Selecting machines and buffers in unreliable series-parallel production lines

This article formulates a new optimal design problem of a series-parallel manufacturing production line, where parallel machines and in-process buffers are included to achieve a greater production rate. The objective is to maximise production rate subject to a total cost constraint. Machines and buffers are chosen from a list of products available in the market. The buffers are characterised by their cost and size. The machines are characterised by their cost, failure rate, repair rate and processing time. To estimate series-parallel production line performance, an analytical decomposition-type approximation is proposed. Simulation results show that this approximate technique is very accurate. The optimal design problem is formulated as a combinatorial optimisation one where the decision variables are buffers and types of machines, as well as the number of parallel machines. To solve this problem, ant colony optimisation and simulated annealing are compared empirically through several test problems.     

Performance evaluation of flow lines with non-identical and unreliable parallel machines and finite buffers

This paper examines serial production lines with unreliable non-identical parallel machines at each workstation and intermediate buffers with finite capacities. All machines are assumed to have exponential service times, times to failure and repair times. An efficient decomposition technique is introduced for the performance evaluation of such lines. Rather than replacing each parallel-machine workstation with an equivalent single-server workstation, the main contribution of this paper is the presentation of a direct approach to derive and apply decomposition equations directly for every parallel machine at each workstation. Experimental results indicate that such a method can provide a computationally efficient algorithm to analyse large serial unreliable multi-server production lines with a good accuracy compared against simulation and other available methods.     

An efficient analytical method for performance evaluation of transfer lines with unreliable machines and finite transfer-delay buffers

Buffers are widely adopted in transfer lines to reduce the fluctuations caused by the imbalances of systems or machine failures. This paper presents an efficient analytical method to evaluate the performance of transfer lines with unreliable machines and finite transfer-delay buffers. Firstly, the buffers with transfer delays are transformed equivalently into a series of perfect machines and buffers without transfer delays. Correspondingly, the initial transfer line is replaced by an equivalent transfer line with more machines and zero-transfer-delay buffers. Since in the equivalent transfer line the orders of magnitude of machines’ reliability parameters (mean times between failures and mean times to repair) are not at the same level, an advanced decomposition method is introduced to analyse the equivalent transfer line, using the general-exponential distributions instead of the exponential distributions to approximate the repair time distributions of the fictitious machines. Finally, extensive simulation and numerical cases are carried out to verify the performance of the developed method.     

Joint production,setup and preventive maintenance policies of unreliable two-product manufacturing systems

This article addresses the problem of joint optimisation of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup, production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities. To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behaviour, and to optimise the control policy parameters which minimise the total cost incurred. An in-depth study of the effects of the system parameter variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.     

Optimal design of flexible production lines with unreliable machines and infinite buffers

Despite the increasing use of automated manufacturing systems, combining flexible technology, only a few models for designing such systems are available. This paper presents a model for the determination of the profit-maximizing configuration of workstations (both machine types and number) along a flexible production line with unreliable machines and infinite buffers. A mixed integer programming formulation of the problem is introduced and an optimal solution algorithm is developed. For large scale problems a heuristic procedure is presented.     

Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.     

Reliability-based maintenance and job scheduling for identical parallel machines

Quality has an important role in manufacturing, and on the other hand, machine condition has a significant effect on quality. Based on this fact, all manufacturers integrate the production scheduling with maintenance activities to keep the machines in perfect conditions. In this paper, we propose a mixed integer nonlinear model to optimise the quality cost, maintenance cost, earliness–tardiness cost and interruption cost simultaneously. We assume that if machines work in undesirable conditions, their quality is reduced, resulting in quality cost. On the other hand, if the machines are repaired to decrease the quality cost, maintenance cost and other cost such as earliness–tardiness cost and interruption cost are imposed to the manufacturer. Several numerical instances are implemented by the proposed model to show the model effectiveness to obtain the best maintenance and production scheduling with minimum quality cost.     

Preventive maintenance of manufacturing systems under environmental constraints

The paper describes a new preventive maintenance approach for manufacturing systems under environment constraints. The manufacturing system under consideration consists of a machine M₁ that produces a single product in a Just-in-Time context. To satisfy a constant demand d, the system called upon another machine M₂ (the subcontractor), comprising the so-called environment, which produces at a certain rate the same type of product as M₁. Both machines are subjected to random failures. Whereas machine M₂ is uncontrollable from the maintenance point of view, an age-limit policy is used for preventive maintenance of machine M₁. It is proved that the best age to perform preventive maintenance depends on the history of machine M₁ and the state of M₂. A numerical example is used to illustrate the proposed approach.     

Integrated production,statistical process control,and maintenance policy for unreliable manufacturing systems

The objective of this paper is to develop an integrated approach for the joint control of production, maintenance and quality for batch manufacturing systems. We consider such systems that are subject to degradation which is at the origin of the production of defective units. The quality control of lots produced is performed using an ‘x-bar’ control chart. This graphical tool will allow estimating the quality of the batch being produced and possibly undertake perfect preventive or corrective maintenance actions on the production system. A buffer stock is built to maintain continuity of supply during maintenance actions. The incurred total cost includes setup cost, inventory costs, the cost of unused products, maintenance costs and quality costs. Decision variables include the buffer stock size, the sample size, the sampling interval, the surveillance and the control limits of the control chart. Numerical experiments and sensitivity analyses are provided to evaluate the effectiveness of the proposed control policy and the robustness of the solving approach.     

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.     

