Performance Of Flexible Manufacturing System Research Articles

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems.

A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best–worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates production.

Investigation of the Importance of Machine Sequence Flexibility on A Flexible Manufacturing System Performance

Machine sequence flexibility is defined as the combination of operation and routing flexibilities in this study. Its importance in the performance level of a flexible manufacturing cell (FMC) is investigated in this study. Studies related to the effects of various flexibility types, such as routing flexibility, are available in the literature. For example, studies related to routing flexibility try to measure the effects of routing flexibility on the performance levels in the operation of manufacturing systems under their own manufacturing environments. Similarly, this study also aims to present a performance measurement model based on Taguchi methods to evaluate the effects of machine sequence flexibility factors on the FMC performance and obtain an optimum and robust performance level. Two crucial responses, such as manufacturing lead time (MLT) and surface roughness (SR) are analysed to optimize the FMC performance. Robot speed, cutting tool type, and work-part material type are taken as the three other input factors to show the importance of machine sequence flexibility with respect to the other inputs. The study presented in this paper points out that machine sequence flexibility is the most effective input factor among the four input factors in the performance of the FMC.

Using inventory related performance measures in the analysis of a production/inventory flexible manufacturing system

To survive in the ever-increasing competition in the market, manufacturing firms must adopt the latest technologies available. Flexible manufacturing systems (FMS) provide an effective solution to the problem, but its performance should be continually enhanced to remain competitive in the market. The aim of this work was to identify the effect of routing flexibility and system load conditions on the performance of FMS using both production and inventory related performance measures. ARENA simulation software was used to model and study the system. (R, r) inventory control policy was applied to the production/inventory system. Routing flexibility was found to have a significant effect on all the performance parameters, with maximum improvement of performance observed at increase of level from 0 to 1. It was further observed that the system load condition does not have a significant effect on any of the performance measure. The FMS showed slightly improved performance when the system was fully balanced. It can be concluded that the inventory related performance measures give comparable results to that of production related performance measures.

AGV’s effect on the performance of flexible manufacturing system in stochastic environment

This simulation-based experimental study focuses on the performance of stochastic flexible manufacturing system with Automated Guided Vehicles AGVs. Four levels of AGV fleet size (i.e. 1 AGV, 2 AGVs, 3 AGVs and 4 AGVs) at four different AGV velocities (i.e. 2 m/s, 4 m/s, 6 m/s and 8 m/s) with four sequencing rules are used to perform the experiments. The performance of the system is measured by two performance measures like make-span time and resource utilization. It is observed that the performance improves as the numbers of AGVs increases in all combinations and this increase is more visible when the system runs with 2 AGVs instead of 1 AGV. The impact of AGV velocity is found to be more visible with the use of more number of AGVs. There was effect of sequencing rules for different combinations of number of AGVs and velocities.

Investigating the effect of input variables on the performance of FMS followed by multi-response optimization: A simulation study

Flexible manufacturing systems (FMSs) help in producing reliable and good-quality products. The performance of FMSs depends on the various input variables and therefore, it is important to investigate their effect on the performance of the system. This work explores the effect of three input variables i.e., newly developed sequencing rules (SR), the number of machines, and AGV selection rules to determine their optimal setting that yields optimal results of three performance measures viz. makespan time, AGV utilization, and machine utilization simultaneously. ARENA simulation software was used to develop a replicate model of an FMS problem taken from literature. Nine models for a different combination of variables as input as per Taguchi’s L9 orthogonal array were developed and data for three performance measures were collected. Further, multi-response optimization using the integrated MEREC-MARCOS method was performed. The results of the study revealed that considering 3 machines, with GMPD as SR and shortest distance as AGV selection rule yields the optimal performance of the FMS. Further, among the three input variables, the number of machines was found to have a significant effect on the multi-performance measures. Finally, the optimal solution obtained in the study was validated by performing a confirmation test.

Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies

The implementation of Industry 4.0 technologies has improved the flexibility of the entire manufacturing system. These technologies are the Internet of Things (IoT), big data, Artificial Intelligence (AI), Additive Manufacturing (AM), advanced robotics, virtual reality, cloud computing, simulation, and among others, have arisen to improve the flexibility in the entire manufacturing system. Industry 4.0 is becoming recognised as a unique industrial paradigm. It is predicated on the widespread adoption of communication and information technology, which would lead to improved organisational performance and flexibility. The incorporation of Industry 4.0 is the true game-changer in terms of flexibility and customisation. Manufacturers may utilise this technology to build digital twins of items used by consumers in the real world. The digital twin gets real-time information from sensors on the actual objects. Manufacturers benefit from digital twin and simulation technology, including predictive maintenance and making errors easier and faster to rectify. This paper discusses Industry 4.0 and its Flexible Manufacturing System (FMS) capability. Different dimensions and technologies of Industry 4.0 Practices for improving FMS performance are studied,d and then discusses several flexible approaches using Industry 4.0 technologies are. One of the most significant benefits of adopting virtual infrastructure maintained by a service provider is improved flexibility. Cloud services allow auto-scaling, which means that the underlying computer resources automatically adjust to changing utilisation rates. Industry 4.0 increases production flexibility, allowing a facility to respond to market changes quickly. A plant control system automatically varies output depending on shifting utility rates, lowering production costs. Industry 4.0 offers some incredible benefits and has gone a long way in the last several years.

DEVELOPMENT OF AN AUTOMATED DESIGN SYSTEM FOR FLEXIBLE AUTOMATED PRODUCTION

Design of flexible manufacturing systems (FMS) of modern multi-level production is usually carried out on the basis of general rationing when using large recommendations. At the same time, the specifics and features of a particular production are not always taken into account. In such a design, the most important is the experience of the designer, which is not always based on modern methods of optimizing project solutions. Therefore, the problem of creating automated design systems in the development of flexible automated productions (FAP), which use cost equipment with numerical control (CNC) is extremely urgent. The development of automated design systems is based on the ideas of a systematic approach that determine different cycles of the process: design - production preparation - production. Information about the projected object is generated in the process of project development by different groups of users: researchers, designers, designers, technologists, production organizers. A multilevel, cyclical design process requires the use of such a volume of information that cannot be processed without the use of modern mathematical methods and calculated equipment. Therefore, it is extremely important to create automated gap design systems, which are marked by greater versatility, efficiency and possibility of development, improvement and adaptation to the conditions of various enterprises. Such requirements served as the basis for the creation of an automated design system , which allows to take into account a huge amount of information in the automatic cycle during the development of the project. The scientific novelty of the work is the development of an integrated automation system for the design of processing technology and the selection of elements of FMS structures. At the same time, information unity with the system of technological training of production at the level of operation of FMS is ensured.

A Methodology for the Assessment of Operator 4.0 Skills based on Sentiment Analysis and Augmented Reality

Human-Cyber-Physical Systems are the key to the successful operation of manufacturing systems. Consequently, the need for adequate assessment of human operators and tracking of their skills and competencies evolution emerges. Additionally, the advances in digital technologies encourage the development of supportive and adaptive frameworks for the operation of flexible manufacturing systems. This paper presents an Augmented Reality based methodology for the detailed evaluation of human skills and competencies based on the processing of raw textual data with a Natural Language Processing algorithm, aiming at the provision of technician guidance. The developed framework is tested and validated in an industrial environment.

Decision making in FMS by COPRAS approach

In this paper, decision-making related to the ranking of flexibility and performance indicators of the flexible manufacturing system (FMS) are taken. Some flexibility and performance selection attributes are considered related to the objectives. The selection of the right factor in this environment is still a complex issue. This paper presents the application of a MADM approach, i.e., complex proportional assessment (COPRAS) for decision making as the ranking of flexibility and performance indicator of FMS. Two illustrative examples are presented: one for the ranking of the flexibility of FMS and the second one for ranking of the performance indicator of FMS. The results obtained by the COPRAS approach for the ordering of flexibility are differentiated with the outcomes of other researches. The result of COPRAS methodology regarding the flexibility shows that product flexibility has the top flexibility, especially for the new part configuration. Secondly, productivity is the primary factor to boost the performance of FMS. This research has illustrated that the COPRAS methodology is logical and more significant for the selections of alternatives even can handle qualitative data for attributes.

