A Dendrite Net based decoupled framework for the reliability-based control co-design problem

To improve the reliability of the dynamic system including physical and control design, the reliability-based control co-design (RB-CCD) problem has been studied to account for the uncertainty stemming from the random physical design. However, when encountering RB-CCD in the sophisticated system in which the dynamic model simulation is time-consuming or the state equation is expressed implicitly, the available RB-CCD methods will consume significant computational effort to perform numerous system simulations for the reliability analysis and deterministic optimization. Therefore, this work proposes a Dendrite Net-based decoupled framework for RB-CCD to alleviate the computational burden. Specifically, the Dendrite (DD) model constructed by the suggested training scheme integrated with an adaptive sampling strategy is used to approximate the state equation in the dynamic system. After that, the sequential optimization and reliability assessment method decouples RB-CCD into the control co-design (CCD) problem and time-dependent reliability assessment problem, which are solved sequentially based on the cheap estimations of DD model, rather than the expensive simulations of the original system. Furthermore, two numerical examples and an engineering example of 3-DOF robot system are applied to demonstrate the feasibility and efficiency of the proposed framework.     

Scheduling dynamic block assembly in shipbuilding through hybrid simulation and spatial optimisation

Scheduling block assembly in shipyard production poses great difficulties regarding the accurate prediction of the required spatial resource and effective production control for achieving managerial objectives due to the dynamic spatial layout and the stochastic nature of the production system. In this study, this dynamic space-constrained problem is viewed as two sequential decisions, namely rule-based dispatching and a static spatial configuration. A novel hybrid planning method is developed to employ discrete-event simulation as look-ahead scheduling to evaluate the system performance under various control policies. To rationalise block placement and improve long-term area utilisation, a discrete spatial optimisation problem is formulated and solved using an enumeration-based search algorithm, followed by the application of a series of heuristic positioning strategies. By imitation of the dynamic dispatching and spatial operation, a statistical analysis of the resultant performance can be conducted to select the best-performing priority rules. A case study with an experimental investigation is performed for a local shipyard to demonstrate the applicability of the proposed method.     

基于层次模糊推理的施工机群动态调度方法研究

以Zadah的模糊数学理论为基础,结合决策融合技术提出了一种基于层次模糊推理的施工机群动态调度方法。与传统的单层模糊调度控制方法相比,该方法具有建模简单且可扩展性好的优点,同时避免了单层推理的维数灾难问题。将该方法应用于施工机群的动态调度系统中,按照生产情况对施工系统进行了建模仿真,仿真结果和调度实例证明了该方法的有效性和合理性。该方法为机群施工以及与之相似的生产施工的调度提供了技术框架。     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

Optimal variability sensitive condition-based maintenance with a Cox PH model

Condition based maintenance (CBM) is an important maintenance strategy in practice. In this paper, we propose a CBM method to effectively incorporate system health observations into maintenance decision making to minimise the total maintenance cost and cost variability. In this method, the system degradation process is described by a Cox PH model and the proposed framework includes simulation of failure process and maintenance policy optimisation using adaptive nested partition with sequential selection (ANP-SS) method, which can adaptively select or create the most promising region of candidates to improve the efficiency. Different from existing CBM strategies, the proposed method relaxes some restrictions on the system degradation model and taking the cost variation as one of the optimisation objectives. A real industry case study is used to demonstrate the effectiveness of our framework.     

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.     

A learning-based methodology for dynamic scheduling in distributed manufacturing systems

To enhance productivity in a distributed manufacturing system under hierarchical control, we develop a framework of dynamic scheduling scheme that explores routeing flexibility and handles uncertainties. We propose a learning-based methodology to extract scheduling knowledge for dispatching parts to machines. The proposed methodology includes three modules: discrete-event simulation, instance generation, and incremental induction. First, a sophisticated simulation module is developed to implement a dynamic scheduling scheme, to generate training examples, and to evaluate the methodology. Second, the search for training examples (good schedules) is successfully fulfilled by the genetic algorithm. Finally, we propose a tolerance-based learning algorithm that does not only acquire general scheduling rules from the training examples, but also adapts to any newly observed examples and thus facilitates knowledge modification. The experimental results show that the dynamic scheduling scheme significantly outperforms the static scheduling scheme with a single dispatching rule in a distributed manufacturing system.     

