Synchronising operations on productive systems modelled by timed event graphs

This study considers the synchronisation of events modelled by graphs of timed event systems. The start and finish points of events are indicated by the firing of transitions in the system. Synchronising the firing of these transitions control events, which are activities within a manufacturing system. Synchronisation is achieved by controlling the inflow of raw materials into the system. A feedback controller is proposed to control the flow of incoming feedstock. The controller parameters are calculated. Delays are applied to the input supply and the production rate is established when the control of the synchronisation constraints is satisfied. The proposed method ensures that the system control operates according to the control strategy of ‘just-in-time’ production planning.     

Service-based manufacturing systems: modelling and control

In service-based manufacturing systems, functionalities are independently developed as services and a central engine orchestrates their integration. As industrial processes tend to be very large, and performance and productivity are expected to be maximised, there is a constant interest in providing (in-advance) quality guarantees for services interactions, which contrasts with the usual non-automated workflow design. This paper provides an alternative to enhance service orchestration capabilities using supervisory control techniques. Initially, each component (atomic and composite activities) belonging to an orchestration language is modelled as a state-machine. Then, activity models are properly combined and composed, reproducing orchestrated workflows. Finally, supervisory control is used to calculate an optimal version of the orchestrator. Practical implications of handling large state-spaces are discussed and examples are provided.     

Minimising makespan in the two-stage assembly hybrid flow shop scheduling problem using artificial immune systems

Two-stage hybrid flow shop (HFS) scheduling problem followed by single assembly machine is addressed in this paper. To produce the final product, parts need to be processed on the HFS stages and thereafter, several parts are joined under the assembly operations based on the predefined Bill of Materials of the product. The aim of this research is to find the schedule which minimises completion time of the last product, i.e. makespan. For the considered problem, lower bound, heuristic algorithms and two metaheuristic techniques based on artificial immune system are developed. Computational results demonstrate that the proposed lower bound and heuristic algorithms outperform the existent lower bounds and heuristic algorithms.     

Constructing the closed-form solution of control-related states real-time information for insufficient k-th order systems with one non-sharing resource to realize dynamic modeling large TNCS systems of petri nets

Since the contribution from the closed-form solution (CFS) of the number of control-related states (CRSs) of the variant k-th order system can enumerate the number of each type of CRS in real time, we can apply this real-time information to enhance the capability for dynamic modeling of such systems. For example, allocating a non-sharing waiting dummy resource (known as a deadlock thread-holder [DTH]) in each forbidden substate at that location will increase the maximum number of reachable states that can be derived using a CFS in real time. We can thus avoid the occurrence of deadlock without implementing an additional controller. To extend this capability to model a k-th order system with one non-sharing circle subnet allocated at the top position of the left-hand process (denoted as a top non-sharing circle subnet [TNCS] k-th order system) by Petri net (PN), which is the fundamental manufacturing model for different products sharing common parts, this paper extends the existing research on CFS for PNs to an insufficient k-th order system, which is the essential element for a TNCS system with one non-sharing resource. The proposed deadlock avoidance algorithm can be employed to realize the allocation of dynamic non-sharing processes.     

Logical models of discrete event systems: A comparative exposition

The increasing complexity of man-made systems calls for new tools and techniques to model them efficiently and at the desired level of abstraction. Well-established modelling paradigms, such as finite state machines, petri nets, communicating sequential processes etc., which are borrowed from the fields of computer science and operations research, often lack certain essential features for capturing discrete event dynamics. New tools such as state charts, timed transition models, finitely recursive processes etc., are evolving to take into account some of these requirements. In this paper we first characterize such systems as well as typical problems related to them. We then discuss and critically evaluate several modelling frameworks through examples. At the end we provide a comparison among the frameworks and directions for future research.     

Production system redesign using realistic visualisation

The process of redesigning production systems is usually complex, for which virtual design tools are available. These tools are used to analyse and evaluate planned changes prior to implementation, making it possible to identify and prevent costly design mistakes. Despite this, design mistakes arise during and after the implementation. A source for design mistakes is incorrect or insufficient spatial data of the production systems used in the virtual design tools. The aim of this paper is to show how to reduce the time required for planning and implementing the redesign by supporting the process with realistic visualisation, created from accurate spatial data of the real production systems. Three industrial studies were carried out to evaluate how address realistic visualisation in order to support the redesign process. The result shows terrestrial 3D laser scanning to be suitable for capturing spatial data for realistic visualisation of production systems. The realistic visualisation can be used to virtually analyse design alternatives of the production systems, by, for example, combining the 3D laser scan data with 3D CAD models. The realistic visualisation enabling effective and accurate planning, which gives the opportunity to reduce the time required for planning and implementing redesigned production systems.     

