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.     

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.     

Corrective maintenance through dynamic work allocation and pre-emption: case study and application

This case study develops an innovative management and scheduling system for corrective maintenance of machines in a manufacturing facility. The study also involves a comparative evaluation of the proposed and the existing systems under a spectrum of operating conditions. A comprehensive simulation is used to evaluate system performances under a variety of settings which include reliability, service level, and cost consequences. The analysis is based on a full factorial experimental design. In summary, the developed self-regulating management system which involves dynamic work allocation and pre-emption is shown to yield higher machine availability and higher mechanic utilisation even with fewer mechanics. The study also finds that the new system is more streamlined, agile, and robust although it is subject to more-constrained machine reliability and mechanic service time environments. Further, a major reduction of current manpower can still achieve at least 95% machine availability, illustrating the cost effectiveness and efficacy of the developed system. This rule-based corrective maintenance system can be operated in uncertain environments on a real-time basis without additional reformatting costs and provides a competitive measure to deal with managerial issues such as low retention rate for skilled mechanics, highly uneven training levels and pay scales. The financial consequences and gains in strategic advantage with respect to the facility's operational structure are promising after implementation. Moreover, the system developed in this case study represents a meaningful starting point for a more vigorous theoretical research on the bucket brigade system to different functions in industrial and operations management.     

Simulation-based optimisation of logistics distribution system for an assembly line with path constraints

The design of logistics distribution system for an assembly line with given layout is usually constrained by various factors such as the vehicles for the distribution of assembly components and the paths on the shop floor for the vehicle movement. Since design optimisation of these production systems with such constraints is hard to solve by mathematic approaches, simulation-based approach is mostly used. In this paper, simulation-based analysis and optimisation of a logistic distribution system is performed combined with a heuristic algorithm to obtain a solution that most suits the practical requirements of an assembly line. The proposed simulation-based method builds a model that satisfied various complicated real-world problems at the work site of an investigated factory. Meanwhile, a dynamic dispatching method for vehicle movement is also presented which can adjust the control strategy and decision parameters dynamically in the process of simulation and contributes significantly to form a more fruitful design scheme. Experimental results show that proposed method performs better than the analytical model-based approach and some other methods, and the dynamic vehicle dispatching method presented performs better than some existing common strategies used in literature.     

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.     

A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing

This research addresses a hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem. It optimises three performance criteria simultaneously, namely: cycle time, slack time, and throughput. A case study is adopted to illustrate the performance of applying the methodology. Thin film transistor-liquid crystal display (TFT-LCD) is a high-technology industry, with a growing market. The manufacturing process is complex. It involves multi-products, sequence-dependent set-ups, random breakdowns, and multiple-objectives, with bias-weighted optimisation problems. To determine appropriate dispatching strategies, under various system conditions, is a non-trivial challenge to control the complex systems. There has been little research on these problems aimed at solving them simultaneously. This paper presents an event-triggered dynamic dispatching system that combines artificial intelligence methods to archive optimum dispatching strategies under diverse shop-floor conditions. Results show this system to be superior to previous researches.     

Development of a hybrid negotiation scheme for multi-agent manufacturing systems

A hybrid inter-agent negotiation mechanism based on currency and a pre-emption control scheme is proposed to improve the performance of multi-agent manufacturing systems. The multi-agent system considered consists mainly of four types of agents: machine, clone, part and mediator. The machine agent controls the scheduling and the execution of a task. The clone agent aims to maximize the utilization rate by attracting relevant work to the machine. The part agent communicates with the machine agent or clone agent to acquire necessary production resources in order to get the required processing done, and the mediator agent contains the status of the part that will be processed by the subcontracting machine agent. The primary objective is to design decentralized control protocols for discrete part manufacturing systems to enhance the efficiency of the system and to allocate dynamically the resources to critical jobs based on the dynamic search tree. This research incorporates both the currency and the pre-emption schemes within a common framework. Currency functions are used to help the agents meet their individual objectives, whereas the pre-emption scheme is used to expedite the processing of parts based on their due dates. A dynamic search algorithm for the best route selection of different operations based on the job completion time is also proposed and it is implemented on a small manufacturing unit.     

Aligning workload control theory and practice: lot splitting and operation overlapping issues

This paper addresses the problem of lot splitting in the context of workload control (WLC). Past studies on WLC assumed that jobs released to the shop floor proceed through the different stages of processing without being split. However, in practice, large jobs are often split into smaller transfer sublots so that they can move more quickly and independently through the production process and allow operations overlapping relating to the same job. This paper assesses the performance of different lot splitting policies for job release and dispatching strategies under lot splitting. A new dispatching rule was designed to specifically take advantage of lot splitting and operations overlapping in the context of WLC. Discrete-event simulation is used to assess system performance in relation to the ability to provide shorter delivery times and on time deliveries. Results highlight the importance of releasing the sublots of the same job together and demonstrate that combining an effective lot splitting policy with an appropriate dispatching rule can enhance the performance of production systems.     

