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This research develops a new performance indicator for material flow effectiveness in production systems. The so-called flow value can represent the smoothness of material flow in a production system, thus can be used as real time indicator to instantly reveal the performance of a production system. A simulation study on a machining equipment manufacturing company in Taiwan validates the usefulness of using flow value as an indicator of material flow performance. This flow value is also capable of revealing areas where improvement can be made to effectively improve total system's performance in terms of material flow.

This paper studies the optimal redundant policies for a manufacturing system processing jobs with random working times. The redundant units of the parallel systems and standby systems are subject to stochastic failures during the continuous production process. First, a job consisting of only one work is considered for both redundant systems and the expected cost functions are obtained. Next, each redundant system with a random number of units is assumed for a single work. The expected cost functions and the optimal expected numbers of units are derived for redundant systems. Subsequently, the production processes of N tandem works are introduced for parallel and standby systems, and the expected cost functions are also summarized. Finally, the number of works is estimated by a Poisson distribution for the parallel and standby systems. Numerical examples are given to demonstrate the optimization problems of redundant systems.

It has been assumed in most maintenance models that (i) the number of units for a parallel system can be predefined precisely, (ii) maintenance cost after failure should be avoided, and (iii) age replacement can always be performed at its optimized times. However, these assumptions are challenged from practical perspectives in this paper. For this purpose, shortage and excess costs, which claim that replacement done too early before failure involves a waste of operation, are introduced into replacement models, such as replacement plans with time T or distribution G(t) for constant n or random N of units. Furthermore, the number N of working times and the number n of available units for a scheduled replacement time T are discussed. For each model, the expected replacement cost for one cycle and expected replacement cost rate for a long run are optimized, and numerical examples are given. 2016 Elsevier Ltd. All rights reserved. This work is partially supported by Postdoctoral Research Award PDRA1-0116-14107 from the Qatar National Research Fund, and Ministry of Science and Technology of Taiwan MOST 103-2221-E-030 -008 . Scopus

It has been proposed in recent literatures that if replacement time is planned too early prior to failure time, a waste of operation cost, i.e., excess costs, would incur because the system might run for an additional period of time to complete critical operations; and if the replacement time is too late after failure, a great failure cost, i.e., shortage cost, is incurred due to the delay in time of the carelessly scheduled replacement. The above notion of shortage and excess costs are taken into consideration for the replacement first, replacement last and replacement overtime models in this paper. We obtain the expected cost rates and their optimum replacement times. Comparisons among these optimum times are made analytically and numerically.

Some systems work for a job with random working times. It would be useful for such systems that they are checked at the completion of working times to detect their failures, which is called a random inspection policy. Two inspection policies, where a system is checked at periodic or successive times and also at every completion of working times, are considered. The total expected costs until the detection of failure are obtained, and when the random working time is exponential, optimal inspection policies which minimize them are derived analytically. Furthermore, the inspection policy where the system is checked only at every completion of N th working time is proposed. Finally, as one of modified random inspection models, the backup model where the system goes back to the latest checking time when it has failed is taken up and analyzed, by using the inspection policy.

This paper proposes a random and age replacement policy for an operating unit which works at random times. First, the unit is replaced before failure at a planned time T or at the completion of a working time, whichever occurs first. The expected cost rate is obtained. Next, as one extended model, the unit is replaced before failure at a number N of working times or at a planned time T. An optimal policy which minimizes the expected cost rate is discussed analytically, and its numerical example is given. Two modified models, where the unit is replaced at the first completion of the working time over time T or at number N, and it is replaced at time T or number N, whichever occurs last, are considered. Furthermore, we show that this corresponds to a cumulative damage model by replacing shock with work. Finally, one optimization problem of how much time to preset for scheduling the completion of N works is proposed.

This article is an extension of the periodic imperfect maintenance model in the literature. The purpose of this research is to investigate the influence on the optimal periodic maintenance policy after considering the failure replacement in the last period of preventive maintenance (PM). The specific maintenance decisions for a repairable system are: when to replace the system, when to repair the system; whether to conduct PM or keep the system operating. A partially periodic PM policy is proposed incorporating the costs of minimal repair, PM, failure replacement, and preventive replacement. The average cost rate for the proposed policy is obtained. Finally, the optimal parameters of the maintenance policy can be calculated using the cost rate function and the numerical comparisons of different policies are provided to demonstrate the effect of failure replacement.

In this paper, we study a modified minimal repair/replacement problem that is formulated as a Markov decision process. The operating cost is assumed to be a nondecreasing function of the system's age. The specific maintenance actions for a manufacturing system to be considered are whether to have replacement, minimal repair or keep it operating. It is shown that a control limit policy , or in particular a ( t , T ) policy, is optimal over the space of all possible policies under the discounted cost criterion. A computational algorithm for the optimal ( t , T ) policy is suggested based on the total expected discounted cost.

Flexibility is an important consideration when implementing an optimal policy for the maintenance and replacement of systems subject to stochastic deterioration. Repair and replacement policies are usually determined on the basis of expected costs; however, it is important to provide a means by which a given policy can be re-evaluated for a particular realization. In other words, when a machine fails, particular costs relevant at that time should be used to modify the policy if those costs vary from the expected values that were used in determining the original policy. In this paper we discuss a minimal repair/replacement problem to demonstrate the use of post-optimal analyses that are performed at the time of policy implementation.

Some systems work for a job with random working and processing times, and are checked at periodic and random times. When a failure occurs, we execute the backup operation to the latest checking time. First, the expected costs with periodic and random backups are formulated, and comparisons between periodic and random policies are made. Next, we focus on the backup policies when the system is checked at the Nth interval of working times, two total expected costs are obtained and optimal numbers N* which minimize them are derived analytically. Finally, one modified backup model in which failures are detected only at checking times is proposed and two expected costs are obtained.