Evaluation of routing flexibility of a flexible manufacturing system using simulation modelling and analysis

Routing flexibility is a major contributor of the flexibility of a flexible manufacturing system (FMS). The present paper focuses on the evaluation of the routing flexibility of an FMS with the dynamic arrival of part types for processing in the system. A typical FMS configuration is chosen for detailed study and analysis. The system is set at five different levels of routing flexibility. Operations of part types can be processed on alternative machines depending upon the level of routing flexibility present in the system. Two cases have been considered with respect to the processing times of operations on alternative machines. A discrete-event simulation model has been developed to describe the operation of the chosen FMS. The performance of the system under various levels of routing flexibility is analyzed using measures such as mean flow time, mean tardiness, percentage of tardy parts, mean utilisation of machines, mean utilisation of automatic-guided vehicles, and mean queue length at machines. The routing flexibility for producing individual part types has been evaluated in terms of measures such as routing efficiency, routing versatility, routing variety and routing flexibility. The routing flexibility of the system has been evaluated using these measures. The flexibility levels are ranked based on the routing flexibility measure for the system. The ranking thus obtained has been validated with that derived using fuzzy logic approach.     

Effects of routing flexibility, sequencing flexibility and scheduling decision rules on the performance of a flexible manufacturing system

This paper focuses on a simulation-based experimental study of the interaction among routing flexibility, sequencing flexibility and part sequencing rules in a typical flexible manufacturing system (FMS). Two scenarios are considered for experimentation. Three routing flexibility levels, five sequencing flexibility levels and four scheduling rules for part sequencing decision are considered for detailed investigation. The performance of the FMS is evaluated using various measures related to flow time and tardiness of parts. The simulation results are subjected to statistical analysis. The analysis of results reveals that deterioration in system performance can be minimized substantially by incorporating either routing flexibility or sequencing flexibility or both. However, the benefits of either of these flexibilities diminish at higher flexibility levels. Part sequencing rules such as earliest due date and earliest operation due date provide better performance for all the measures at higher flexibility levels.     

Real-Time Manipulation of Alternative Routeings in Flexible Manufacturing Systems: A Simulation Study

This paper presents the results of a simulation study of a typical flexible manufacturing system (FMS) that has routeing flexibility. The objective is this study is to test the effectiveness of the dissimilarity maximisation method (DMM) for real-time FMS scheduling. DMM is an alternative process plan selection method developed for routeing selection in off-line FMS sched-uling. An integrated framework that consists of a computer simulation model, which mimics a physical system, a C++ module, and a linear program solver is used to evaluate the effects of various operational control rules on the system performance. The hypothetical FMS employed in this study consists of seven machining centres, a loading and an unloading area, and six different part types. Owing to the existence of identical machining centres in the system, the part types have alternative routeings. For selecting an incoming part and later routeing it to a machining centre for its next operation, three control rules, namely, first-in first-out/first available (FIFO/FA), equal probability loading (EPL), and dissimilarity maximisation method/first-in first-out (DMM/ FIFO) are used. In this study, DMM is 1. Used as a real-time decision-making tool to select routeings for the parts that are in the system. 2. Tested and benchmarked against FIFO/FA and EPL. The results show that DMM/FIFO outperforms FIFO/FA and EPL on system throughput. Other measures such as average waiting time, average transportation time, and percentage utilisation rates are also investigated to provide insights for the effectiveness of the DMM rule for real-time FMS control applications.     

Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.     

Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm

Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Both the scheduling of operations on machine centers as well as the scheduling of AGVs are essential factors contributing to the efficiency of the overall flexible manufacturing system (FMS). An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs an integral part of the overall scheduling activity. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a non-traditional optimization technique called the adaptive genetic algorithm (AGA). The problem considered here is a large variety problem (16 machines and 43 parts) and combined objective function (minimizing penalty cost and minimizing machine idle time). If the parts and AGVs are properly scheduled, then the idle time of the machining center can be minimized; as such, their utilization can be maximized. Minimizing the penalty cost for not meeting the delivery date is also considered in this work. Two contradictory objectives are to be achieved simultaneously by scheduling parts and AGVs using the adaptive genetic algorithm. The results are compared to those obtained by conventional genetic algorithm.     

Dynamic Scheduling for a Flexible Manufacturing System - The Pre-emptive Approach

This paper presents a simulation model of a flexible manufacturing system (FMS) which minimises three performance criteria simultaneously, i.e. mean flow-time, mean tardiness, and mean earliness. The dispatching rule will be changed at a frequency that is varied by the quantity of output produced by the system. Therefore, the dynamic system is an event-trigger rather than triggered by changing the rule at regular time intervals, which is passive. Three indices are used to represent the three criteria being monitored, and the indices are ranked in descending order. The larger the index, the worse is the condition of the criterion in the system. An appropriate rule will be selected for the next operation in order to tackle that criterion with the largest index. This mechanism is called the pre-emptive method. Furthermore, the indices can be biased by the decision maker so that a particular criterion can have larger weighting. Results show that a solution (range of frequency) can always be obtained for changing the dispatching rule so that the system is better than one which just uses fixed FMS scheduling rules.     

