A mixed dispatching rule approach in FMS scheduling

Short-term scheduling in flexible manufacturing systems (FMSs) is a difficult problem because of the complexities and dynamic behavior of FMSs. To solve this problem, a dispatching rule approach is widely used. In this approach, however, a single dispatching rule is usually assigned for all machines in a system during a given scheduling interval. In this paper, a mixed dispatching rule which can assign a different dispatching rule for each machine is proposed. A search algorithm which selects an appropriate mixed dispatching rule using predictions based on discrete event simulation is developed for this approach. The search algorithm for the mixed dispatching rule is described in detail. The effectiveness (in meeting performance criteria) of the mixed dispatching rule and the efficiency of the search algorithm relative to exhaustive search (complete enumeration) is demonstrated on an FMS model. The mixed dispatching rule approach performs up to 15.9% better than the conventional approach, and is 4% better on average. The statistical significance of the results is dicussed.     

An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell

To overcome deficiency in the global capacity of a single dispatching rule, it is vital to select a dispatching rule in real time for dynamic scheduling. Among the studies addressing the method for selecting dispatching rules, few have no requirements for domain knowledge or accurate training example, which is hard to acquire from the real production system. In this paper, a new learning algorithm, along with the presentation of an adaptive scheduling control policy, is proposed to obtain the dynamic scheduling knowledge effectively, and different dispatching rules are selected to schedule the jobs in the machine buffer according to the current transient state of the system. Case studies are given to illustrate the validity of the scheduling control policy.     

基于强化学习的模式驱动调度系统研究

目前,还没有一种调度规则能够根据系统环境状态的改变来进行自适应调整.对此,提出一种基于智能体的模式驱动调度系统,由智能体和仿真环境两个主要部分构成.其中,智能体将利用强化学习(Q学习算法)进行训练,以提高其动态选择合适调度规则的能力.仿真结果表明,这种模式驱动调度系统能够很好地根据系统环境状态的改变选择出对应的最优调度规则,且其调度性能优于单一调度规则,适合于系统环境不断变化的动态调度.     

Selection of dispatching rules in FMS: ANP model based on BOCR with choquet integral

Scheduling policy which depends on selection of dispatching rules is one of the reasons which affect FMS’ productivity. Selection of a dispatching rule can only be based on one or two criteria such as processing time, due date, or system components at traditional methods such as mathematical programming, simulation, and heuristic algorithms. However, traditional methods have some weakness; the first one is about selection criteria based on only one dispatching rule or a few combination of the rules, the second one is related with not considering most of the manufacturing system information, and the last one is to deal with selection decision is not a dynamic structure. In this study, an analytical network process (ANP) model has been developed in order to eradicate these weaknesses. The model based on multiple criteria decision-making process. In addition, most of manufacturing system information have been employed, and it consists of a dynamic decision-making process. The proposed model has a comprehensive structure which consists of a company’s manufacturing criteria and macro-strategies. Although interdependence is accepted among criteria in ANP, non-interaction is assumed among them (i.e., additive measure). Because of existence of interactivities among criteria, a choquet integral has been adapted in an ANP model.     

A study on the weighted composite dispatching rule in modular production systems

Scheduling semi-conductor manufacturing process systems is a complicated and difficult job, due to such characteristics as re-entry into manufacturing processes, high uncertainty of processes, and products and technologies changing rapidly. The researchers have carried out many studies to find efficient techniques for semi-conductor manufacturing systems with a view to accomplishing the goals of systems, such as saving cycling time and increasing production quantity per unit time. The production flow in the semi-conductor industry has the most unique characteristics, which makes it difficult to plan production and to schedule semi-conductor manufacturing. Currently, the scheduling methods in semi-conductor assembly processes follow the dispatching rule on a simple first come first serve (FCFS) basis, and a backlog is operated as a buffer based on the daily production quantity. In this study, therefore, we will apply various dispatching rules on a real-time basis and verify the effect and result of exact scheduling through simulation, based on the assumption that competitive advantages in production come from efficient inventory control and exact scheduling.     

