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.     

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.     

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.     

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。     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

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.     

Dynamic co-ordinated scheduling in the supply chain under a process modernisation

A new dynamic model for co-ordinated scheduling of interlinked processes in a supply chain under a process modernisation is presented. Such a problem is vital in many of the supply chain management domains. This problem is represented as a special case of the scheduling problem with dynamically distributed jobs. The peculiarity of the proposed approach is the dynamic interpretation of scheduling based on a natural dynamic decomposition of the problem and its solution with the help of a modified form of continuous maximum principle blended with combinatorial optimisation. The special properties of the developed model allow using methods of discrete optimisation for the schedule calculation. Optimality and sufficiency conditions as well as structural properties of the model are investigated. Advantages and limitations of the proposed approach are discussed. With the developed approach, an explicit inclusion of a process modernisation in the SC co-ordinated decisions for a wide ranges of possible applications as well as a dynamic model and a tractable algorithm for optimal discrete time scheduling on the basis of continuous maximum principle have been obtained.     

A competitive neural network approach to multi-objective FMS scheduling

The main contribution of this paper is the development of a multi-objective FMS scheduler which is designed to maximally satisfy the desired values of multiple objectives set by the operator. For each production interval, a decision rule for each decision variable is chosen by the FMS scheduler. A competitive neural network is applied to present fast but good decision rules to the operator. A unique feature of the FMS scheduler is that the competitive neural network generates the next decision rules based on the current decision rules, system status and performance measures. A commercial FMS is simulated to prove the effectiveness of the FMS scheduler. The result shows that the FMS scheduler can successfully satisfy multiple objectives.     

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.     

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.     

Dynamic job shop scheduling using variable neighbourhood search

In this paper a scheduling method based on variable neighbourhood search (VNS) is introduced to address a dynamic job shop scheduling problem that considers random job arrivals and machine breakdowns. To deal with the dynamic nature of the problem, an event-driven policy is selected. To enhance the efficiency and effectiveness of the scheduling method, an artificial neural network with a back propagation error learning algorithm is used to update parameters of the VNS at any rescheduling point according to the problem condition. The proposed method is compared with some common dispatching rules that have been widely used in the literature for the dynamic job shop scheduling problem. Results illustrate the high efficiency and effectiveness of the proposed method in a variety of shop floor conditions.     

A real-time scheduling mechanism for a flexible manufacturing system: Using simulation and dispatching rules

This paper presents a real-time scheduling methodology which uses simulation and dispatching rules for flexible manufacturing systems. We develop a scheduling mechanism in which job dispatching rules vary dynamically based on information from discrete event simulation that is used for evaluating candidate dispatching rules. In this paper, we improve and extend a previous research on simulation-based real-time scheduling by suggesting a more systematic framework for the scheduling mechanism through refinement of functions of modules in the mechanism, and by presenting and analysing various scheduling strategies used to operate the mechanism. The strategies are formed by combining two factors that might influence the performance of the mechanism: type of simulation model which is used in the mechanism and points of time when new dispatching rules are selected. In order to compare performance of the scheduling strategies, computational experiments are performed and results are reported.     

An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Scheduling in a flexible manufacturing system (FMS)must take into account the shorter lead-time, the multiprocessing environment, the flexibility of machine tools, and the dynamically changing states. The scheduling approach described in this paper employs a knowledge-based system to carry out the nonlinear planning method developed in artificial intelligence. The state-space process for plan-generation, by either forward- or backward-chaining, can handle scheduling requirements unique to the FMS environment. A prototype of this scheduling system has been implemented on a LISP machine and is applied to solve the scheduling problem in flexible manufacturing cells. This scheduling method is characterized by its knowledge-based organization, symbolic representation, state-space inferencing, and its ability for dynamic scheduling and plan revision. It provides a foundation for integrating intelligent planning, scheduling, and machine learning in FMSs.     

Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

Dynamic selection of scheduling rules during real operations has been recognized as a promising approach to the scheduling of the production line. For this strategy to work effectively, sufficient knowledge is required to enable prediction of which rule is the best to use under the current line status. In this paper, a new learning algorithm for acquiring such knowledge is proposed. In this algorithm, a binary decision tree is automatically generated using empirical data obtained by iterative production line simulations, and it decides in real time which rule to be used at decision points during the actual production operations. The configuration of the developed dynamic scheduling system and the learning algorithm are described in detail. Simulation results on its application to the dispatching problem are discussed with regard to its scheduling performance and learning capability.     

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.     

Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions

In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.     

考虑有限物流运输能力的智能车间AGV调度算法研究

以大批量生产模式智能车间为背景,针对单环布局下多自动导航小车 （AGV） 的调度问题进行研究。首先,对典型智能车间特点进行了分析,利用Plant Simulation仿真平台建立了基于实际生产场景的仿真模型;其次,提出了基于软时间窗的AGV调度规则和基于备选调度规则集的6个运行机制;最后,通过设计仿真实验对上述7个调度规则进行了分析和比较。实验结果表明,基于软时间窗AGV调度方法总体上表现更好。这为企业配置和调度AGV小车提供了决策支持。     

Dispatching rules in scheduling Dispatching rules in scheduling: a fuzzy approach

Job-shop scheduling through simulation uses various kinds of dispatching rules such as SPT or the slack time rule. Each of these rules aims at satisfying a single criterion although workshop management is a multi-criteria problem. This paper proposes a way to use fuzzy logic in order to build aggregated rules allowing to obtain a compromise between the satisfaction of several criteria. When the criteria of performance change with the evolution of the production environment, these aggregated rules can be parametrized in order to modify the respective influence of the elementary rules they are composed of.     

An integrated methodology for automating the determination of layout and materials handling system

There is a severe scanty of models and solution techniques for the determination of layout and the materials handling system when neither are fixed. This is a complex problem for which this paper proposes a new integrated methodology using a knowledge-based/optimisation approach to the problem. The knowledge-base consists of facts and rules to determine the feasibility of using a materials handling equipment type for a given move. The optimisation part determines the layout of machines minimising the materials handling costs and the dead space in the layout using a multi-criteria optimisation model. The methodology aims to minimise materials handling costs, aisle space usage and dead-space in the resulting layout. It is particularly applicable to heavy manufacturing environments. The system outputs the optimum location, configuration and orientation of machines, and the material handling equipment types, their design capacities, utilisations and the assignment of moves to each item of materials handling equipment. The results of a successful application to an example problem are given.     

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.