Knowledge-based scheduling in flexible manufacturing systems: An integration of pattern-directed inference and heuristic search

This paper presents a knowledge-based scheduling approach based on the problem-solving techniques developed in artificial intelligence. The approach is based on three key techniques. The first is the pattern-directed inference technique to capture the dynamic nature of the scheduling environment. The second is the non-linear planning technique to coordinate manufacturing processes and resource assignments. The third technique is the A? search algorithm to expedite the searching procedure. It models the scheduling process by state-space transitions; the job routing is obtained through selecting a sequence of scheduling operators guided by heuristics. Keeping track of the manufacturing system by a symbolic world model, this approach is adaptive to such environmental changes as new job arrivals and machine breakdowns, suitable for making real-time scheduling decisions.     

Toward Knowledge-Based Control Of Flexible Manufacturing Systems

This paper examines the use of artificial intelligence (AI) concepts to augment/enhance flexible manufacturing system (FMS) control systems by providing flexibility in decision-making to manage and control the system. Such a system would use a “knowledge-based” approach to system control. The paper first defines the problems and issues in FMS planning and control and then examines the AI technology that can be used to solve them. The paper shows that the FMS control structure is conceptually similar to an Al-based opportunistic hierarchical planning architecture. The paper concludes by discussing the prospects for knowledge-based control of FMSs in view of current technology.     

FMS Scheduling Simulation Based on an Evolution Algorithm

A FMS（flexible manufacturing system) scheduling algorithm based on an evolution algorithm (EA) is developed by intensively analyzing and researching the scheduling method in this paper.Many factors related to FMS scheduling are considered sufficiently.New explanations for a common kind of the encoding model are given.The rationality of encoding model is ensured by designing a set of new encoding methods,while the simulation experiment is performed.The results show that a FMS scheduling optimum problem with multi-constraint conditions can be effectively solved by a FMS scheduling simulation model based on EA.Comparing this method with others,this algorithm has the advantage of good stability and quick convergence.     

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.     

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.     

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.     

Production planning for flexible flow systems with limited machine flexibility

A flexible manufacturing system (FMS) is highly capital-intensive and FMS users are concerned with achieving high system utilization. The production planning function for setting up an FMS prior to production should be developed in order to make the most of the potential benefits of FMSs. We consider two production planning problems of grouping and loading a flexible flow system, which is an important subset of FMSs where the routing of parts is unidirectional. We show that considering this routing restriction as well as limited machine flexibility strongly affects both the solution techniques and the quality of the solutions. Because of the complexity of the problem, we present a heuristic approach that decomposes the original problem into three interrelated subproblems. We show that the proposed approach usually finds a near-optimum solution and is superior to an approach that exists in the literature of FMS production planning. We also introduce effective heuristic methods for two new subproblems that arise because of the unidirectional flow precedence and flexibility constraints. Computational results are reported and future research issues are discussed.     

Methods for activating the decision-making process that is used for controlling flexible manufacturing systems

This paper deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Shnits et al. (Shnits, B., Rubinovitz, J., and Sinreich, D., 2004. Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system. International Journal of Production Research, 42 (17), 3457–3472.) and Shnits and Sinreich (Shnits, B. and Sinreich, D., 2006. Controlling flexible manufacturing systems based on a dynamic selection of an appropriate operational criteria and scheduling policy. International Journal of Flexible Manufacturing Systems, 18 (1), 1–27.) developed a multi-criteria dynamic scheduling mechanism for controlling an FMS that can cope with such environments. An important part of this mechanism functioning, which impinges directly on its performance, is the activation of its decision-making process. This study continues the research presented in the above-mentioned papers and proposes different triggering methods for activating the control system decision-making process. The operational conditions for each suggested triggering method were analysed and a comparative analysis between these methods was performed. It was revealed that the highly dynamic triggering method, which activates the decision-making process right before a resource becomes available, outperformed the triggering methods that use a predefined scheduling period.     

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.     

