Random Flexible Manufacturing System Research Articles

Real‐time routing selection for automated guided vehicles in a flexible manufacturing system

PurposeThe purpose of this paper is to present the development of an architecture for real‐time routing of automated guided vehicles (AGV) in a random flexible manufacturing system (FMS).Design/methodology/approachAGV routing problem is modeled using an evolutionary algorithm‐based intelligent path planning model, which handles vehicle assignments to material handling requests and makes routing decisions with the objective of maximizing the system throughput. The architecture is implemented on a 3‐layer software environment in order to evaluate the effectiveness of the proposed model.FindingsThe proposed architecture, along with the evolutionary algorithm‐based routing model, is implemented in a simulated FMS environment using hypothetical production data. In order to benchmark the performance of the path planning algorithm, the same FMS model is run by traditional dispatching rules. The analysis shows that the proposed routing model outperforms the traditional dispatching rules for real‐time routing of AGVs in many cases.Research limitations/implicationsFuture work includes expanding the scope of the current work by developing and implementing other routing models and benchmarking them against the proposed model on different performance measures.Originality/valueThe implementation of evolutionary algorithms in real‐time routing of AGVs is unique. In addition, due to its modularity, the proposed 3‐layer architecture can allow effective and efficient integration of different real‐time routing algorithms; therefore it can be used as a benchmarking platform.

Performance-based dynamic scheduling model for flexible manufacturing systems

A performance-based dynamic scheduling model for random flexible manufacturing systems (FMSs) is presented. The model is built on the mathematical background of supervisory control theory of discrete event systems. The dynamic FMS scheduling is based on the optimization of desired performance measures. A control theory-based system representation is coupled with a goal programming-based multi-criteria dynamic scheduling algorithm. An effectiveness function, representing a performance index, is formulated to enumerate the possible outputs of future schedules. Short-term job scheduling and dispatching decisions are made based on the values obtained by optimizing the effectiveness function. Preventive actions are taken to reduce the difference between actual and desired target values. To analyse the real-time performance of the proposed model, a software environment that included various Visual Basic Application® modules, simulation package Arena®, and Microsoft Access® database was developed. The experimentation was conducted (a) to determine the optimum look-ahead horizons for the proposed model and (b) to compare the model with conventional scheduling decision rules. The results showed that the proposed model outperformed well-known priority rules for most of the common performance measures.

Dissimilarity Maximization Method for Real-time Routing of Parts in Random Flexible Manufacturing Systems

This paper presents a dissimilarity maximization method (DMM) for real-time routing selection and compares it via simulation with typical priority rules commonly used in scheduling and control of flexible manufacturing systems (FMSs). DMM aims to reduce the congestion in the system by selecting a routing for each part among its alternative routings such that the overall dissimilarity among the selected routings is maximized. In order to evaluate the performance of DMM, a random FMS, where the product mix is not known prior to production and off-line scheduling is not possible, is selected for the simulation study. A software environment that consists of a computer simulation model, which mimics a physical system, a C++ module, and a linear program solver is used to implement the DMM concept. In addition to DMM, the simulation study uses two priority rules for routing (i.e., machine) selection and seven priority rules for selecting parts awaiting service at machine buffers. The results show (1) DMM outperforms the other two routing selection rules on production rate regardless of the part selection rule used, and (2) its performance is highly dependent on the part selection rules it is combined with.

An Evaluative Study of Operation Grouping Policies in an FMS

The increased use of flexible manufacturing systems to provide customers with diversified products efficiently has created a significant set of operational challenges for managers. This technology poses a number of decision problems that need to be solved by researchers and practitioners. In the literature, there have been a number of attempts to solve design and operational problems. Special attention has been given to machine loading problems, which involve the assignment of job operations and allocation of tools and resources to optimize specific measures of productivity. Most existing studies focus on modeling the problem and developing heuristics in order to optimize certain performance metrics rather than on understanding the problem and the interaction between the different factors in the system. The objective of this paper is to study the machine loading problem. More specifically, we compare operation aggregation and disaggregation policies in a random flexible manufacturing system (FMS) and analyze its interaction with other factors such as routing flexibility, sequencing flexibility, machine load, buffer capacity, and alternative processing-time ratio. For this purpose, a simulation study is conducted and the results are analyzed by statistical methods. The analysis of results highlights the important factors and their levels that could yield near-optimal system performance.