Decision making in FMS by COPRAS approach

In this paper, decision-making related to the ranking of flexibility and performance indicators of the flexible manufacturing system (FMS) are taken. Some flexibility and performance selection attributes are considered related to the objectives. The selection of the right factor in this environment is still a complex issue. This paper presents the application of a MADM approach, i.e., complex proportional assessment (COPRAS) for decision making as the ranking of flexibility and performance indicator of FMS. Two illustrative examples are presented: one for the ranking of the flexibility of FMS and the second one for ranking of the performance indicator of FMS. The results obtained by the COPRAS approach for the ordering of flexibility are differentiated with the outcomes of other researches. The result of COPRAS methodology regarding the flexibility shows that product flexibility has the top flexibility, especially for the new part configuration. Secondly, productivity is the primary factor to boost the performance of FMS. This research has illustrated that the COPRAS methodology is logical and more significant for the selections of alternatives even can handle qualitative data for attributes.

Integrated maintenance and operations decision making with imperfect degradation state observations

In highly flexible and integrated manufacturing systems, such as semiconductor fabs, strong interactions between the equipment condition, operations executed on the various machines and the outgoing product quality necessitate integrated decision making in the domains of maintenance scheduling and production operations. Furthermore, in highly complex manufacturing equipment, the underlying condition is not directly observable and can only be inferred probabilistically from the available sensor readings. In order to deal with interactions between maintenance and production operations in Flexible Manufacturing Systems (FMSs) in which equipment conditions are not perfectly observable, we propose in this paper a decision-making method based on a Partially Observable Markov Decision Processes (POMDP's), yielding an integrated policy in the realms of maintenance scheduling and production sequencing. Optimization was pursued using a metaheuristic method that used the results of discrete-event simulations of the underlying manufacturing system. The new approach is demonstrated in simulations of a generic semiconductor manufacturing cluster tool. The results showed that, regardless of uncertainties in the knowledge of actual equipment conditions, jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems-a new metaheuristic Ant Colony Optimization (ACO) algorithm is proposed.

Simultaneous Scheduling of Machines and AGVs in FMS through Simulated Annealing Algorithm

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems- a new metaheuristic Simulated Annealing (SA) algorithm is proposed.

A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle

The product quality and production efficiency of a flexible manufacturing system have improved effectively by introducing the computer management and the material transportation system. The flexible manufacturing system performance greatly depends on the performance of the material transportation system. As a mobile robot controlled by a central controller, an automated guided vehicle has a strong ability for material transportation. This article studies a dynamic scheduling problem in a shop floor, where machines and automated guided vehicles run at a specified speed and specifies a mathematical model for the dynamic scheduling problem with the goal of makespan minimization. Meanwhile, inspired by the hormone secretion principle of the endocrine system, a bio-inspired scheduling optimization approach is developed to solve the dynamic scheduling problem in the flexible manufacturing system. To verify its practical application, the bio-inspired scheduling optimization approach and other scheduling approaches are tested, and the results illustrate that the bio-inspired scheduling optimization approach has better scheduling performance as well as optimizes the quality of integrated and real-time scheduling of machines and automated guided vehicles.

Flexible Manufacturing Systems Performance Analysis and Improvement

Due to the intense market competition, managements of manufacturing industries are striving to optimize their manufacturing lead times, enhance quality of products, increase part variety, and reduce production cost. Thus, the trend towards market globalization requires these manufacturing environments to be designed, analyzed for its performance and improved in such way that it can cater the market place challenges to survive and grow in the sector. In this paper a case study of Hibret Manufacturing & Machine Building Industry (HMMBBI) in flexible manufacturing system (FMS) shop is presented. The goal of the study is to analyze the performance of FMS and propose a performance improvement method. Analytical methods of performance analysis-bottleneck model and its extension called ‘extended bottleneck model’ are applied to determine the current production rate and the percentage workstations utilization of the existing FMS shop. The proposed FMS with balanced workloads between workstations has shown a significant improvement in percentage utilization of workstations, production lead time and as well as production throughput.