Distributed production planning using a graph-based negotiation protocol

A distributed model for allocating a single job to be processed through multiple machines and alternative routings so that the job flow time is experimentally shown to reach its minimum is presented. Issues are addressed pertaining to dynamic and fully distributed decision-making for determining the job flow time in a cooperative manner between system agents (i.e. machines and a job), and for selecting a good routing (i.e. a short flow time) for a job when alternative routings are available. Two cases with confirmed jobs and without confirmed jobs are examined. The proposed protocols can deal with job shop scheduling with multiple product types in a sequential manner. The model is developed on the basis of a distributed shortest path algorithm using asynchronous job-initiated protocols. These protocols are examined using object-oriented simulation models and it is shown how the agents converge to the optimal path in a finite time.     

ROBOT SCHEDULING IN A CIRCUIT BOARD PRODUCTION LINE:A HYBRID OR/ANN APPROACH

A hybrid method that combines human intelligence, an optimization technique (semi-Markov decision model) and an artificial neural network to solve real-time scheduling problems is proposed. The proposed method consists of three phases: data collection, optimization, and generalization. The testbed of this approach is the robot scheduling problem in a circuit board production line where one overhead robot is used to transport jobs through a line of sequential chemical process tanks. Because chemical processes are involved in this production system, any mistiming or misplacing will result in defective jobs. The proposed hybrid system performs better than the human scheduler from whom the models were formulated, both in terms of productivity and quality.     

Formal design and analysis of a hybrid supervisory control structure for Virtual Production Systems

Virtual Production Systems (VPSs) is a dynamic paradigm for production resources structure, which was proposed to cope with changing and uncertain manufacturing environment. However, the high performance of VPSs relies on a high-quality control in practice. Motivated by both local quick responses and global optimization, a supervisory control structure based on autonomous and coordination mechanisms is proposed for VPSs in this paper. This hybrid supervisory control structure is formally designed and analysed with timed automata on the platform of an integrated tool named UPPAAL. First, the Discrete Events Dynamic Systems (DEDS) model of VPSs is established. Then autonomous and coordinated supervisory controllers are further designed under the guidance of heuristic scheduling rules, such that the desired characteristics of both performance (production flow and time) and activity (overflow-, conflict- and deadlock-free) can be ensured. Finally, system analysis, including deadlock-free analysis, calculation and simulation of time-optimal scheduling, is made through verification. A case study is made to illustrate that control and scheduling can be well integrated into this hybrid supervisory control structure in practice.     

Workshop scheduling using practical (inaccurate) data Part 3: A framework to integrate job releasing,routing and scheduling functions to create a robust predictive schedule

A comprehensive simulation study conducted by the authors investigated the robustness of a predictive scheduling system in a dynamic and stochastic environment. The results revealed that to improve the robustness of a scheduling system, besides using a robust scheduling method with a frequent rescheduling policy, the shop load should be well controlled and kept balanced. Integrating the planning and the scheduling functions has been shown to achieve this objective. This paper discusses the effects of the planning i.e. job releasing and routing and the scheduling functions in creating a robust schedule and a framework to integrate the above functions is proposed. This system consists of a planning module that is concerned with job releasing and routing decisions and a scheduling module that provides the detailed scheduling. A mathematical model using the integer programming technique is use to demonstrate a solution for the planning module. In addition, a heuristic algorithm is used to solve the scheduling problem. It is shown that, in terms of shop load balance level and job delivery time, the proposed system performs better than a benchmark loading strategy on the basis of minimum processing cost.     

Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining

A new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.     

Agile manufacturing system control based on cell re-configuration

The control of an agile manufacturing system (AMS) is expected to be flexible, open, scalable and re-configurable so as to tackle the more complex and uncertain information flows. To meet these requirements, we propose agent-based control architecture for AMS, under which the functions of task planning, scheduling and dynamic control are integrated seamlessly. First of all, this paper introduces the concept of RMC (re-configurable manufacturing cell), based on which, we construct the control architecture for AMS in compliance with multi-agent system (MAS). The whole control process under the architecture comprises two hierarchies, i.e. the upper one for order planning and RMC forming and the lower one for task scheduling within each RMC. For the upper hierarchy, we establish a linear integer programming (LIP)-based mathematical model and a MAS-based dynamic process model, and present a two-step approach to order planning and RMC forming. For the lower hierarchy, we develop the scheduling model, a method based on the bidding mechanism from contract net, and describe the rescheduling mechanism in the control system. To illustrate the methodology proposed in the paper, a simulation study is thoroughly discussed. Our studies demonstrate that the RMC-based control architecture provides an AMS with an optimal, dynamic and flexible mechanism of responding to an unpredictable manufacturing environment, which is crucial to achieve agility for the whole manufacturing system.     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