Adaptive scheduling in random flexible manufacturing systems subject to machine breakdowns

In a FMS dynamic scheduling environment, frequent rescheduling to react to disruptions such as machine breakdowns can make the behaviour of the system hard to predict, and hence reduce the effectiveness of dynamic scheduling. Another approach to handle the disruptions is to update the job ready time and completion time, and machine status on a rolling horizon basis, and consider the machine availability explicitly in generating schedules. When machine downtime has a small variation, the operation completion time is estimated by using limiting (steady-state) machine availability. However, steady-state analysis is sometimes unlikely to provide a complete picture of the system when there is a large variation in machine downtime and repair time, and frequent disruptions (e.g. tool failures) exist. Transient analysis of machine availability will be more meaningful in such a situation during a finite observation period. In this paper, an adaptive scheduling approach is proposed to make coupled decisions about part/machine scheduling and operation/tool assignments on a rolling horizon basis, while the operation completion time is estimated by a transient machine availability analysis based on a two-state continuous time Markov process. The expected tool waiting time is explicitly considered in the job machine scheduling decision. The effectiveness of the proposed method is compared with other approaches based on various dispatching heuristics such as Apparent Tardiness Cost, Cost OVER Time, and Bottleneck Dynamics, etc under different shop load and machine downtime levels.

The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.

A rule-based, object-oriented framework for operating flexible manufacturing systems

A methodology for operating a flexible manufacturing system (FMS) is presented which consists of a decomposition of the problem, where heuristics, rules, and simulation are integrated to make the control decisions needed for random FMS's. Expert systems are used to make operating decisions, to choose heuristics, and make operating policies. Multiple passes of discrete-event simulation are employed to evaluate decision alternatives. Object-oriented programming provides a suitable environment to implement the integrative framework

Flexibility trade-offs in a random flexible manufacturing system: A simulation study

SUMMARY Flexible manufacturing systems (FMSs) are allowing firms to take advantage of diversified, low volume production of products with short life-cycles. The flexibility provided by an FMS improves the ability of a system to respond to change. Several types of manufacturing flexibility (M F) have been identified in the literature. Ideally, it is desirable to provide an FMS with all types of flexibility. However, there is a general consensus on the discordant nature of some of the flexibilities. This makes it imperative for management to recognize which flexibilities it deems critical to the manufacturing strategy of the firm. This allows the management to trade-off some of the less important flexibilities for the more important ones, as the need arises. This study is an attempt toward an empirical examination of the flexibility trade-offs in an FMS. By using computer simulation, we study the flexibility tradeoffs in the stochastic environment of a random FMS. Various configurations of the FMS are simulated under the influence of a variety of loading and dispatching strategies. The results show that there is a trade-off among the various flexibility types under most circumstances. However, some scenarios illustrate that an appropriate combination of the variables like scheduling environment, product variety, and system configuration, can be effective in containing these trade-offs. In other cases, it is seen that the FMS can accommodate more than one flexibility type simultaneously.

Selection of scheduling rules in FMSs—A simulation approach

This paper deals with the development of a microcomputer-based simulation model of a random flexible manufacturing system aimed at solving scheduling problems. The model is written in SLAM II and can be used interactively. It offers five alternative scheduling rules, but other rules could be incorporated if required. The selection process is demonstrated through an example, and an experimental design is conducted to evaluate the effect of changes in the levels of various resources on system performance.

Scheduling random flexible manufacturing systems with dynamic environments

AbstractAmerican designers are designing more flexibility into flexible manufacturing systems (FMSs). However, there has been little research on the scheduling of these more flexible systems. This study adds insights into how these systems should be scheduled and increases our understanding of the influences of routing flexibility.A few American FMSs use off‐line schemes for scheduling but most use real‐time schemes. This research compares seven real‐time and two off‐line scheduling schemes for a random flexible manufacturing system. The results indicate that full advantage can be taken of these more flexible systems if off‐line schemes are used. The scheme that takes the greatest advantage of increased flexibility is an off‐line scheme that decomposes the scheduling problem. As flexibility increases this scheme is able to schedule more output and reduce the average flow time of parts, and it accomplishes this with little additional computational effort.

A loading and dispatching problem in a random flexible manufacturing system

A scheduling problem in a flexible manufacturing system (FMS) is considered to be a composite of two interdependent tasks: loading and sequencing. Formulations are presented for the loading problem with two objectives: (i) minimization of the system workload unbalance, and (ii) minimization of system unbalance and the number of late jobs; including constraints such as the number of tools slots with duplications, unique job routing, nonsplitting of jobs and machine capacity. For both the objectives, heuristic methods are developed and performance is compared with the exact mixed integer programming solutions. A simulation model is developed for investigating the system performance for the problem of minimizing the system unbalance using heuristic and sequential loading methods in conjunction with four—FIFO, SPT, LPT and MOPR—dispatching rules.