In acceptance sampling plans, the decisions on either accepting, rejecting the lot or inspecting all items in the lot is still a challenging problem. We developed a new acceptance sampling plan to decide about the received lot based on cost objective function in the presence of inspection errors. It is assumed that inspection is not perfect and type I and type II errors occur in the inspection process. The problem of acceptance sampling plan in the presence of inspection errors is modeled using decision tree approach, where the sample size and decision of accepting, rejecting or inspecting are decision nodes. Bayesian inference is used to update the probability distribution function of nonconforming proportion. Then the cost at terminal nodes is analysed and optimal decisions are determined using a backward recursive approach. A case study is solved for illustrating the application of the model and sensitivity analysis is carried out on the parameters of the proposed methodology and the behavior of model by changing the parameters is investigated. We also compared the proposed model with single sampling model. At the end, the model is generalized in order to consider different potential decisions that can be made in practice.
An acceptance sampling plan plays a very important role in any quality assurance system. In this new economical design of acceptance sampling plan, three types of costs are included in the objective function by considering average outgoing quality limit (AOQL), average quality level (AQL) and lot tolerance percent defective (LTPD) constraints based on the maxima nomination sampling (MNS) method in a two-stage approach. The design of this sampling inspection plan involves the minimum average total inspection (ATI). The model is designed to minimize the summation of costs and the proposed MNS economical sampling plan is compared with the classical one. Practitioners can use the proposed model to decrease the total cost of inspection.
The increasing importance of maintenance and a cleaner environment besides the relations between them has encouraged the current authors to investigate a mathematical Markovian model for the condition-based maintenance problem while considering environmental e ects. In this paper, the problem of proposing a maintenance optimal policy for a partially observable, stochastically deteriorating system is studied in order to maximize the average pro t of the system with sustainability aspects. The modeling of this Condition-Based Sustainable Maintenance (CBSM) problem is done by mathematical methods such as Partially Observable Markov Decision Process (POMDP) and Bayesian theory. A new exact method, called accelerated vector pruning method, and other popular estimating and exact methods are applied and compared for solving the presented CBSM model, and several managerial conclusions are obtained.
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