Influence of maintenance policies on multi-stage manufacturing systems in dynamic conditions

Manufacturing systems are subject to a degradation process that leads to machine failure if no action is taken. Machine failures reduce the performance of the manufacturing system with loss of profits. The research proposed here concerns the evaluation of the manufacturing system performance in dynamic conditions when different maintenance policies are implemented in a multi-machine manufacturing system controlled by multi-agent-architecture. There are two extreme maintenance policies that can be applied: no preventive maintenance, where action is taken on the failure state, and intensive preventive maintenance, which can eliminate unforeseen failures, but at a high cost. Dynamic policy maintenance is proposed to reduce the number of maintenance operations of the preventive policy. A discrete simulation environment has been developed in order to investigate the performance measures and the indexes of the costs of maintenance policies. The simulations have been conducted for several levels of mix, product demand and working time uncertainty. The simulation results show that the proposed approach leads to better performance for the manufacturing system and reduces the number of maintenance operations (cost index of the maintenance policy), except in the case of the mean time between failure, which is characterised by a very low standard deviation.     

Evaluating order acceptance policies for divergent production systems with co-production

The impacts of using different order acceptance policies in manufacturing sectors are usually well known and documented in the literature. However, for industries facing divergent processes with co-production (i.e. several products produced at the same time from a common raw material), the evaluation, comparison and selection of policies are not trivial tasks. This paper proposes a framework to enable this evaluation. Using a simulation model that integrates a custom-built ERP, we compare and evaluate different order acceptance policies in various market conditions. Experiments are carried out using a case from the forest products industry. Results illustrate how and when different market conditions related to divergent/co-production industries may call for available-to-promise (ATP), capable-to-promise (CTP), and other known strategies. Especially, we show that advanced order acceptance policies like CTP may generate a better income for certain types of market and, conversely to typical manufacturing industries, ATP performs better than other strategies for a specific demand patterns.     

Markovian models for deadlock analysis in automated manufacturing systems

Deadlocks constitute a major issue in the desing and operation of discrete event systems. In automated manufacturing systems, deadlocks assume even greater importance in view of the automated operation. In this paper, we show that Markov chains with absorbing states provide a natural model of manufacturing systems with deadlocks. With illustrative examples, we show that performance indices such as mean time to deadlock and mean number of finished parts before deadlock can be efficiently computed in the modelling framework of Markov chains with absorbing states. We also show that the distribution of time to deadlock can be computed by conducting a transient analysis of the Markov chain model.     

A model based approach to assess the performance of production systems in degraded mode

Nowadays production systems are asked to perform their activities in a high uncertainty environment and to guarantee their performance in this environment. Therefore, they are asked to master risks that are part of their daily activities, to maintain the performance which is considered as their key success factor. Risks may cause serious effects that threaten the production systems and degrade their performance. Nevertheless, we cannot estimate the degradation that a risk may cause to system performance, since risk analysis methods found in the literature do not allow simulating the behaviour of the system in degraded mode. In order to help production systems to assess their performance in risk situations, we propose in this paper a model-based approach that enables assessing the performance of production systems in degraded mode. Our approach is based on function, interaction, structure (FIS) modelling framework that enables modelling complex system and its failures. The resulting model is converted into an executable simulation model based on a new class of Petri Nets (PNs) called predicate-transition, prioritised, synchronous (PTPS) PN. The obtained simulation model is then executed in order to obtain performance indicators in degraded mode. This tool is used during the system design, in order to study the impact of risks on the designed production system performance. It is also used to study an existing production system in order to analyse and optimise its behaviour in degraded mode. In this article, we present our tool and apply it to a special case of production systems which is a hospital sterilisation system.     

Modelling and simulation of automated manufacturing systems for evaluation of complex schedules

Automated manufacturing systems have been studied widely in terms of scheduling. As technology evolves, the behaviour of tools in automated manufacturing systems has become complicated. Therefore, mathematical approaches to the analysis of complex schedules no longer reflect reality. In this paper, we propose a systematic way of conducting simulation experiments to evaluate the complex operating schedules of automated manufacturing systems. A simulation model is based on a timed Petri net to take advantage of its mathematical strength. Since a Petri net cannot itself have token firing rules, we introduce additional states called operational states. Operational states are not directly related to a Petri net, and are only used for decision making. In addition, a decision function that is responsible for the conflict resolution of a Petri net model and an operational state transition function are introduced. The parallel simulation concept is also suggested by dividing a Petri net into several independent decision sub-nets. A multi-cluster tool system for semiconductor manufacturing is analysed as an application.     

New heuristics for no-wait flow shops with a linear combination of makespan and maximum lateness

In this work we study a flow shop scheduling problem in which jobs are not allowed to wait between machines, a situation commonly referred to as no-wait. The criterion is to minimise a weighted sum of makespan and maximum lateness. A dominance relation for the case of three machines is presented and evaluated using experimental designs. Several heuristics and local search methods are proposed for the general m-machine case. The local search methods are based on genetic algorithms and iterated greedy procedures. An extensive computational analysis is conducted where it is shown that the proposed methods outperform existing heuristics and metaheuristics in all tested scenarios by a considerable margin and under identical CPU times.     