Optimisation of block erection scheduling based on a Petri net and discrete PSO

The shipyard block erection system (SBES) is a typical discrete-event dynamic system. To model multiprocessing paths and a concurrent assembly procedure, a timed Petri net (TPN) is proposed. The definition of a Petri net is extended to accord with the real-world SBES organisation. The basic TPN modules are presented to model the corresponding variable structures in the SBES, and then the scheduling model of the whole SBES is easily constructed. A modified discrete particle swarm optimisation (PSO) based on the reachability analysis of Petri nets is developed for scheduling of the SBES. In the proposed algorithm, particles are coded by welding transitions and selecting places of the TPN model, and then the collaboration and competition of particle individuals is simulated by crossover and mutation operators in a genetic algorithm. Numerical simulation suggests that the proposed TPN–PSO scheduler can provide an improvement over the conventional scheduling method. Finally, a case study of the optimisation of a back block erection process is provided to illustrate the effectiveness of the method.     

Optimal design and control of a reactive distillation system

This article presents the design and control of a reactive distillation system utilizing recent advances in mixed integer dynamic optimization. A high fidelity dynamic model is used to predict the behavior of the process under time-varying disturbances. Design and control decisions, involving both discrete and continuous variables, are simultaneously optimized leading to a more economically attractive and better controlled system than that obtained by following a sequential optimization approach. It is shown that the resulting design and control scheme can guarantee feasible operation under bounded uncertainty at a minimum total average cost, representing ～17% savings over the original design.     

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.     

An optimization based decision support system for integrated planning and scheduling on dedicated freight corridors

Empty wagon redistribution, train formation, routing and scheduling are complex problems for large railways, many of which currently have or are planning dedicated freight railway corridors (DFC). DFC operations due to their unique characteristics require research and new models for better operations planning. The rolling-stock, being expensive assets, need to be utilised in an optimal manner while meeting service quality levels. Motivated by Indian DFC, we present an integer programming formulation of the dynamic problem of empty distribution and train scheduling in DFC and discuss associated modelling issues. By unifying the separate problems into a single and dynamic model, we have developed a framework for more effective rolling stock utilisation. Based on this optimization model, an interactive decision support system is proposed for better decision-making on rolling-stock allocation and train scheduling. Extensive experiments and systematic analyses for a case of Indian DFC highlight the potentialities and effectiveness of the proposed DSS for DFC operations planning and management.     

A neural network decision-making model for job-shop scheduling

Scheduling in a job-shop system is a challenging task. Simulation modelling is a well-known approach for evaluating the scheduling plans of a job-shop system; however, it is costly and time-consuming, and developing a model and interpreting the results requires expertise. As an alternative, we have developed a neural network (NN) model focused on detailed scheduling that provides a versatile job-shop scheduling analysis framework for management to easily evaluate different possible scheduling scenarios based on internal or external constraints. A new approach is also proposed to enhance the quality of training data for better performance. Previous NN models in scheduling focus mainly on job sequencing and simple operations flow, and may not consider the complexities of real-world operations. The proposed model’s output proved statistically equivalent to the results of the simulation model. The study was accomplished using sensitivity analysis to measure the effectiveness of the input variables of the NN model and their impact on the output, revealing that the batch size variable had a significant impact on the scheduling results in comparison with other variables.     

Innovative design of active insulation system for in-situ fidelity coring in deep strata

The in-situ insulation of deep strata is the key technology of deep drilling and deep resource exploitation, but at present, this technology is still a difficult problem in the study of in-situ fidelity coring in deep strata. Taking into account the complex geological conditions of deep strata, the strong disturbance and high cost caused by large-scale mining activities limit the working space of deep drilling. At the same time, the fidelity cabin is too far from the ground surface during deep drilling operations, which will cause great difficulty in power supply, temperature control and data collection. For this reson, the function-behavior-structure (FBS) model was used to define and describe the function of the active insulation system for in-situ fidelity coring in deep strata, and the functional requirements and functional structure layout of the active insulation system were initially proposed. Combining the TRIZ, the technical conflicts in the preliminary design scheme of active insulation system were analyzed and improved. In addition, a high-efficiency active insulation system consisting heat pipes, Peltier cooler and graphene heating coating was proposed. By setting different temperature gradients and performing constant temperature control, the insulation simulation of in-situ temperature at different depths was realized, which verified the feasibility of the proposed active insulation system. The realization of the active insulation system for in-situ fidelity coring in deep strata can provide research ideas and methods for the later research of passive insulation and temperature pressure coupling.