Optimal lot sizing in screening processes with returnable defective items

This paper is an extension of Hsu and Hsu (Int J Ind Eng Comput 3(5):939-948, 2012) aiming to determine the optimal order quantity of product batches that contain defective items with percentage nonconforming following a known probability density function. The orders are subject to 100 % screening process at a rate higher than the demand rate. Shortage is backordered, and defective items in each ordering cycle are stored in a warehouse to be returned to the supplier when a new order is received. Although the retailer does not sell defective items at a lower price and only trades perfect items (to avoid loss), a higher holding cost incurs to store defective items. Using the renewalreward theorem, the optimal order and shortage quantities are determined. Some numerical examples are solved at the end to clarify the applicability of the proposed model and to compare the new policy to an existing one. The results show that the new policy provides better expected profit per time

Decision rule of repetitive acceptance sampling plans assuring percentile life

AbstractIn this research, Repetitive Group Sampling (RGS) plans are developed for the Weibull and generalized exponential distributions. To design the proposed plans, the median of a life-time is first used as the quality parameter. Then, a decision-making framework is developed, based on first and second type errors. Next, based on acceptable and limiting quality level criteria, tables are obtained to select the parameters of the proposed decision-making framework. The advantages of the proposed method over single sampling plans are discussed at the end

A multi-objective multi-state series-parallel redundancy allocation model using tuned meta-heuristic algorithms

In this paper, we study a series-parallel multi-objective multi-state redundancy allocation problem (MSRAP) with known performance levels and corresponding state probabilities. The problem is comprised of multiple subsystems in series and each subsystem is comprised of multiple components in parallel. The system components have a range of performance level from complete working to complete failure. The subsystems contain homogenous redundant components and the component prices come under an all-unit discount policy if a unique brand (type) is chosen for purchasing all subsystem components. Each component is characterised by its cost, weight and availability. The goals are to find the optimal combination of the components in each subsystem that maximises system availability and minimises the total cost under a weight constraint. We propose a multi-objective harmony search (MOHS) algorithm, a non-dominated sorting genetic algorithm (NSGA-II), and a multi-objective genetic algorithm (MOGA) to solve this problem. In addition, the Taguchi method is utilised to tune the parameters in each algorithm. We use a number of numerical examples to demonstrate the applicability and exhibit the efficacy of the three algorithms. The results show that the MOHS outperforms the NSGA-II and MOGA with respect to all of the considered metrics

Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

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