Scheduling decisions in FMS using a heuristic approach

The paper deals with the multilevel scheduling decisions of a Flexible Manufacturing System (FMS) to generate realistic schedules for the efficient operation of the FMS. The primary concern of an Operations Management System (OMS) for a FMS is production scheduling, Material Handling System (MHS) scheduling, Automated Storage/Retrieval System (AS/RS) operation and control and tool management. Scheduling is a critical issue and determines how efficiently the production resources are utilised and how the selected parts are affected in the system. In this paper, the integrated scheduling of FMS, namely, the production scheduling conforming with the MHS scheduling, is addressed. An enumerative heuristic is used, namely Giffler and Thompson, which is an evolutionary combining a Genetic Algorithm (GA) and a stochastic neighborhood search technique using a Simulated Annealing (SA) algorithm is employed.A. Noorul Haq received his PhD in Manufacturing Management from the Indian Institute of Technology (IIT), New Delhi, India, a Master of Engineering degree from Madras University, and a Bachelor of Engineering degree from Annamalai University, India. He is currently an assistant professor in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests include aggregate production planning, facility layout and scheduling and optimisation techniques.T. Karthikeyan received his Master's degree from Bharathidhasan University. Currently, he is a research scholar in the Department of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His research interests includes facility layout, FMS and simulation.M. Dinesh received his bachelor's degree in engineering from Bharathiar University, India and is currently working on his master's degee in engineering in the Department Of Production Engineering, Regional Engineering College, Tiruchirappalli 620015, India. His current research interests are in optimisation techniques, facility layout and scheduling.     

PSO算法在FMS刀具可复用调度中的应用与仿真

刀具调度在现代FMS生产中越来越频繁,在前期刀具可复用策略研究的基础上,将粒子群算法(PSO)嵌入可复用策略的具体刀具调度中。算法设计上采用了四维变量编码法、整数圆整解码法、以及浮动目标函数迭代法,将刀具复杂的多指标十进制迭代过程灵活的加以实现。对该PSO算法以实例进行了仿真,结果证实了刀具的利用率有所提高、系统加工完成时间缩短,从而证明了带PSO的可复用调度策略应用于刀具调度带来的经济性。     

FMS调度与控制的统一建模与集成设计

分析了FMS调度与控制统一建模与集成设计的必要性，从FMS运行过程的IDEF0功能建模入手，建立了基于模块化彩色赋时Petri网（CTPN）的FMS调度与控制的统一模型。该模型不仅可描述FMS的调度问题，而且能表达FMS的运行控制逻辑与仿真，从而为FMS控制系统的集成设计奠定了基础。为简化FMS控制系统的设计，提出了虚拟工作站的概念与方法，将FMS控制器的部分控制功能下放到低层的工作站控制层。基于上述CTPN统一模型，开发了FMS调度与控制的集成设计原理和方法。     

Integrated management of a bishuttle FMS using a discrete/ stochastic simulator

This paper provides a demonstration of the culmination of research which has been undertaken by the authors for a period of nearly 20 years. Specifically, the work presented here has been carried out in collaboration with Mandelli SpA, one of the leaders in FMS production in Italy. The paper ooutlines the complete use of an off-line FMS simulator originally realized in Fortran 77 and implemented on an IBM PS/2-80. The FMS simulation examines the system's efficiency both prior to and following improvements made in scheduling and managing mannedand unmanned working shifts, resulting in great improvements in system utilization.     

Development and evaluation of feature-focused dynamic routing policy

This paper reports on a development of feature-focused dynamic routing policy and its evaluation in a flexible manufacturing systems (FMS) simulation framework. The dynamic policy is based on an integrative methodology in which process planning system IMPlanner is integrated with FMS simulation module. IMPlanner’s rule-based system process selection system performs knowledge-intensive task of generating alternative processing options for each feature for parts in production plan. Generated alternative routings (process plan network) for each part are utilized in the FMS simulation module such that routing decision in FMS are made on periodic intervals by considering alternative processes for each feature and making decision based on the current system status and performance. The proposed framework has been evaluated in an experimental FMS simulation module, implemented in Arena, in which two performance criteria, machine utilization and WIP, were used to make routing decisions. The feature-focused approach is compared with traditional static decision-making, and its improved performance is demonstrated.     