Machine cell formation for production management in cellular manufacturing systems

The formation of machine-part families is an important task in the design of cellular manufacturing systems. Manufacturing cell grouping has the effect of reducing material handing cost and work in process. Among the many methods utilized in machine cells formation, the similarity coefficient method is most widely used. Production sequence and product volumes, if incorporated properly in determining the machine cells, can enhance the quality of solutions and reduce the number of intercellular movements. Measures for cell formation based on operations sequence utilizing ordinal production data are few and have many limitations, such as counting the number of the trips for each individual part instead of counting the weights of the batches. A new ordinal production data similarity coefficient based on the sequence of operations and the batch size of the parts is introduced. Furthermore, a new clustering algorithm for machine cell formation is proposed. The new similarity measure showed more sensitivity to the intercellular movements and the clustering algorithm showed better machine grouping.     

Modeling of machine failures in a flexible manufacturing cell with two machines served by a robot

In this study, a stochastic model is developed to analyze performance measures of a flexible manufacturing cell (FMC) under different operational conditions, including machine failures and repairs. The FMC consists of two machines served by a robot for loading and loading purposes, and a pallet handling system. The model is based on Markov processes and determines closed-form solutions for the probabilities of system states that are used to calculate system performance measures, such as production output rate and utilizations of system components under different parametric conditions and equipment failures and repairs.     

Re-entrant lines with unreliable asynchronous machines and finite buffers: performance approximation and bottleneck identification

In this paper we study a re-entrant line with unreliable asynchronous exponential machines and finite buffers. First, an approximation method is presented to estimate the throughput of the re-entrant line. The idea of the approximation is to transform an M-machine re-entrant line into a 2M-machine serial line. Then, a system approach to identify a c-bottleneck based on blockage and starvation information is proposed, where a c-bottleneck machine is the machine whose improvement in machine capacity leads to the largest improvement in system throughput compared with improving all other machines. It is shown that the approximation method results in acceptable accuracy, and the bottleneck identification method can correctly detect the bottlenecks in most cases.     

Performance evaluation of linear and non-linear multi-product multi-stage lines with unreliable machines and finite homogeneous buffers

More and more machines are increasing their flexibility so as to be able to produce different products with the same tooling. In this context, it is necessary to have fast and accurate methods to evaluate the system performance. This paper presents an approximate analytical method for the performance evaluation of production systems in which Z different types of products are produced. The proposed method is based on the decomposition of the complex system into a set of simpler building blocks, each one formed by two machines and Z homogeneous buffers. The set of building blocks models the whole behavior of the original system. The performance of each building block is evaluated by using the aggregation technique applied to two-machine lines in which the behavior of several products is modeled by an aggregate product, thus simplifying the complexity of the analysis. The numerical results reported in this paper prove the method provides accurate results.     

An improved decomposition method for the analysis of production lines with unreliable machines and finite buffers

We consider production lines consisting of a series of machines separated by finite buffers. The processing time of each machine is deterministic and all the machines have the same processing time. All machines are subject to failures. As is usually the case for production systems we assume that the failures are operation-dependent (Buzacott and Hanifin 1978, Dallery and Gershwin 1992). Moreover, we assume that the times to failure and the times to repair are exponentially distributed. To analyze such systems, a decomposition method was proposed by Gershwin (1987). The computational efficiency of this method was later significantly improved by the introduction of the so-called DDX algorithm [Dallery et al. 1988, 1989). In general, this method provides fairly accurate results. There are however cases for which the accuracy of this decomposition method may not be so good. This is the case when the reliability parameters (mean times to failure and mean times to repair) of the different machines have different orders of magnitude. Such a situation may be encountered in real production lines. The purpose of this paper is to propose an improvement of Gershwin's original decomposition method that provides accurate results even in the above mentioned situation. The basic difference between the decomposition method presented in this paper with that of Gershwin is that the times to repair of the equivalent machines are modeled as generalized exponential distributions (Kouvatsos 1986) instead of exponential distributions. This allows us to use a two-moment approximation instead of a one- moment approximation of the repair time distributions of these equivalent machines.     

A Bayesian model for the joint optimization of quality and maintenance decisions

We develop a model for the economic design of a Bayesian control chart for monitoring a process mean. The process may randomly suffer failures that result in a non‐operating state, and thus call for an immediate corrective maintenance action, as well as assignable causes that shift the process mean to an undesirable level. Quality shifts, apart from poorer quality outcome and higher operational cost, also result in higher failure rate. Consequently, their removal, besides improving the outcome quality and reducing the quality‐related cost, is also a preventive maintenance action since it reduces the probability of a failure. The proposed Bayesian model allows the determination of the design parameters that minimize the total expected quality and maintenance cost per time unit. The effectiveness of the proposed model is evaluated through the comparison of its expected cost against the optimum expected cost of the simpler variable‐parameter Shewhart chart. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach.     

Joint optimisation of production rate and preventive maintenance in machining systems

This paper studies an integrated control strategy of production and maintenance for a machining system which produces a single type of product to meet the constant demand. Different from previous research, we assume in this study that during the production, the production rate not only influences the life of cutting tool, but also the reliability of the machine. Both the replacement of cutting tool and the preventive maintenance (PM) of machine are considered in this paper. The machine is preventively maintained at the Nth tool replacement or correctively repaired at the machine failure, whichever occurs first. PM and corrective repair may cause shortage which can be reduced by controlling inventory. There are two decision variables p and N, where p denotes the production rate and N denotes the number of cutting tool replacement before the PM is performed. An integrated model is developed to simultaneously determine the optimal production rate and PM policy that minimise the total expected cost per unit item produced. Finally, an illustrative example and sensitivity analysis are given to demonstrate the proposed model.