Simultaneous Scheduling of Machines and AGVs in FMS Through Hybrid JAYA Algorithm

High amount of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding a variety of products with reduced product life cycles. Flexible manufacturing system (FMS) is the right choice to achieve these challenging tasks. The performance of FMS is dependent on the selection of scheduling policy of the manufacturing system. In Traditional scheduling problems machines are as considered alone. But material handling equipment’s are also valuable resources in FMS. The scheduling of AGVs is needed to be optimized and harmonized with machine operations. Scheduling in FMS is a well-known NP-hard problem due to considerations of material handling and machine scheduling. Many researchers addressed machine and AGVs individually. In this work an attempt is made to schedule both the machines and AGVs simultaneously. For solving these problems- a new hybrid metaheuristic JAYA algorithm (HJAYA) is proposed.

Decisional DNA based intelligent knowledge model for flexible manufacturing system

Modeling an effective mechanism for design and control strategies for the implementation of a flexible manufacturing system (FMS) has been a challenge. Consequently, to overcome this issue various techniques have applied in the past but most of these models are effective only for some specific situation or an element of FMS. In this study, the knowledge representation technique of Decisional DNA (DDNA) is applied to FMS to develop a generic model to achieve effective scheduling and manufacturing flexibility. Decisional DNA based Virtual Engineering Objects (VEO) are used as communicating media between machines, equipment and works pieces. The concept of Virtual Engineering Process (VEP) is applied for modeling routing flexibility. VEOs combined with VEPs form FMS-DDNA model, which facilitates in enhancing the performance of FMS, by inducing intelligence based on its own previous experience thus making it practical and smart.

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

This article presents a performance evaluation of a flexible manufacturing system (FMS), in which the system and its components can have different performance levels ranging from perfect functioning to total failure. An original multistate transition process for the FMS’s main component, Computerized Numerical Control (CNC) machine tools, proposed in the current article and treated as an aging process because of its increasing failure rate is investigated. The Markov reward approach is utilized to analyze the availability of CNC machine tools and sensitivity analysis of each non-operational state to the machine’s downtime. To overcome the “state explosion problem” in the subsequent FMS performance evaluation process, an application of the Lz-transform method is proposed to determine the performance evaluation and availability of such aging multistate FMS. An illustrated case is presented to demonstrate the performance evaluation process. The results show that the suggested methods can be implemented in engineering decision making and construction of various aging multistate FMS related to demands, availability, and output performance.

Effect of nature-inspired algorithms and hybrid dispatching rules on the performance of automatic guided vehicles in the flexible manufacturing system

The application of nature-inspired algorithms and priority hybrid dispatching rules for simultaneous scheduling and dispatching of automatic guided vehicles (AGVs) are observed to be highly significant for the best utilization of the flexible manufacturing system (FMS) facility. The earlier studies on the use of AGVs in the FMS have generally focused on minimizing the complexity of AGV operations by optimizing their material handling schedule and their routing in different types of FMS configurations. However, this is achieved only by using an appropriate optimizing algorithm and dispatching rule under different FMS operating conditions. The aim of this paper is to use the simulation methodology so as to compare and analyze the combined effect of four experimental factors, namely four types of priority hybrid dispatching rules, three different nature-inspired algorithms, two levels of loading/unloading times and two levels of machine failures, on the different performance parameters of the FMS. The performance parameters of the FMS analyzed are simultaneous minimization in distance travel and backtracking of AGV, the total production rate of the FMS, mean AGV utilization and mean work center utilization in the FMS. Additionally, in order to find the mean and interaction effect of the experimental factors on the aforementioned performance parameters of the FMS, an analysis of variance (ANOVA) is also carried out. From the results, it is observed that the interaction between experimental factors, namely nature-inspired algorithms and loading–unloading time, has a significant effect on the performance measures, namely simultaneous reduction in distance travel and backtracking, mean work center utilization (%) and total production rate of the FMS. The interaction between aforesaid experimental factors has no significant effect on mean AGV utilization (%) in the FMS. However, the interaction between priority hybrid dispatching rules and loading/unloading times is found to have significant effect on the mean work center utilization (%) in the FMS facility.