State estimation of discrete event systems for RUL prediction issue

This paper concerns Remaining Useful Life (RUL) estimation of discrete event systems. For that purpose, physics-based models with partially observed stochastic Petri nets are used to represent the system and its sensors. The advantage of the proposed modelling approach is to provide a realistic representation of the system, including the interaction between the normal behaviours and the failure processes. From the proposed modelling and collected measurements, timed trajectories, which are consistent with the observations, are obtained. Based on the event dates, our approach consists in evaluating the probabilities of the consistent behaviours using probabilistic models. State estimation is obtained as a consequence. The most probable future degradations, from the current state, are then considered and a method for fault prognosis is presented. Finally, the prognosis result is used to estimate the RUL as a time interval. A case study is proposed to show the applicability of the proposed method.     

Bottleneck detection of complex manufacturing systems using a data-driven method

Bottlenecks within a production line significantly affect system productivity. Most current bottleneck detection schemes focus on the long-term bottleneck detection problem using an analytical or simulation model. Furthermore, these studies are restricted to serial tandem lines only. This research focuses on extending the newly developed data-driven method for throughput bottleneck detection from a serial line to a manufacturing system with a complex layout. Within these complex systems, two specific layouts are considered: the concurrent process and the closed-loop feedback process. The method is verified using simulation case studies. An industrial case study is examined to demonstrate the practicality of this approach and to validate the efficiency of the proposed bottleneck detection method.     

Meta-heuristics for the distributed two-stage assembly scheduling problem with bi-criteria of makespan and mean completion time

This paper focuses on the distributed two-stage assembly flowshop scheduling problem for minimising a weighted sum of makespan and mean completion time. This problem involves two inter-dependent decision sub-problems: (1) how to allocate jobs among factories and (2) how to schedule the assigned jobs at each factory. A mathematical model is formulated for solving the small-sized instances of the problem. Since the NP-hardness of the problem, we also proposed a variable neighbourhood search (VNS) algorithm and a hybrid genetic algorithm combined with reduced variable neighbourhood search (GA-RVNS) to solve the distributed two-stage assembly flowshop scheduling problems and approximately optimise makespan and mean completion time simultaneously. Computational experiments have been conducted to compare the performances of the model and proposed algorithms. For a set of small-sized instances, both the model and the proposed algorithms are effective. The proposed algorithms are further evaluated on a set of large-sized instances. The results statistically show that both GA-RVNS and VNS obtain much better performances than the GA without RVNS-based local search step (GA-NOV). For the instances with small numbers of jobs, VNS achieves better performances than GA-RVNS. However, for the instances with large numbers of jobs, GA-RVNS yields better performances than the VNS. It is also shown that the overall performances of VNS are very close to GA-RVNS with different numbers of factories, weights given to makespan and numbers of machines at the first stage.     

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.     

Simulation of capacity and cost for the planning of future process chains

One of the most promising approaches in modern microelectronics is the introduction of 3D chip micro systems with through-silicon via (TSV) interconnections. A successful transfer of this technology from the scientific level up to the level of mass production is not least of all a matter of cost-effectiveness and profit, which is directly related to high productivity. The developed technologies therefore have to be feasible for effective mass production. In this paper we introduce a method for planning and evaluating costs in future process chains. This method goes beyond usual mostly Excel-supported solutions, as it is based on a discrete event simulation system. The simulation model is generically generated out of an XML process chain definition file and includes a sophisticated state model for machines. Multiple process scenarios are created with the help of a supporting software tool. These scenarios are investigated for achieving favourable equipment and process chain configurations as well as control strategies to support manufacturing ramp-ups.     

An integrated IDEF0-3/CTPN/SFC approach for design and analysis of discrete event control systems

Due to its low cost and increased reliability, the programmable logical controller (PLC) plays an important role in industry automation. However, as systems become larger and more complex, efficient and systematic analysis and design of PLCs become a more important issue. In this research, an integrated IDEF0-3/CTPN/SFC (IPS) for developing discrete event control systems (DECSs) is presented. The proposed integrated methodology provides a complete and systematic development process for the DECSs. The development process is divided into four stages: functional analysis, system behaviour analysis, system design, and system implementation. The IDEF0-3, coloured timed Petri net (CTPN) and sequential function chart (SFC) are integrated into the corresponding stages based on their characteristics. In addition, the transformation rules between the IDEF0-3 and CTPN and between CTPN and SFC are also conducted for developing DECSs. The transformation rules provide a straightforward mapping from the IDEF0-3 to CTPN and from the CTPN to SFC. The result of the integrated IPS systematically leads to SFC for PLC implementation. Finally, a ball assorting system is given to illustrate how the integrated IPS approach is implemented for developing the DECSs.     

A composite computational model of liver glucose homeostasis. I. Building the composite model

A computational model of the glucagon/insulin-driven liver glucohomeostasis function, focusing on the buffering of glucose into glycogen, has been developed. The model exemplifies an ‘engineering’ approach to modelling in systems biology, and was produced by linking together seven component models of separate aspects of the physiology. The component models use a variety of modelling paradigms and degrees of simplification. Model parameters were determined by an iterative hybrid of fitting to high-scale physiological data, and determination from small-scale in vitro experiments or molecular biological techniques. The component models were not originally designed for inclusion within such a composite model, but were integrated, with modification, using our published modelling software and computational frameworks. This approach facilitates the development of large and complex composite models, although, inevitably, some compromises must be made when composing the individual models. Composite models of this form have not previously been demonstrated.