Simulation study of a bottleneck-based dispatching policy for a maintenance workforce

Maintenance is important for production operations and for continuous improvement. Appropriate dispatching of the maintenance workforce to quickly respond to equipment failures and carry out preventive services can improve system productivity. The first-come-first-served policy is typically used in many manufacturing industries. In this paper, we present a priority-based dispatching policy, a dynamic bottleneck policy, based on the analysis of real-time data. In such a policy, priority is assigned to the bottleneck machine after a fixed time period, and the maintenance worker will service the high-priority machine (i.e. bottleneck machine) first when multiple service requests are received. It is shown by extensive simulation experiments that this policy can lead to a greater improvement in system throughput compared with the first-come-first-served policy. To implement such a policy, the appropriate time period for data collection and the frequency for carrying out bottleneck analysis are investigated. In addition, a sensitivity study suggests that the results obtained are insensitive to machine downtime, efficiency, and reliability models.     

Integrating load-based order release and priority dispatching

Workload control (WLC) is a well-established production control concept for job shops that put primary emphasis on load-based order release. Recent advances in load-based order release research have led to an improved delivery performance at reduced shop floor workloads. But although order release is the primary focus of WLC research, it must be coupled with priority dispatching on the shop floor if order progress is to be regulated. Prior simulation research suggests that load-based order release methods should only be coupled with simple dispatching rules because other, more powerful rules can conflict with the functioning of the release method. Yet, recent empirical research suggests that powerful priority dispatching rules – such as due date-oriented dispatching rules – are in fact needed for a high level of delivery performance to be obtained in practice. This paper focuses on overcoming the conflict between order release and dispatching, so load-based order release can be combined with due date-oriented dispatching. Preliminary analysis reveals that part of the conflict is because existing due date-oriented dispatching rules overcompensate for schedule deviations that occur when orders are either released earlier or later than planned. Two alternative new dispatching rules based on an improved method of determining operation due dates are then developed to better account for schedule deviations and overcome the conflict with load-based order release. Further improvements in delivery performance are obtained, while the large workload reductions achieved by recently developed load-based order release methods are retained.     

自适应鲁棒H2控制及其在小型无人直升机航向控制中的应用

提出了一个新的鲁棒控制方法并将其应用到小型无人直升机的航向控制中.给出了直升机的非线性航向动力学模型并进行简化,然后线性化为具有仿射不确定性的线性模型.针对这个线性模型,基于线性矩阵不等式方法提出了具有自适应机制的鲁棒H2反馈控制器.这种设计方法可以降低固定增益控制器所固有的保守性,提高控制效果.数字仿真验证了设计方法的可行性.     

An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing

The on-line control and scheduling of flexible manufacturing systems has been a major interest in the production research area since these systems first appeared. In this paper, a scheduling algorithm is described which employs discrete simulation in combination with straightforward part dispatching rules in a dynamic fashion. The result is that, instead of scheduling being planned ahead of time and then being applied to a rapidly changing system, a dispatching rule is determined for each short period just before the implementation time occurs. In the long run, the algorithm combines various dispatching rules in response to the dynamic status of the system. The algorithm is described in detail. The efficacy of the algorithm is discussed and demonstrated on a prototype system.     

On-line dispatching rules for vehicle-based internal transport systems

On-line vehicle dispatching rules are widely used in many facilities such as warehouses and manufacturing facilities to control vehicles’ movements. Single-attribute dispatching rules, which dispatch vehicles based on only one parameter, are usually used. However, multi-attribute dispatching rules prove to be better in general. In this paper, we study the impact of reassigning moving vehicles on some good dispatching rules, both single- and multi-attribute, in the literature. Results suggest that reassigning moving-to-park vehicles has a significant positive effect on reducing the average load waiting time. We evaluate the dispatching rules’ performance using the experimental design of a real-life case study. The performance criteria are: minimizing the average load waiting time, keeping the maximum load waiting time as short as possible and utilizing better vehicles. The results show that the combined dispatching rules which integrates multi-attribute dispatching and vehicle reassignment yields the best performance overall.     

Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems

This paper studies the performance of static and dynamic scheduling approaches in vehicle-based internal transport (VBIT) systems and is one of the first to systematically investigate under which circumstances, which scheduling method helps in improving performance. In practice, usually myopic dispatching heuristics are used, often using look-ahead information. We argue more advanced scheduling methods can help, depending on circumstances. We introduce three basic scheduling approaches (insertion, combined and column generation) for the static problem. We then extend these to a dynamic, real-time setting with rolling horizons. We propose two further real-time scheduling approaches: dynamic assignment with and without look-ahead. The performances of the above five scheduling approaches are compared with two of the best performing look-ahead dispatching rules known from the literature. The performance of the various approaches depends on the facility layout and work distribution. However, column generation, the combined heuristic, and the assignment approach with look-ahead consistently outperform dispatching rules. Column generation can require substantial calculation time but delivers very good performance if sufficient look-ahead information is available. For large scale systems, the combined heuristic and the dynamic assignment approach with look ahead are recommended and have acceptable calculation times.     

