Experimental investigation of flexible manufacturing system scheduling decision rules

This paper reports the results of an experimental investigation of scheduling decision rules for a dedicated flexible manufacturing system. A simulation model of an existing flexible manufacturing system (FMS) comprised of 16 computer numerical controlled machines (CNC) was constructed using actual operation routings and machining times to evaluate the performance of various part loading and routing procedures. The results indicate that FMS performance is significantly affected by the choice of heuristic parts scheduling rules.     

Multi-criteria optimisation-based dynamic scheduling for controlling FMS

This study deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Most studies that address this issue use some sort of adaptive scheduling that enables the FMS to cope with the randomness and variability efficiently. The methods presented in the literature are usually based on heuristics and use simple dispatching rules. They do not consider changing the decision criteria dynamically as the system conditions change. In contrast to previous studies, the present study focuses on developing a control mechanism for dynamic scheduling that is based on incremental optimisation. This means that each time a scheduling decision is made, the local optimisation problem is solved such that the next jobs to be processed on machines are selected. The objective function (dominant decision criterion) for this optimisation problem is selected dynamically based on production order requirements, actual shop-floor status and system priorities. The proposed multi-criteria optimisation-based dynamic scheduling methodology was evaluated and compared with some known scheduling rules/policies. The results obtained demonstrate the superiority of the suggested methodology as well as its capability to cope with a multi-criteria environment.     

Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion

Although a significant amount of research has been carried out in the scheduling of flexible manufacturing systems (FMSs), it has generally been focused on developing intelligent scheduling systems. Most of these systems use simple scheduling rules as a part of their decision process. While these scheduling rules have been investigated extensively for a job shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rules are tested under a variety of experimental conditions by using an FMS simulation model.     

A comparison of tool management strategies and part selection rules for a flexible manufacturing system

An important element in the successful operation of flexible manufacturing systems (FMS) is the management of the tooling component. This paper reports on one aspect of tool management for FMS operations. Four tool allocation and scheduling strategies are compared in the presence of three part selection rules through a simulation study of a five-machine FMS with an automated tool handling system. The tool allocation strategies are similar to those used in industry while the part selection rules are synthesized from the literature on FMS scheduling under tooling constraints. The use of different tooling strategies produces significantly different outcomes in FMS performance.     

基于规则对象Petri网的柔性制造系统建模

针对FMS建模中存在的问题，通过分析FMS的组成和活动特点，提出了规则对象Petri网的建模方法。该方法集中了Petri网和面向对象技术的优点，并融系统决策规则子模型中。利用规则对象Petri网，建立了FMS的资源对象模型、动态行为模型和调度决策模型，为设计、分析和调度控制提供了有效的工具。     

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.     

Operating an FMC by a decision-tree-based adaptive production control system

In previous works decision tree mechanisms were suggested for real-time production control of flexible manufacturing systems operating in a constant mix environment. The current paper tests the performance of a decision-treebased adaptive production control system operating in a flexible manufacturing cell in more random environments. A decision-tree-based production control system, which periodically chooses, on the basis of the current state of the system, the most appropriate scheduling rule, out of several predetermined ones, is presented. The performance of the decision-tree-based production control system was tested by simulating a flexible manufacturing cell operating in two different operational environments, distinct by the variability of their demand. The decision tree-based-production control system has performed rather well in the produce-to-order environment. However, it does not outperform the alternative scheduling rules in a highly random produce-to-order environment.     

A study of scheduling rules of flexible manufacturing systems: A simulation approach

This study examines the effects of scheduling rules on the performance of flexible manufacturing systems (FMSs). Several machine and AGV scheduling rules are tested against the mean flowtime criterion. In general, scheduling rules are widely used in practice ranging from direct applications as a stand-alone scheduling scheme to indirect application as a part of complicated scheduling systems. In this paper, we compare the rules under various experimental conditions by using an FMS simulation model. Our objective is to measure sensitivity of the rules to changes in processing time distributions, various levels of breakdown rates, and types of AGV priority schemes. A comprehensive bibliography is also presented in the paper.     

Development of a real-time multi-objective scheduler for a semiconductor fabrication system

Semiconductor wafer fabrication involves possibly one of the most complex manufacturing processes ever used. This causes a number of decision problems. A successful system control strategy would assign appropriate decision rules for decision variables. Therefore, the goal of this study is to develop a scheduler for the selection of decision rules for decision variables in order to obtain the desired performance measures given by a user at the end of a certain production interval. In this proposed methodology, a system control strategy based on a simulation technique and a competitive neural network is suggested. A simulation experiment was conducted to collect the data containing the relationship between the change of decision rule set and current system status and the performance measures in the dynamic nature of semiconductor manufacturing fabrication. Then, a competitive neural network was applied to obtain the scheduling knowledge from the collected data. The results of the study indicate that applying this methodology to obtaining a control strategy is an effective method considering the complexity of semiconductor wafer fabrication systems.     

