Rework and postponement: a comparison of bottling strategies

This paper presents the results of a case study in a batch production facility for biological vaccines. The problem considered is that of finding the best bottling strategy for produced batches. A batch can be bottled directly after production, after positive intermediate test results, or after positive final test results. Strategies that start the bottling process quickly after production, have the advantages of a low capacity requirement for production tanks and of a small throughput time if all test results are positive. However, a production batch can only be reworked as long as it has not been bottled. So fast bottling reduces the possibilities for rework and therefore reduces the production yield. We present performance measures for comparing the different strategies and derive closed-form expressions for them. We illustrate the results obtained for the considered case.case study;process industry;rework;yield uncertainty

Rework and postponement: a comparison of bottling strategies

This paper presents the results of a case study in a batch production facility for biological vaccines. The problem considered is that of finding the best bottling strategy for produced batches. A batch can be bottled directly after production, after positive intermediate test results, or after positive final test results. Strategies that start the bottling process quickly after production, have the advantages of a low capacity requirement for production tanks and of a small throughput time if all test results are positive. However, a production batch can only be reworked as long as it has not been bottled. So fast bottling reduces the possibilities for rework and therefore reduces the production yield. We present performance measures for comparing the different strategies and derive closed-form expressions for them. We illustrate the results obtained for the considered case

Logistic planning and control of reworking perishable production defectives

We consider a production line that is dedicated to a single product. Produced lots may be non-defective, reworkable defective, or non-reworkable defective. The production line switches between production and rework. After producing a fixed number (N) of lots, all reworkable defective lots are reworked. Reworkable defectives are perishable, i.e., worsen while held in stock. We assume that the rework time and the rework cost increase linear with the time that a lot is held in stock. Therefore, N should not be too large. On the other hand, N should not be too small either, since there are set-up times and costs associated with switching between production and rework. For a given N, we derive an explicit expression for the average profit (sales revenue minus costs). Using that expression, the optimal value for N can be determined numerically

Reverse logistics

This paper gives an overview of scientific literature that describes and discusses cases of reverse logistics activities in practice. Over sixty case studies are considered. Based on these studies we are able to indicate critical factors for the practice of reverse logistics. In addition we compare practice with theoretical models and point out research opportunities in the field

Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability

We propose a new condition-based maintenance policy for complex systems, based on the status (working, defective) of all components within a system, as well as the reliability block diagram of the system. By means of the survival signature, a generalization of the system signature allowing for multiple component types, we obtain a predictive distribution for the system survival time, also known as residual life distribution, based on which of the system's components currently function or not, and the current age of the functioning components.The time to failure of the components of the system is modeled by a Weibull distribution with a fixed shape parameter. The scale parameter is iteratively updated in a Bayesian fashion using the current (censored and non-censored) component lifetimes. Each component type has a separate Weibull model that may also include test data.The cost-optimal moment of replacement for the system is obtained by minimizing the expected cost rate per unit of time. The unit cost rate is recalculated when components fail or at the end of every (very short) fixed inter-evaluation interval, leading to a dynamic maintenance policy, since the ageing of components and possible failures will change the cost-optimal moment of replacement in the course of time. Via numerical experiments, some insight into the performance of the policy is given.</p