A bottleneck-based beam search for job scheduling in a flexible manufacturing system

In this paper, we study job-scheduling methods for flexible manufacturing systems (FMSs). Routeing flexibility is a feature that distinguishes FMS scheduling from a classic general jobshop problem. We formalize the problem as a flexible jobshop problem and introduce a flexibility index to measure the extent of routeing flexibility. Based on a procedure to identify a potential bottleneck machine, we develop a beam search method for approximately solving the problem. The proposed method yields a significantly shorter makespan than that of the commonly used shortest processing-time dispatching rule, and it properly exploits the added flexibility in routeing. The computational effort required also is small enough to enable practical implementation.     

A New Method of Global Path Planning for AGV

Path planning is important in the research of a mobile robot （MR）. Methods for it have been used in different applications. An automated guided vehicle（ AGV） , which is a kind of MR, is used in a flexible manufacturing system （FMS）. Path planning for it is essential to improve the efficiency of FMS. A new method was proposed with known obstacle space FMS in this paper. FMS is described by the Augmented Pos Matrix of a Machine （ APMM ） and Relative Pos Matrix of Machines （ RPMM）, which is smaller. The optimum path can be obtained according to the probability of the path and the maximal probability path. The suggested algorithm of path planning was good performance through simulation result： simplicity, saving time and reliability.     

Scheduling of flexible manufacturing systems based on Petri nets and hybrid heuristic search

This paper proposes and evaluates a hybrid search strategy and its application to flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model multiple lot sizes for each job, the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for FMS scheduling, this paper presents a hybrid heuristic search strategy, which combines the heuristic A* strategy with the DF strategy based on the execution of the Petri nets. The search scheme can invoke quicker termination conditions, and the quality of the search result is controllable. To demonstrate this, the scheduling results are derived and evaluated through a simple FMS with multiple lot sizes for each job. The algorithm is also applied to a set of randomly generated more complex FMSs with such characteristics as limited buffer sizes, multiple resources, and alternative routings.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Scheduling of machines and automated guided vehicles in FMS using differential evolution

This paper addresses the problem of simultaneous scheduling of machines and two identical automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). For solving this problem, a new meta-heuristic differential evolution (DE) algorithm is proposed. The problem consists of two interrelated problems, scheduling of machines and scheduling of AGVs. A simultaneous scheduling of these, in order to minimise the makespan will result in a FMS being able to complete all the jobs assigned to it at the earliest time possible, thus saving resources. An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs as an integral part of the overall scheduling activity. The algorithm is tested by using problems generated by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers and analysed.     

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.     

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.     

Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system

A methodology is presented for the dynamic scheduling of flexible manufacturing systems (FMSs). A two-level control hierarchy is suggested. The higher level is used for determining a dominant decision criterion and relevant scheduling rules, based on an analysis of the actual shop status. The lower level uses simulation for determining the best scheduling policy to be selected. Simulation is used to evaluate different control options, and once a control decision is made, it is operated in real time to serve as the FMS controller. The suggested scheduling and control scheme is being developed, implemented and tested in a physical computer integrated manufacturing (CIM)/FMS environment at the CIM and Robotics Lab of the Faculty of Industrial Engineering and Management, Technion. This will serve as a test-bed to study the performance of the FMS under different scheduling rules and control options, and to recommend the best combination of control policies and parameters for specific system conditions and global production objectives.     

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.     

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     

Simulation modelling and analysis of scheduling in robotic flexible assembly cells using Taguchi method

Due to increasing competition in the developing global economy, today’s companies are facing greater challenges than ever to employ flexible manufacturing systems (FMS) capable of dealing with unexpected events and meeting customers’ requirements. One such system is robotic flexible assembly cells (RFACs). There has been relatively little work on the scheduling of RFACs, even though overall scheduling problems of FMS have attracted significant attention. This paper presents Taguchi optimisation method in conjunction with simulation modelling in a new application for dynamic scheduling problems in RFACs, in order to minimise total tardiness and number of tardy jobs (N_T). This is the first study to address these particular problems. In this study, Taguchi method has been used to reduce the minimum number of experiments required for scheduling RFACs. These experiments are based on an L9 orthogonal array with each trial implemented under different levels of scheduling factors. Four factors are considered simultaneously: sequencing rule, dispatching rule, cell utilisation and due date tightness. The experimental results are analysed using an analysis of mean to find the best combination of scheduling factors and an analysis of variance to determine the most significant factors that influence the system’s performance. The resulting analysis shows that this proposed methodology enhances the system’s scheduling policy.