Flexible manufacturing systems (FMS): Current diffusion and main advantages

Sheet metal machining is a special area in manufacturing industry. The flexible manufacturing system (FMS) has been used in the fabrication of sheet metal products in order to achieve high flexibility and high production efficiency. The configure and parameters are key factors for the performance optimization of the FMS. In this paper the discrete event system simulation is used to evaluate the configure and parameters, and to optimize the performance of the FMS. The composition and characteristics of sheet metal FMS are introduced briefly, the evaluation criteria for FMS' performance and the maximal service capability that the piler can provide to the machining cells are given out. Some simulation schemes are constructed to evaluate whether the configure and parameters are reasonable, and whether the piler has sufficient serviceability. Some instructive conclusions have been reached from the simulation: (1) The parameters of the piler have great effect on the FMS's performance. (2) As the production process of sheet metal are comparatively simple and fixed, the scheduling rules have minor influence on the FMS's performance. (3) On given parameters, a piler can provide sufficient service for four punch-shear machining cells.

Flexibility of flexible manufacturing systems (FMSs) has been considered as an effective tool to compete in the present manufacturing environment. Enormous research efforts have been made to harness the benefits of flexibility through superior control strategies. While modeling flexibility and control strategies, researchers have mostly assumed an information system that can provide real-time control. Literature qualitatively reports that the real-time control can be highly capital intensive and difficult to achieve. This paper focuses on FMS operating under review-period (RP)-based control and presents a combined study of routing flexibility (RF), control strategies, and information system under Taguchi's method using simulation. RP-based control for FMS has been compared with real-time control. This paper contributes an approach for the decision maker to study the performance of an FMS operating under RP control and to identify the periodicity (time interval) of RP that will not deteriorate its performance in comparison to real-time control. It also helps the decision maker to reach a tradeoff between RP-based control and real-time control. The results show that RP-based control can be effectively implemented on an FMS having lower RF level. RP-based control can outperform real-time control with a superior control strategy and smaller RP size. The results under Taguchi's method suggest that the RF and control strategy should have maximum relative percentage contributions in FMS performance, whereas contribution of the RP (information system) should be minimum. Increasing the relative percentage contribution of the information system may deteriorate the performance of FMS. The information system is needed as a catalyst to facilitate the contributions of other factors in improving the FMS performance and not its own contribution. © 2007 IEEE.

Research has found that while flexibility is purported to be a prime advantage of flexible manufacturing systems (FMS), it has not yet become a major competitive priority for American and European manufacturers. Moreover, many installed systems either are not very flexible or do not use the available flexibility to the best advantages. We suspect that inappropriate application of the conventional production management concepts to FMS is one of the major factors that hinders current FMSs from capturing their key advantage: flexibility. This paper takes a first step to examine why the conventional job shop loading and fixed routeing concepts fail to capture the flexibility of FMS. Once problems are identified we suggest ways to exploit the FMS flexibility. We further conduct an experiment to investigate FMS performance under different manufacturing policies and operating conditions. The results indicate that the performance of an FMS greatly diminishes when the conventional job shop loading and fixed routeing concepts are applied. This is because the inherent flexibility of the FMS is not fully utilized. Moreover, the operating conditions such as tooling duplication levels and operation processing time variation could also significantly affect the FMS performance.

Discrete event simulation (DES) is a well-recognized technique in evaluating the performance of flexible manufacturing systems (FMS). FMSs lend themselves to such a method because of the inherent complex element interactions, the queueing principles involved and the changes of state that occur within them. In particular, to attempt to rely on intuition, experience or static optimization alone can lead only to a limited FMS design solution. DES allows the analyst to recognize the dynamics of FMS while also allowing him to interact with the system model in such a way that changes in, say, layout, system design and operation strategies (scheduling, routing, etc.) may be assessed. Hence, “what if?” scenarios are orchestrated [1]. The analyst not only is able to make changes to the simulation rules but also is capable of altering data records and displays.