Family-based dispatching: anticipating future jobs

Group Technology exploits similarities in product and process design to meet the diversity of customer demand in an economic way. In this paper we consider one of the implementations of this concept?–?family-based dispatching. Intrinsic to family-based dispatching is the grouping of similar types of products for joint processing. In this way the number of set-ups may be reduced. Consequently, lead-time performance of the shop can be improved. We extend existing rules for family-based dispatching by including data on upcoming job arrivals. Typically, this type of data resides in the minds of the operators, or is stored in a shop-floor control system. Its availability allows for (1) better estimates of the composition of a process batch for a family, (2) the consideration of families for which no products are available at the decision moment, and (3) the possibility to start set-ups in anticipation of future job arrivals. The potential of including forecast data in decision-making is demonstrated by an extensive simulation study of a single-machine shop. Results indicate the possibility of significant improvements of flow time performance.     

Improving the applicability of workload control (WLC): the influence of sequence-dependent set-up times on workload controlled job shops

Simulation has demonstrated that the workload control (WLC) concept can improve performance in job shops, but positive empirical results are scarce. A key reason for this is that the concept has not been developed to handle a number of practical considerations, including sequence-dependent set-up times. This paper investigates the influence of sequence-dependent set-up times on the performance of a workload-controlled job shop. It introduces new set-up-oriented dispatching rules and assesses the performance impact of controlled order release. Simulation results demonstrate that combining an effective WLC order release rule with an appropriate dispatching rule improves performance over use of a dispatching rule in isolation when set-up times are sequence dependent. The findings improve our understanding of how this key implementation challenge can be overcome. Future research should investigate whether the results hold if set-up time parameters are dynamic and set-up times are not evenly distributed across resources.     

复杂型面智能生产单元任务−刀具联合动态调度策略

以复杂型面智能生产单元为背景,针对多机器的任务?刀具联合动态调度问题展开研究。通过分析复杂型面智能生产单元的作业流程和特征,建立任务?刀具联合调度问题的数学模型。结合通过对问题进行过程分解的方式构建一种组合规则调度算法生产框架,并通过在框架中嵌入启发式规则的方式生成72种组合规则算法。设计大量的算例测试集,通过对比、分析72种算法在差异化环境配置下的仿真结果,对比不同系统指标下算法的表现情况,发现基于FNOP规则构建的算法在75%的场景中均能取得较优的求解质量。研究成果为车间生产管理人员制定任务?刀具调度策略提供一定指导作用。     

基于双模糊PID的药品包装贴标张力控制系统设计

目的 为提高药品包装的贴标精度,设计一种基于双模糊PID控制器的贴标机张力控制系统。方法 简要论述药品包装生产线结构及其工艺流程。针对标签纸带收卷过程具有时变性、非线性和动态干扰等特点,设计一种双模糊PID张力控制器。利用副模糊控制器实现主模糊控制器的变量论域系数整定,而主模糊控制器可实现PID控制器参数整定。通过实验验证所述控制系统的有效性。结果 实验结果表明,与PID控制器相比,双模糊PID控制可使系统超调量减小50%,系统调节时间减小65%;贴标位置准确、标签贴附平整。结论 所述控制系统具有较好的抗干扰性能,贴标效果良好,具有一定使用价值。     

Adaptive and dynamic process planning using neural networks

Although feature-based process planning plays a vital role in automating and integrating design and manufacturing for efficient production, its off-line properties prevent the shop floor controller from rapidly coping with dynamic shop floor status such as unexpected production errors and rush orders. This paper proposes a conceptual framework of the adaptive and dynamic process planning system that can rapidly and dynamically generate the needed process plans based on shop floor status. In particular, the generic schemes for constructing dynamic planning models are suggested. The dynamic planning models are constructed as neural network forms, and then embedded into each process feature in the process plan. The shop floor controller will execute them to determine machine, cutting tools, cutting parameters, tool paths and NC codes just before the associated process feature is machined. The dynamic nature of process planning enables the shop floor controller to increase flexibility and efficiency in unexpected situations.     

An investigation of cooperative dispatching for minimising mean flowtime in a finite-buffer-capacity dynamic flowshop

Scheduling in a dynamic flowshop that receives jobs at random and unforeseen points in time has traditionally been done by using dispatching rules. This study compares the performances of leading dispatching rules with a cooperative dispatching approach, for the objective of minimising mean flowtime in a flowshop, in which the buffers that hold in-process jobs between machines have finite capacities. Cooperative dispatching employs a consultative and consensus-seeking methodology for deciding which job to dispatch next on a machine. Computational experiments using randomly generated test problems for three different utilisation (congestion) levels are carried out for 5- and 10-machine flowshops, under a wide range of buffer capacities. The results highlight the sensitivity of some of the popular dispatching rules to buffer size. In contrast, cooperative dispatching emerges as a robust method that performs consistently well across the range of buffer sizes and machine utilisations tested. The reductions in mean flowtime obtained by cooperative dispatching, in comparison to the other dispatching rules, are particularly large in flowshops that operate with very tight buffer capacities and elevated levels of congestion