Search results

This paper deals with the production and preventive maintenance planning control problem for a multi‐machine flexible manufacturing system (FMS). A two‐level hierarchical control model is developed according to the discrepancy between the time scale of the discounting cost event and one of the machine state processes. The proposed model extends the classical singular perturbation approach by considering age‐dependent machine failure rates and controlling both production and preventive maintenance rates. We replace the stochastic optimal control problem by a deterministic one termed limiting control problem. With this approach, we compute an age‐dependent near‐optimal control policy of the stochastic initial control problem from the optimal solution of the equivalent limiting control problem. A numerical example is used to illustrate the procedure and to show the reduction of the control problem size.

For most practical control system problems, the state variables of a system are not often available or measureable due to technical or economical constraints. In these cases, an observer-based controller design problem, which is involved with using the available information on inputs and outputs to reconstruct the unmeasured states, is desirable, and it has been wide investigated in many practical applications. However, the investigation on a discrete-time singular Markovian jumping system is few so far. This paper aims to consider an observer-based control problem for a discrete-time singular Markovian jumping system and provides a set of easy-used conditions to the proposed control law.

According to the connotation of the separation principle extended from linear systems, a mode-dependent observer and a state-feedback controller is designed and carried out independently via two sets of derived necessary and sufficient conditions in terms of linear matrix inequalities (LMIs).

A set of necessary and sufficient conditions for an admissibility analysis problem related to a discrete-time singular Markovian jumping system is derived to be a doctrinal foundation for the proposed design problems. A mode-dependent observer and a controller for such systems could be designed via two sets of strictly LMI-based synthesis conditions.

The proposed method can be applied to discrete-time singular Markovian jumping systems with transition probability p_ij > 0 rather than the ones with p_ii = 0.

The formulated problem and proposed methods have extensive applications in various fields such as power systems, electrical circuits, robot systems, chemical systems, networked control systems and interconnected large-scale systems. Take robotic networked control systems for example. It is recognized that the variance phenomena derived from network transmission, such as packets dropout, loss and disorder, are suitable for modeling as a system with Markovian jumping modes, while the dynamics of the robot systems can be described by singular systems. In addition, the packets dropout or loss might result in unreliable transmission signals which motivates an observer-based control problem.

Both of the resultant conditions of analysis and synthesis problems for a discrete-time singular Markovian jumping system are necessary and sufficient, and are formed in strict LMIs, which can be used and implemented easily via MATLAB toolbox.

The purpose of this paper is to find the optimal production and setup policies for a manufacturing system that produces two different types of parts. The manufacturing system consists of one machine subject to random failures and repairs. Reconfiguring the machine to switch production from one type of product to another generates a non-production time and a significant cost.

This paper proposes an approach based on the development of optimal production and setup policies, taking into account the possibilities of undertaking the setup for all modes of the machine, and covering them at the end of setup. New optimality conditions are developed in terms of modified Hamilton-Jacobi-Bellman (HJB) equations and recursive numerical methods are applied to solve such equations.

The proposed approach led to determine more realistic production rates of both parts and setup sequences for the different modes of the machine that significantly influence the inventory and the system capacity. A numerical example and sensitivity analysis are used to determine the structure of the optimal policies and to show the helpfulness and robustness of the results obtained.

Following the steps of the proposed approach will provide the control policies for industrial manufacturing systems with setup permitted at all modes of the machine, and when the setup does not necessarily restore the machine to its operational mode. The proposed optimal policy takes into account the stochastic nature of the machine mode at the end of setup and we show that ignoring it leads to non-natural policies and underestimates significantly the safety stock thresholds.

Considering the assumptions presented in this paper leads to a new structure of the control laws for the production planning of manufacturing systems with setup.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

This paper seeks to develop an optimal stochastic control model where interactive feedback consists of the quantity of flawless and defective products. The main objective of this study is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs.

The model differs from similar research projects in that, instead of age‐dependent machine failure, it considers only defective products as feedback into the optimal model for maintenance and production planning. In this paper a near optimal control policy of the system through numerical techniques is obtained.

In this paper, a new model in which the system's retroaction is the quantity of defective products is presented, considering that defective products are a consequence of global manufacturing system deterioration. Instead of taking into account machine failure and human error separately, it considers a defect in product as being the consequence of a combined failure; this consideration allows one to be more realistic by merging all failure parameters into a single one. A new stochastic control model, which focuses on defective products, inventory, and backlog, has been developed.

This approach extended the concept of hedging point policy to the quantity of defective products combined with preventive and corrective maintenance strategies. The control policy obtained has a bang bang structure and is completely known for given parameters.

The integration of maintenance and production strategies has been mainly focused on the machine. Many research projects have been focusing on the age when dealing with machine failure. It is considered as the main target of the cost reduction in maintenance engineering departments. The originality of this paper is the taking into account of all operational failures into the same optimization model. It brings a value added to high level of maintenance and for operation managers who need to consider all failure parameters before taking decisions related to cost.

This paper studies the integration of production and maintenance planning for an unreliable production system subject to gradual deterioration. The goal of this planning is to optimize production and maintenance while reducing workers' exposure to silica dust. The objective will therefore be to offer manufacturers a production strategy that minimizes the total cost of production while considering the health of employees.