Data-based scheduling framework and adaptive dispatching rule of complex manufacturing systems

Based on the analysis of the differences and relations between traditional and data-based scheduling methods for complex manufacturing systems, a data-based scheduling framework was proposed and discussed for its implementation into a semiconductor manufacturing system. The state-of-the-art research on the key technologies of data-based scheduling was then introduced together with their development trends. By taking a real wafer fabrication facility (fab) as an example, an adaptive dispatching rule (ADR) was developed. Firstly, a simulation system for the fab was developed, and study samples were generated by simulation. Then, the relations between the parameters of ADR and real-time running state of the fab were obtained by learning with an integration of a binary regression model, backward propagation neuro-network, and particle swarm optimization algorithm from these study samples to realize the adaptive regulations of these parameters of ADR. Finally, ADR was integrated with the simulation system. The simulation results showed that ADR had a positive effect on the operational performance of the fab. Its “move” performance was increased by 2.41 and 7.24 % for the cases of 70 % and 90 % workload, respectively.     

Makespan minimization for m-machine permutation flowshop scheduling problem with learning considerations

Increased complexity of current manufacturing systems together with dynamic conditions and permanent demands for flexible and robust functionality makes their management and control very difficult and challenging. Workflow simulation is an effective approach to investigate dynamic workflow scheduling policies and evaluate the overall manufacturing system performance. The results attained in simulation model can give directions on how to maximize system output when selecting an appropriate scheduling practice for a real system. In this paper, we investigate the abilities of multi-agent systems in combination with dynamic dispatching rules and failure handling mechanisms to manage dynamic environment conditions (such as machine failures) for systems in the production automation domain. We measure system robustness by systematically assessing the total system performance (e.g., number of finished products) in a number of representative test cases. We use an agent-based simulation environment, MAST, which has been validated with real-world hardware to strengthen the external validity of the simulation results. We investigated the performance of a re-scheduling component which uses four different policies that define how to adjust the system schedule in case of machine disturbances/failures. In the context of the empirical study the Complete Rerouting re-scheduling policy outperformed all other policies.     

Design and analysis of a rule-based knowledge system supporting intelligent dispatching and its application in the TFT-LCD industry

As flexibility and agility become key success factors of a competitive manufacturing enterprise, the ability to support the short term decision making of manufacturing planning, scheduling, and dispatching becomes a critical issue. This research presents a rule-based knowledge system run on the Java Expert System Shell (JESS) platform to addresses how engineering knowledge can be dynamically represented and efficiently utilized in job dispatching. The knowledge system, called Intelligent Dispatching Decision Support System (IDDSS), is designed and implemented using the rule-based inference and reasoning approach. The distinctive technical contributions of IDDSS focus on three critically integrated elements: (1) a visualized rule editor, (2) a knowledge object data gateway, and (3) an embedded application component. Furthermore, a case study of the thin-film transistor liquid-crystal display (TFT-LCD) panel repair line is applied to demonstrate the rule-based knowledge system for agile TFT-LCD repair job dispatching.     

Efficient dispatching rules for dynamic job shop scheduling

This study attempts to provide efficient dispatching rules for dynamic job shop scheduling by combining different dispatching rules. A dispatching rule is used to select the next job to be processed from a set of jobs awaiting service. A job shop will be treated as dynamic, when conditions such as continuously arriving new jobs and deviations from current schedule need to be accommodated, and a job shop should be treated as an integrated part of a manufacturing system. The discussion includes a simulation technique which uses ARENA 4.0. software to simulate the dynamic model of a job shop under various rules and performance measures . Results of the simulation show that, for most of the performance measures, combined rules perform well. In this study, the combined rules MWKR_FIFO and TWKR_SPT do well under most conditions.     

