Real-time OHT Dispatching Mechanism for the Interbay Automated Material Handling System with Shortcuts and Bypasses

As a key to improve the performance of the interbay automated material handling system (AMHS) in 300 mm semiconductor wafer fabrication system, the real-time overhead hoist transport (OHT) dispatching problem has received much attention. This problem is first formulated as a special form of assignment problem and it is proved that more than one solution will be obtained by Hungarian algorithm simultaneously. Through proposing and strictly proving two propositions related to the characteristics of these solutions, a modified Hungarian algorithm is designed to distinguish these solutions. Finally, a new real-time OHT dispatching method is carefully designed by implementing the solution obtained by the modified Hungarian algorithm. The experimental results of discrete event simulations show that, compared with conventional Hungarian algorithm dispatching method, the proposed dispatching method that chooses the solution with the maximum variance respectively reduces on average 4 s of the average waiting time and average lead time of wafer lots, and its performance is rather stable in multiple different scenarios of the interbay AMHS with different quantities of shortcuts. This research provides an efficient real-time OHT dispatching mechanism for the interbay AMHS with shortcuts and bypasses.     

Differentiated preemptive dispatching for automatic materials handling services in 300 mm semiconductor foundry

Highly automated materials handling in 300 mm semiconductor manufacturing is one of the biggest concerns to foundry practitioners because effective coordination among efficient manual operations has been the core competence in their 200 mm manufacturing successful stories. It is still very challenging to provide almost no-wait transport to hot lots (high priority lots) in an automatic materials handling production environment. This paper proposes an effective overhead hoist transport (OHT) dispatching rule, differentiated preemptive dispatching (DPD) policy, to reduce the possible blocking effects during the transportation of hot lots in a 300 mm OHT system. The dispatching objective is to minimize the delivery time of hot lots while minimizing the impact to the transport of normal lots. Simulation experiments based on realistic data from a local 300 mm foundry fab are conducted. Numerical results demonstrate that the DPD rule can effectively expedite the movements of hot lots. The proposed DPD rule is very useful to hot lots management and shop floor control functions, like scheduling and dispatching, in a 300 mm fab-wide automated production environment.     

A cross-entropy heuristic statistical modeling for determining total stochastic material handling time

This paper concerns with proposing a heuristic statistical technique to compute total stochastic material handling time in an automated guided vehicle (AGV) equipped jobshop manufacturing system. With respect to stochastic times of AGVs material handling process, the material handling activities probability distributions are considered. Using the probability distributions, we model the AGV material handling problem using a heuristic statistical method when the activities’ probability distribution functions are the same. Also, in the case that the activities’ probability distribution functions are different, a cross-entropy approach is proposed and developed to model the problem. The effectiveness of the proposed model is illustrated in a numerical example and verified by a simulation study. The numerical experiments are worked out in two cases, namely having same probability distributions and different probability distributions for activities. Both cases are verified by simulation study using different simulation softwares showing the efficiency of the employed approaches. In case 1, the deviation of ARENA from the proposed statistical model is estimated to be 0.5 % while for other softwares detailed in the experiments the value of deviations are more than 30 %. On the other hand, in case 2 again ARENA performed better than others having the deviation of 0.3 % from the proposed cross-entropy.     

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。     

基于深度学习算法的调度自动化云平台任务优化策略研究

分析了云平台任务调度的特点和目标,从任务调度算法入手,提出了基于改进粒子群算法的电力调度自动化系统的人工智能方法,开发了云计算操作的模型。基于该算法和物理模型的运行控制考虑了 QoS 要求和平台云居民的环境负载平衡,可以有效提高所提电力调度自动化系统的云平台任务调度的效率。以电力自动化云平台为分析对象,研究其架构,将修正的 PSO 算法与云资源调度模型的结构拓扑相结合,建立三级数据节点,给出了基于改进 PSO 的云平台调度模型,旨在提高云计算资源配置效率,改善云服务质量,解决电力调度自动化系统的任务调度问题。     

CMS scheduling problem considering material handling and routing flexibility

Cell manufacturing as an application of group technology increases the flexibility and efficiency of the production. Cell scheduling problem, one of the subjects in cell manufacturing, has not been widely studied by researchers compared with other problems in cell manufacturing. In spite of great importance of material handling in cell scheduling, it has not been paid enough attention by researches. In this paper, a new mathematical model for cell scheduling problem considering material handling time and routing flexibility is proposed. The proposed model belongs to the mixed-integer nonlinear programs (MINLP). A linearization procedure is proposed to convert the MINLP to an integer program (IP) in order to develop more powerful optimization tools. Furthermore, a simulated annealing-based heuristic is developed to solve the large-size problems.     

AGV schedule integrated with production in flexible manufacturing systems

Flexible manufacturing systems (FMS) comprise, automated machine tools, automated material handling, and automated storage and automated retrieval systems (AS/RS) as essential components. Effective sequencing and scheduling of the material handling systems (MHS) can have a major impact on the productivity of the manufacturing system. The material handling cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines, material handling systems and computer. In this context, this paper attempts to link the operation of automated guided vehicles (AGV) with the production schedule and suggests a heuristic algorithm that employs vehicle dispatching rules (vdr) for conflict resolution. The vdrs considered in this paper are: shortest operation time (SPT), longest operation time (LPT), longest travel time (LTT) and shortest travel time (STT). The performance of the vdrs in the proposed heuristic is compared with makespan criteria. The results show that the STT provides the best solutions compared to other vdrs.     

