasing the total time needed for RMTs reconfiguration and for part and auxiliary module travelling.

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“…In particular, Industry 4.0 includes nine enabling technologies to support the implementation of the paradigm in industry, as in Table 1 [2]. In the current production context, factors as efficiency, flexibility and responsiveness to the dynamic market demand are crucial for the survival of industrial companies [1][2][3][4]. Modern production systems have to face with emerging factors such as shorter product lifecycles, the mass-customization and personalization and the reduction of the delivery time to the customers [5,6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Automation Assembly Systems in the Industry 4.0 Era: A Reference Framework and Full–Scale Prototype

et al. 2021

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Industry 4.0 emerged in the last decade as the fourth industrial revolution aiming at reaching greater productivity, digitalization and operational efficiency standard. In this new era, if compared to automated assembly systems, manual assembly systems (MASs) are still characterized by wide flexibility but poor productivity levels. To reach acceptable performances in terms of both productivity and flexibility, higher automation levels are required to increase the skills and capabilities of the human operators with the aim to design next-generation assembly systems having higher levels of adaptivity and collaboration between people and automation/information technology. In the current literature, such systems are called adaptive automation assembly systems (A3Ss). For A3Ss, few design approaches and industrial prototypes are available. This paper, extending a previous contribution by the Authors, expands the lacking research in the field and proposes a general framework guiding toward A3S effective design and validation. The framework is applied to a full-scale prototype, highlighting its features together with the technical- and human-oriented improvements arising from its adoption. Specifically, evidence from this study show a set of benefits from adopting innovative A3Ss in terms of reduction of the assembly cycle time (about 30%) with a consequent increase of the system productivity (about 45%) as well as relevant improvements of ergonomic posture indicators (about 15%). The definition of a general framework for A3S design and validation and the integration of the productivity and ergonomic analysis of such systems are missing in the current literature, representing an element of innovation. Globally, this research paper provides advanced knowledge to guide research, industrial companies and practitioners in switching from traditional to advanced assembly systems in the emerging Industry 4.0 era matching current industrial and market features.

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Reconfigurability in cellular manufacturing systems: a design model and multi-scenario analysis

Cited by 35 publications

References 36 publications

Optimization of Reconfigurable Manufacturing Systems Configuration: A Literature Review

Optimization of Reconfigurable Manufacturing Systems Configuration: A Literature Review

Bi-Objective Design and Management of Reconfigurable Manufacturing Systems to Optimize Technical and Ergonomic Performances

Adaptive Automation Assembly Systems in the Industry 4.0 Era: A Reference Framework and Full–Scale Prototype

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