Dynamic production control with dispatching within pre-emption

This paper deals with a production control problem for the testing and rework cell in a dynamic and stochastic computer integrated manufacturing (CIM) system. This research first defines dispatching within pre-emption for an extended form of pre-emption. A dynamic controller called competitive decision selector (CDS) is then modified and extended as CDSplus to handle three different production control decisions; dispatching, pre-emption, and dispatching within pre-emption. It observes the status of the system and jobs at every decision point, and makes its three different decisions in real time. The CDSplus dynamic control shows better performance than static control rules with respect to the number of tardy jobs.     

Fuzzy inference-based multiple criteria FMS scheduling

This paper proposes a fuzzy inference-based scheduling decision for flexible manufacturing systems (FMS) with multiple objectives. The objectives have different and dynamic preference levels. It is inferred that the changes in the production environment may be sensed by environmental variables. The detected changes are input in a fuzzy inference mechanism, which outputs the current preference levels of all objectives. A multiple criteria scheduling decision is then made, using the partitioned combination of the preference levels. An example of application is presented. Simulation results show very good performance for the proposed system.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

Integration of inductive learning and neural networks for multi-objective FMS scheduling

In this paper, we propose an integrated approach of inductive learning and competitive neural networks for developing multi-objective flexible manufacturing system (FMS) schedulers. Simulation and competitive neural networks are applied sequentially to extract a set of classified training data which is used to create a compact set of scheduling rules through inductive learning. The FMS scheduler can assist the operator to make decisions in real time, while satisfying multiple objectives desired by the operator. A simulation-based experiment is performed to evaluate the performance of the resulting scheduler.     

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     

Design of an FMS decision support system

Flexible manufacturing system (FMS) is an integrated system consisting of computer numerical control machine tools and automated material handling system controlled by an overall control computer system. The FMS design is broken down into strategic, tactical and operational level designs. This research is concerned with the development of a decision support system for the design of FMS (FMSDDS. Simulation is used as a primary analysis tool, making the DSS capable of solving problems at all three levels. The decision making ability is provided by a multi-attribute utility model which accounts for both quantitative and qualitative factors that affect a decision. The FMSDSS is implemented using three routines: input, analysis, and output. The input routine helps the user to select feasible FMS configurations interactively. The analysis routine consists of a generalized FMS simulation model. This routine analyses the FMS alternatives selected and provides the information required for the output analysis. The qualitative and quantitative measures of performance that have to be considered for decision making are handled by the output routine. It also combines performance measures of different units such as time and cost. The DSS is modular and hence future enhancements can be readily added.     

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.     

Virtual reality in requirement analysis for CIM system development suitable for SMEs

This paper presents a methodology, based on Virtual Reality (VR), for representing a manufacturing system in order to help with the requirement analysis (RA) in CIM system development, suitable for SMEs. The methodology can reduce the costs and the time involved at this stage by producing precise and accurate plans, specification requirements, and a design for CIM information systems. These are essentials for small and medium scale manufacturing enterprises. Virtual Reality is computer-based and has better visualization effects for representing manufacturing systems than any other graphical user interface, and this helps users to collect information and decision needs quickly and correctly. A VR-RA tool is designed and developed as a software system to realize the features outlined in each phase of the methodology. A set of rules and a knowledge base is appended to the methodology to remove any inconsistency that could arise between the material and the information flows during the requirement analysis. A novel environment for matching the physical and the information model domains is suggested to delineate the requirements.     

An experimental study of human decision-making in computer-based scheduling of flexible manufacturing system

This paper describes a study which explores human decision-making abilities in scheduling and dispatching of a flexible manufacturing system (FMS) An experiment is described, using an FMS, in which subjects make scheduling and dispatching decisions using a real-time interactive computer-simulation based system. The experimental results demonstrate that human decision-making is superior to general dispatching rules. An explanation of these results and an analysis of subjects' behaviour is presented in the light of information obtained from verbal protocol data     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

Situation dependent decision selector for production control in testing and rework cell

This paper presents a production control approach for the testing and rework cell in a dynamic and stochastic computer integrated manufacturing (CIM) system. The proposed situation dependent decision selector (SDDS) observes the status of the system and jobs at every decision point, and makes its three different decisions on dispatching, pre-emption, and dispatching within pre-emption in real time. This paper also develops a new feature selection algorithm for the SDDS. Experimental results show that the SDDS is better than other static control rules and another dynamic control approach with respect to the number of tardy jobs.