Adequate prevention methods are determined and integrated into the granite transformation production system, which evolves in a stochastic environment. With the failure rate of the dust reduction unit being a function of its degradation state, the authors solve the optimization problem using stochastic dynamic programming in the context of nonhomogeneous Markov chain.

The resulting planning strategy shows that one can manage stock optimally while ensuring a healthy environment for workers. It ensures that crystalline silica prevention equipment is available and effective and defines the production rate according to a critical threshold, which is a function of the age of the dust reduction unit.

This article illustrates that it is possible to integrate silica dust reduction measures into production planning while remaining optimal and ensuring the health of operators. In the present study, the machined granite was assumed to be a natural granite, and production takes place in a closed environment.

The originality of this work lies in its development of an optimal joint production and maintenance strategy, which considers limits of exposure to crystalline silica. An optimal production and maintenance control policy considering employees' health is therefore proposed.

As a class of stochastic hybrid systems, Markovian jump systems have been extensively studied in the past decades. In light of some results obtained on this topic. The purpose of this paper is to investigate the stability problems for delayed Markovian jump systems.

The time‐varying‐delays considered in this paper are switched synchronously with system mode. Based on stochastic Lyapunov theory, the delay‐dependent stability conditions are developed by using some linear matrix inequality techniques. To obtain better stability criteria, the different Lyapunov‐Krasovskii functional is chosen and an important inequality is introduced.

Numerical examples show that the resulting criteria in this paper have advantages over some previous ones in that they involve fewer matrix variables, but have less conservatism. Furthermore, they only involve the matrix variables appeared in the Lyapunov functional. Therefore, there are no additional matrix variables coupled with the system matrices, which will be easier to investigate the synthesis problems for the underlying systems and save much computation.

The introduced approach is more efficient to investigate the stability for Markovian jump systems with mode‐dependent time‐varying‐delays.

Asset maintenance activities need to be prioritised as budget and planned outage time is often limited for all maintenance work to be carried out. The purpose of this paper is to develop a technique for prioritising maintenance work that maximises the return on investment under the constraints of budget and time.

Three indices are proposed to be used as indicators for prioritising maintenance. These indices are termed: maintenance investment index (MII), time index (TI) and budgetary index (BI). These indices permit prioritisation of asset maintenance based on the required emphasis on return on maintenance investment, time and budget respectively.

It is found that approaches to prioritising maintenance which integrate the critical dimensions in the decision making process are lacking in the literature. There is a need for such an approach to assist decision makers to ensure enterprise's objectives and targets are maximised with given budget and planned shutdown time.

The proposed techniques will assist engineers and mangers to develop their maintenance plan according to the enterprise needs and constraints and allowing management to make a better informed decision in maintenance.

This paper provides a new technique for ranking maintenance that maximises return on investment.

The paper presents a mathematical model that measures the profitability of the maintenance system based on the traditional accounting definition of profitability. There is currently a dire need for an article presenting a scientific‐based framework for practitioners in maintenance to make optimal instead of sub‐optimal decisions on profitability. Design/methodology/approach – A case study is presented to reflect the actual application of the proposed model. Different situations were considered in modelling to obtain optimal results. Simulation experiments were conducted to demonstrate that maintenance profitability is a reality through the use of differential calculus. Findings – It was observed that the approach presented is a holistic viewpoint of maintenance profitability measurement. Research limitations/implications – An adequate understanding of the maintenance system is needed to properly implement the model. Practical implications – The practical application of the model is that the maintenance system will now be viewed as a value‐adding activity instead of “a necessary evil” or “a bottomless pit for expenses”. Originality/value – This paper fulfils an identified need and offers practical help to managers in taking guided decisions that are optimal.

The analysis of the optimal production and preventive maintenance with lockout/tagout planning problem for a manufacturing system is presented in this paper. The considered manufacturing system consists of two non-identical machines in passive redundancy producing one type of part. These machines are subject to random breakdowns and repairs. The purpose of this paper is to minimize production, inventory, backlog and maintenance costs over an infinite planning horizon; in addition, it aims to verify the influence of human reliability on the inventory levels for illustrating the importance of human error during the maintenance and lockout/tagout activities.

This paper is different compared to other research projects on preventive maintenance and lockout/tagout. The influence of human error on lockout/tagout as well as on preventive maintenance activities are presented in this paper. The preventive maintenance policy depends on the machine age. For the considered manufacturing system the optimality conditions are provided, and numerical methods are used to obtain machine age-dependent optimal control policies (production and preventive maintenance rates with lockout/tagout). Numerical examples and sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The system capacity is described by a finite-state Markov chain.

The proposed model taking into account the preventive maintenance activities with lockout/tagout and human error jointly, instead of taking into account separately. It verifies the influence of human error during preventive maintenance and lockout/tagout activities on the optimal safety stock levels using an extension of the hedging point structure.

The model proposed in this paper might be extended to manufacturing systems, but a number of conditions must be met to make effective use of it.

The originality of this paper is to consider the preventive maintenance activities with lockout/tagout and human error simultaneously. The control policy is obtained in order to find the solution for the considered manufacturing system. This paper also brings a new vision on the importance of human reliability during preventive maintenance and lockout/tagout activities.