Flexible manufacturing systems (FMS) consists of a set of numerically controlled machines, material handling systems (MHS), information system and service centres all working towards a common production schedule goal being the achievement of economic product manufacture in short period of time[1]. An FMS is highly versatile production system capable of performing many different operations given the proper tooling, fixtures and materials. Another feature of this system is the short switching time between different operations assigned to its individual workstations. An FMS to function effectively must have an on-line control system capable of reacting quickly to any problem occurring in the system by providing decisions to deal with the problem in a short period of time. The FMS control system thus needs to receive accurate and reliable information from the shop floor. The operational control of an FMS in general involves accessing a large set of static and dynamic data, and requires complex control algorithms. This paper presents a control system developed for real-time operational control of an FMS. The system is organised hierarchically, each level responsible for making decisions according to the operational needs and the time criticality of the particular level. The overall aim of the control system is to optimise FMS performance while meeting the production requirements.

Summary form only given as follows. As manufacturers face a more competitive environment, they are forced to satisfy needs for productivity and flexibility simultaneously. To satisfy these needs and remain competitive, many manufacturers are upgrading their manufacturing technology by introducing flexible manufacturing systems (FMS). The evidence, however, suggests that the payoffs from investing in FMS will not be adequate unless manufacturers also ensure their human resources lever the new technology. The programmable capabilities of FMS are limited to handling quickly and efficiently the more routine aspects of variable manufacturing processes. For FMS to achieve economies of scope and fulfil their promise, FMS need to be staffed by individuals who can lever FMS. These are individuals who can work with FMS, and who can go beyond the limitations of FMS by handling quickly and efficiently the less routine aspects of these manufacturing processes. To lever FMS, human resources need to be managed differently than in traditional factories. They need to be organized into self-managing teams; they need to be classified into few job categories; they need to be selected because of their FMS matching characteristics; they need to be trained; and they need to be rewarded for their varied skills and cooperation. Moreover, they need to be empowered by managers who substitute the coaching approach for the command and control approach, and who seek closer union-management relationships.

A flexible manufacturing system (FMS) is a highly automated, complex system. Simulation is a well-proven method to design or analyze an FMS. Deployment of a radio frequency identification (RFID) system in FMS produces large volumes of RFID data streams, which provide valuable information to improve the operation of FMS. Different frameworks are presented in this paper regarding the use of RFID data streams in an FMS simulation. Simulations are performed with AutoMod software. Related technical details are also presented. The paper’s structure is complied as the following steps: step 1, the introduction of AMHS, FMS and AutoMod; step 2, FMS simulation using AutoMod software; step 3, simulation frameworks driven by RFID data streams; step 4, conclusion.

There are generally ten (10) common forms of flexibilities involved in Flexible Manufacturing System (FMS), and material handling flexibility is one of them. Implementing an FMS with many Automated Guided Vehicles (AGV) allows the system to increase productivity by choosing the appropriate AGV to handle transportation for processing parts between different workstations. In this paper, we study the effect of increasing the number of AGVs in the system on the performances of Flexible Manufacturing System. The performances considered are Productivity, Makespan, Waiting for Process Time and Waiting for Transport Time.

Optimizing flexible routing in flexible manufacturing systems is one of the aspects that increase the efficiency of flexible manufacturing systems especially in dynamic environment systems. This article presents a multistage approach to solve flexible routing problem in flexible manufacturing systems. Multistage approach includes three stages; the first stage is a production simulation system to find the fitness of the flexible manufacturing systems corresponding to different products’ routes’ groups. The second stage proposes an artificial neural network approach to predict the products’ routes’ group in flexible manufacturing systems. The last stage combines genetic algorithms and artificial neural network to optimize proper routes for all product types in flexible manufacturing systems. Multistage approach proposed in this study aims to reduce the computational time required to obtain and optimize the flexible routes in flexible manufacturing systems. The results of this study show that the artificial neural network can be used efficiently to predict the flexible routes in flexible manufacturing systems and it reduces the computational time for routes’ optimization required with production simulation system. This characteristic improves the flexibility of flexible manufacturing systems since it can be adapted frequently against any change in production ratios.

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

A new FMS simulator with object-oriented-programming techniques

With the advance of research, design and operation of flexible manufacturing systems (FMS) which are designed to produce a variety of different part types with high machine utilization, short lead times and little work-in-progress inventory, new requirements like high modeling efficiency, high model validity and credibility and effectiveness and correct analysis of results are defined. Simulation is an efficient tool to verify design concepts, to select machinery, to evaluate alternative configurations and to test system control strategies of an FMS, etc. This paper presents a useful tool for simulating the behavior of random FMSs. This simulator is used for real time alternative routing selection based on a group of metaheuristics principles which include in particular simulated annealing (SA), genetic algorithm (GA), taboo search (TS), ant colony algorithms (ACO), particle swarm optimization (PSO) and electromagnetism like method (EM). This software is also can used for performance and sensitivity analysis of theses techniques jugged by the production rate, machines and material handling utilization rate, the cycle time and the work in process.