A constructive heuristic for the integrated scheduling of machines and multiple-load material handling equipment in job shops

This paper extends the traditional job shop scheduling problem (JSP) by incorporating the routing and scheduling decisions of the material handling equipment. It provides a generic definition and a mixed integer linear programming model for the problem considering the case of heterogeneous multiple-load material handling equipment. A constructive heuristic is developed for solving the problem. This heuristic is based on the well-known Giffler and Thompson’s algorithm for the JSP with modifications that account for the routing decisions of the material handling equipment and their effect on the start times of the manufacturing operations. Different dispatching rules are integrated into the heuristic, and experiments are conducted to study their effect on the makespan along with the determination of the computational time requirements of the developed heuristic.     

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.     

Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach

This paper studies a flexible job shop problem considering dynamic events such as stochastic job arrivals, uncertain processing times, and unexpected machine breakdowns. Also, the considered job shop problem has routing flexibility and process flexibility. A multi-agent scheduling system has been developed for solution with good quality and robustness. A pheromone-based approach is proposed for coordination among agents. The proposed multi-agent approach is compared with five dispatching rules from literature via simulation experiments to statistical analysis. The simulation experiments are performed under various experimental settings such as shop utilization level, due date tightness, breakdown level, and mean time to repair. The results show that the proposed agent-based approach performs well under all problem settings.     

Modularized simulation for lot delivery time forecast in automatic material handling systems of 300 mm semiconductor manufacturing

The more accurate the forecast is to lot delivery time, the more effective it is in fab scheduling. In fab operations, scheduling is the major impact factor of tools capacity allocation, tools utilization control and bottleneck management. However, there is no effective method to estimate delivery time in 300 mm automatic material handling systems (AMHS) operation. Computer simulations are authentic, but they are either too complex to model fab operations as well as the whole AMHS, or too time-consuming to simulate with a full-scaled fab model. This paper proposes an analytic methodology to estimate the loop-to-loop delivery time for differentiated lots in a 300 mm AMHS environment. Combining simulation and statistics techniques, we develop a modularized simulation method (MSM) for delivery time forecast of priority lots. Numerical experiments based on data from a local 300 mm manufacturing fab are conducted. Simulation demonstrates that the MSM has credible results in estimating lot delivery times. The time differences between MSM and simulation for both priority lots and regular lots are 0.2 s and 0.1 s, respectively. Using the MSM method to forecast AMHS delivery time is a great contribution for streamlining shop floor operations, such as scheduling and dispatching, for eliminating time delays in the 300 mm automatic environment.     

基于遗传算法的模具异地制造的优化调度

研究了模具异地制造的调度问题,建立了模具异地制造调度的最小模型,利用遗传退火算法实现了调度决策的优化,并提出了将遗传退火算法和分派规则相结合的调度策略和动态编码的方法,可提高搜索效率和速度,保证其全局收敛性。经调度实例验证表明,该算法能够提供了较为合理的调度方案。     

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.     

Performance evaluation of priority dispatching rules in multi-level assembly job shops with jobs having weights for flowtime and tardiness

The problem of scheduling in dynamic shops is an important operational problem in view of its complexity and significance in terms of associated costs of scheduling. While a number of research studies have investigated the problem of scheduling in flow shops and job shops, only some attempts have been done to study the problem of scheduling in assembly job shops that manufacture multi-level jobs. The problem of scheduling in dynamic assembly job shops with jobs having weights for holding and tardiness of jobs deserves due attention. In this study an attempt has been made to propose new priority dispatching rules that minimize the performance measures related to weighted flowtime and weighted tardiness of jobs. The existing unweighted dispatching rules have been modified in view of the consideration of weights for flowtime and tardiness of jobs. The performances of the (modified) existing dispatching rules and the proposed dispatching rules are compared through exhaustive simulation experiments with the consideration of a number of different experimental settings involving due-date setting, utilization levels and types of job structures. The proposed dispatching rules are found to perform better than the existing ones in most experimental settings and with respect to a number of measures of performance.     

Bottleneck-based heuristic dispatching rule for optimizing mixed TDD/IDD performance in various factories

Most research on scheduling problems focuses on increasing production efficiency. For instance, the shortest processing time (SPT) and earliest due date (EDD) dispatching rules perform well in minimizing mean flow time and reducing maximum tardiness, respectively. However, those indices ignore the financial impact (material cost and order price) on the factory. Previous studies focused mainly on cycle time and due date. However, the theory of constraint (TOC) considers not only the effect of time, but also financial factors. Therefore, TOC addresses the concepts of throughput-dollar-day (TDD) and inventory-dollar-day (IDD). The former index (TDD) represents penalties for tardy deliveries, while the latter index (IDD) refers to the material holding cost. Based on these two indices, this investigation creates a novel mixed TDD/IDD weighted value (Z _value) to replace the other traditional indices for taking measurements in various factories. This study also designs a heuristic dynamic scheduling algorithm (mixed TDD/IDD dispatching rule) for reducing the system Z _value. Some traditional dispatching rules are compared with the proposed rule in terms of TDD, IDD, and Z _value. Analytical results indicate that the mixed TDD/IDD dispatching rule is feasible and generally outperforms other conventional dispatching rules in terms of Z _value under various factories.     

机器人制造单元的建模与任务调度策略

机器人资源的合理分配和调度是提高制造单元柔性的关键。本文针对一类机器人制造单元的最小周期调度问题，应用极大代数方法建立了单元系统的调度模型，提出了基于禁忌搜索的启发式调度策略，并给出了初始可行解和搜索邻域的构造方法；最后，通过具体的运算实例，验证了所提出方法具有较高的效率，能够解决较大规模的最小周期调度问题，具有广泛的适用性。     

A rolling horizon job shop rescheduling strategy in the dynamic environment

In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.     

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.     

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。     

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.