Heuristical job allocation in a flexible manufacturing system

Two heuristic algorithms are presented for solving the scheduling problem in a statically loaded Flexible Manufacturing System (FMS). The heuristics goal is to minimise the total cost resulting from the tardiness of jobs. Using the same heuristics, an iterative method is proposed to find an optimal makespan and the average lead time. Modifications required to handle the case of a dynamically loaded FMS are then presented. Simulation results show that the developed heuristics appear to out perform the other published techniques used in obtaining the schedules associated with minimum makespan, minimum average lead time and minimum cost of tardiness. Finally, the simulation of the dynamic case shows that the algorithms could be implemented locally on each station for the scheduling calculation.     

A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS

A carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system. Most of the researchers have addressed machine and vehicle scheduling as two independent problems and most of the research has been emphasized only on single objective optimization. Multiobjective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms. In this paper the authors made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives.     

Development of a computer simulator for dynamic scheduling of FMS to achieve optimal performance

This paper deals with the development of a simulation software using the features of visual interactive computer graphics for analysing the dynamic scheduling of a flexible manufacturing system (FMS) to cater to the need of fluctuating markets. While carrying out this work, the main concepts of dynamic modelling, computer simulation and visual interactive graphics have been used for studying the reduction in backlogs of workorders. In addition, queue length, waiting time and the capacity utilisation of the manufacturing system are also considered. The arrival pattern of components is fitted to a suitable distribution pattern based on the workorder position. The effects of despatching rules such as SPT, LPT, EDD, SIO, SLACK, SLACK/LRO on scheduling are analysed using the simulator to imitate the real life operation of FMS. Visual interactive computer graphics enables the user to find out machine utilisation levels, queue length of the components, throughput and comparison between the application of various despatching rules. Since dynamic queue records are maintained, it is possible to establish the various components waiting in the respective work centres and their attributes like lateness, slack, remaining operations etc. This software is very useful in enabling the user to select the appropriate despatching rule for the system. The software has been developed in the C language for IBM/PC compatible systems.     

Simulation study of an AGV system in an FMS environment

A simulation program written in ECSL to simulate an AGV system serving an FMS is presented. It incorporates a special algorithm to enable ‘intelligent’ routing of the AGVs with minimum control requirements. The program was used to examine several parameters and scheduling/control disciplines affecting the AGV system's performance for a specific FMS. Results show that selection of an appropriate combination of parameter values can have a beneficial effect on the FMS.     

Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

Stochastic dynamic job shop scheduling problem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90 % and setup times less than processing times. Results indicate that shortest setup time (SIMSET) rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date (JMEDD) rule provides the best performance for makespan, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.     

Adaptive production control system for a flexible manufacturing cell using support vector machine-based approach

Real-time adaptive production control in the flexible manufacturing cell (FMC) is a complex issue that needs to be addressed to realize good performance and high productivity. In this paper, we have considered a support vector machine (SVM)-based simulation approach to resolve a production control problem in an FMC that operates in a dynamic environment. A SVM-based simulation approach chooses the most relevant scheduling rule out of several predefined ones on the basis of the current states of the system. This paper examines and compares the performance of the SVM-based simulation approach with the competent scheduling rules under two different operational environments which are characterized by the uncertainty of demand. We have also developed a Visual Basic-based simulation approach for scheduling of component parts in the context of FMC under different situations. The SVM methodology to control the production offers better performance than the single-rule-based production control system.     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

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.     

Dynamic load control policies for a flexible manufacturing system with stochastic processing rates

Real-time scheduling and load controls of FMSs are complex processes in which the control logic must consider a broad spectrum of instantaneous state variables while taking into account the probabilistic future impact of each decision at each time epoch. These processes are particularly important in the management of modern FMS environment, since they are known to have a significant impact on the FMS productive capacity and economic viability. In this article we outline the approach developed for dynamic load controls within an FMS producing a variety of glass lenses. Two revenue-influencing objective functions are evaluated for this capital-intensive facility. It is shown that by using Semi-Markovian modeling concepts, the FMS states need to be observed only at certain decision epochs. The mean holding time in each state is then obtained using the probability distribution function of the conditional state occupancy times. Several key performance measures are then derived by means of the value equations. In addition, the structure of the optimal policies are exemplified for a variety of operational parameters. It is shown that the optimal policies tend to generate higher buffer stocks of parts in those work centers having the highest revenue-generation rates. These buffer stocks get smaller and smaller as the relative processing capacity of the centers increases. Similar observations lead us to the introduction of several promising heuristics that capture the structural properties of the optimal policies with a significantly smaller computational effort. Results of the empirical evaluation of these heuristics are also analyzed here.