In this paper, we use Petri nets to analyse flexible manufacturing systems (FMS) to achieve the following objectives. The first objective is to derive a theorem for checking the buffer overflows in an FMS. The second objective is to schedule the earliest starting times for a sequence of operations in an FMS by the timed Petri net. The FMS may involve many part types, machines, pallets and so on. The third objective is to detect the deadlock phenomenon by a corrected algorithm of the P-invariant theory for Petri nets. This method will enable us to know whether there exists any deadlock in an FMS. The algorithm is implemented in the Turbo Pascal language. The fourth objective is to search for the optimal scheduling rule in an FMS. The search procedure is also implemented in the Turbo Pascal language¨ The optimal machine scheduling rule can result in a better throughput.

Deadlock/livelock problems can cause catastrophic results in flexible manufacturing systems (FMS) by forcing them to stop production processes at an unpredictable stage of production. Therefore, they must be handled properly in order to obtain flawless production in FMS. Petri net (PN) based deadlock/livelock studies for FMS have been widely carried out to obtain live PN models. When a PN model is live, this ensures that the modelled FMS is deadlock-free. Firstly, an uncontrolled (plant) PN model is constructed for the FMS suffering from deadlocks/livelocks. Then reachability graph (RG) analysis of this PN model is carried out to provide all possible reachable states of the modelled system. An RG of a PN model of an FMS suffering from deadlock/livelock problems can be split into the live zone (LZ) and the deadlock zone (DZ). The former is the set of system states considered to be legal (good), while the latter is the set of system states regarded to be illegal (bad). The LZ represents the optimal (maximally permissive) system behavior that must be provided by an optimal control policy. Generally, in PN based liveness studies, a liveness enforcing supervisor (LES) consisting of a set of control places (CP) is computed. Then the controlled (closed-loop) PN model is obtained by merging the plant PN model and the LES. If the number of reachable live states of the controlled PN model is the same as the ones that exist in the LZ, then the controlled model is optimal. Otherwise, it is suboptimal. The greater the number of live reachable states of the RG, the more products to be produced within the FMS. Therefore, the number of reachable live states of the controlled PN model is considered as a quality measure. It defines the behavioral permissiveness of the controlled model. In the literature, to verify the behavioral permissiveness of the PN models, the PN tool called INA (Integrated Net Analyzer) has been widely used. Recently, by means of INA, a method was proposed to compute the number of states within the LZ and the DZ of a given PN model suffering from deadlocks/livelocks. Unfortunately, INA cannot compute RGs of large PNs having a few millions of states. Therefore, in this paper by means of TINA (TIme petri Net Analyzer), an alternative faster and much more efficient method is proposed for the computation of the number of states within the LZ and the DZ for PN models with large RGs. In addition, in order to enable the development of new PN based deadlock prevention methods, further computations are also proposed. These include the computation of the set of states in the DZ, LZ and first met bad markings (FBMs) for a given PN model suffering from deadlocks/livelocks. The applicability and the effectiveness of the proposed methods are demonstrated by considering several well-known examples of FMS from the relevant literature.INDEX TERMS Flexible manufacturing systems (FMS), deadlock, deadlock prevention, Petri Nets (PN), liveness enforcing supervisor (LES), PN computation tools.

The optimal controller synthesis problem for discrete event control of a flexible manufacturing system (FMS) is presented in this paper. To explicitly formulate concurrent activities, multiple resources sharing, precedence constraints and dynamic routing in FMS operation, we adopt timed (place) Petri nets for problem representation. The A* based heuristic search algorithm is proposed to search for an optimal event sequence to achieve minimum-time and deadlock-free discrete event control. Based on the obtained event-driven sequence, we use two levels of specification to design the optimal sequence controller. The coordination control level consists of synchronization and parallelism of different sub-systems and is specified by decision-free Petri nets (marked graphs). The local control level consists of running elementary sequences for subsystems and is specified by sequential function charts.