板材FMS的混合调度方法及应用

提出静态调度和动态调度相结合的板材FMS混合调度方法。采用整数规划建立FMS静态调度模型;分析了板材FMS动态调度中的决策节点,以基于调度规则的系统仿真完成FMS动态调度。应用表明,该方法具有实用价值。     

Intelligent scheduling of automated machining systems

An automated machining system involves concurrent use of manufacturing resources, alternative process plans and flexible routings. High investment in the installation of automated facilities requires an efficient scheduling system that is able to allocate the resources specified for operations over a scheduling horizon. The primary emphasis of this paper is to generate schedules that accurately reflect details of the automated environment and the objectives stated for the system. In this paper, a rule for dispatching operations, named the Most Dissimilar Resources (MDR) dispatching rule, is introduced. A scheduling algorithm for automated machining is presented. Using the previous simulation research for this topic, a rule-based scheduling system is constructed. An architecture for an intelligent scheduling system is proposed, and the system has a high potential to provide efficient schedules based on the task-specific knowledge for the dynamic scheduling environment.     

Dynamic dispatching for interbay material handling by using modified Hungarian algorithm and fuzzy-logic-based control

Semiconductor Wafer Fabrication System (SWFS) is one of the most complicate discrete processing systems in the world. As the wafer size grows from 200 to 300 mm and then to 450 mm in recent years, the interbay automated material handling system (AMHS) has been widely adopted. How to improve the overall efficiency of interbay material handling has therefore become a critical and urgent problem to be solved. However, the large-scale, dynamic and stochastic production environment significantly substantiates the complexity of the scheduling problem. Aiming to meet the demands of adaptive adjusting, efficient scheduling and multiple-objective optimization, a dynamic dispatching method based on modified Hungarian algorithm is proposed. The system parameters, including cassette due date, cassette waiting time, and system load are simultaneously considered, and furthermore the multi-parameters’ weight coefficients are adjusted dynamically by using the fuzzy-logic-based control. Discrete event simulation models are constructed with the eM-Plant software to evaluate the proposed approach. Experimental results show that the proposed dynamic dispatching method improves the system efficiency in terms of mean delivery time, mean waiting time and so on. Meanwhile, the proposed dispatching method has a better comprehensive performance such as the robustness compared to conventional vehicle dispatching approaches.     

Comparing the effect of different strategies of continuous improvement programmes on repair time to reduce lead time

To cope with uncertainty in semiconductor wafer fabrication facilities (fabs), scheduling methods are required to produce quick real-time responses. They should be well tuned to track the changes of a production environment to obtain good operational performance. This paper presents an efficient adaptive dispatching method (ADM) with parameters determined dynamically by real-time state information of fabs. ADM is composed of a dispatching rule considering both batch and non-batch processing machines to obtain improved fab-wide performance, several feature selection methods to determine key scheduling-related real-time state information, and a linear regression model to find the relations between the weighting parameters of the dispatching rule and the determined real-time state information. A real fab simulation model is used to demonstrate the proposed method. The simulation results show that ADM is adaptive to changing environment with better performance than a number of commonly used rules (such as FIFO, EDD, CR, LPT, LS, SRPT, and SPT) and an adaptive dispatching rule that considers only real-time ratio of hot jobs to the number of all jobs in a fab and the ratio of jobs with one third of photo steps left to the number of all jobs.     

基于遗传算法的多资源作业车间智能优化调度

提出一种基于遗传算法的调度算法，用于解决作业车间的加工受到机床、操作工人和机器人等多种生产资源制约条件下的优化调度。以生产周期为目标进行的优化调度，将遗传算法和分派规则相结合，通过交叉、交异等遗传操作，得到目标的最优或次优解。最后对算法进行了仿真研究，并给出了算法运行结果，仿真结果表明该算法是可行的。     

Dispatching of overhead hoist vehicles in a fab intrabay using a multimission-oriented controller

The semiconductor manufacturing system has become one of the most important manufacturing systems in recent years. Intrabay automation for wafer transport is in high demand in a 300 mm foundry fab. The control of the transport system plays an important roles in manufacturing efficiency and the satisfaction of production strategy. In general, efficiency is the most important issue that has appeared in most research of automated transport systems. However, how to dynamically adjust the transport policy to meet the production strategy and to increase the transport efficiency as well seems to be more crucial to modern semiconductor manufacturing systems. In this paper, a fuzzy-logic-based multimission-oriented overhead vehicle dispatcher is developed. The vehicle dispatching rule is assigned in real-time according to the statistics of the manufacturing performance. In this manner, the dispatching rules can be adjusted according to possible high-risk lots so that most of the production strategies can be satisfied. If all of the lots meet the prescribed production strategies, then the nearest-job-first rule can be used to offer higher transport efficiency. In addition, the proposed vehicle dispatcher is also capable of deadlock-free operation, collision avoidance, and blockage prevention. Finally, this paper uses AutoMod simulation software to construct and evaluate the manufacturing models. By evaluating the simulation results, the proposed intelligent multimission-oriented vehicle dispatcher performs better performance than the others, and all of the production strategies are satisfied .     

Multicriteria dynamic scheduling by swapping of dispatching rules

For most shop floors, consideration of more than one criterion would be likely to provide more realistic scheduling of a given set of jobs. The present paper considers this aspect of scheduling and uses an algorithm proposed by the authors in their previous work for implementing several criteria simultaneously in a shop of dynamic nature. The algorithm considers several dispatching rules simultaneously for selecting a job for processing and continuously monitors the attained values of performance measures. The selection of dispatching rules is made by identifying the worst performing criterion. A rule that can improve system performance for the worst-performing criterion is selected to dispatch the part under consideration. In this paper, several case studies have been attempted to evaluate the efficiency of the algorithm. The results of the taken case studies indicate that in a dynamic system the system performance improves by changing the dispatching rules corresponding to the worst-performance criterion at the appropriate deterioration in the performance measures.     

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.     

Learning iterative dispatching rules for job shop scheduling with genetic programming

This study proposes a new type of dispatching rule for job shop scheduling problems. The novelty of these dispatching rules is that they can iteratively improve the schedules by utilising the information from completed schedules. While the quality of the schedule can be improved, the proposed iterative dispatching rules (IDRs) still maintain the easiness of implementation and low computational effort of the traditional dispatching rules. This feature makes them more attractive for large-scale manufacturing systems. A genetic programming (GP) method is developed in this paper to evolve IDRs for job shop scheduling problems. The results show that the proposed GP method is significantly better than the simple GP method for evolving composite dispatching rules. The evolved IDRs also show their superiority to the benchmark dispatching rules when tested on different problem instances with makespan and total weighted tardiness as the objectives. Different aspects of IDRs are also investigated and the insights from these analyses are used to enhance the performance of IDRs.     

A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study

Scheduling of a semiconductor wafer fabrication system (SWFS) is complicated due to its re-entrant product flow, high uncertainties in operations, and rapidly changing products and technologies; thus dispatching rules have been widely used for real-time scheduling because they can provide a very quick and pretty good solution. However, deciding how to select appropriate rules is very difficult and seldom tackled. This paper describes an approach into the evaluation and optimization of dispatching rules by integrating the simulation and response surface methodology (RSM). In order to implement the proposed approach, a dynamic bottleneck dispatching (DBD) policy is designed, in which bottlenecks are detected in a timely way and adaptive dispatching decisions are made according to the real-time conditions. In addition, two case studies are carried out to demonstrate the approach. One case compares DBD to regular rules, such as CR + FIFO, EDD, SRPT, SPT, SPNB and Justice, a bottleneck dispatching method. Simulation results show that the DBD policy is superior to the other six methods. In another case study, the parameters of DBD are optimized by RSM and desirability function, and the result proves that the optimized DBD method can get even better performance.     

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop 调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop 调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop 调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop 调度问题的优越性能。