Journal of Quality Engineering and Management

Abstract Purpose: This paper aims to estimate the parameters of the Weibull distribution using a genetic algorithm and compare its performance with traditional estimation methods.
Methodology: A simulation study was conducted under different sample sizes and censoring levels. The genetic algorithm was applied to maximize the likelihood function.
Findings: The results show that the genetic algorithm provides more accurate and stable parameter estimates compared to the maximum likelihood method, especially in the presence of censored data.
Originality/Value: This study presents a novel application of genetic algorithms in reliability analysis, demonstrating their effectiveness in parameter estimation for censored datasets.

Abstract application of Reliability can be seen in many industrial, communication, In the early stages of system design, many system features such as reliability, weight, cost, and etc are associated with uncertainty due to various reasons such as lifespan, operational conditions, etc. Since the use of the probabilistic approach in solving reliability problems has limitations and can only be used in quantitative analysis of information and in many cases does not produce useful and sufficient results for experts, therefore the use of the approach Fuzzy is much more efficient for solving reliability optimization problems. One of the ways to optimize the reliability is to allocate redundancy. When using redundant components in a subsystem, how the redundant components are used is particular importance. In reliability-redundancy allocation problems, the reliability of components is not known in advance and is considered as a decision variable. In the current research, the reliability-redundancy allocation problem has been investigated with two active and cold-standby redundancy strategies and the triangular fuzzy number approach has been used in using the parameters of probability functions and reliability calculation in two model problems and an industrial system. Genetic algorithm has been used to solve the problem. In the implementation of the genetic algorithm, the the random, tournament and roulette wheel methods has been used to select parents and different types of mutation and crossover operators have been used to produce children. The results are more efficient than the results obtained from solving the deterministic model.

Abstract Quadcopters are a special type of unmanned drones that have many applications in today's world. Due to limited resources, the design of a system must be done in such a way as to achieve the highest possible amount of reliability based on our limited resources.
For this purpose, first the reliability of each subsystem was calculated. Then the reliability was optimized using computer algorithms. One of the conventional methods of increasing the reliability of systems is to use redundancies, but due to its limitations Finance and mass for quadcopters, we cannot use any number of extras to increase reliability. Therefore, optimization should be used. The most famous meta-heuristic algorithms can be mentioned as Genetic Algorithm, Coco, Ant Colony, Gray Wolf, etc.
With the help of the firefly algorithm and reliability model, the quadcopter was checked in the presence of redundancy in terms of cost and mass minimization and having the most optimal reliability, and the resulting results were validated by genetic algorithm.

Abstract
 The purpose of this article is to examine the quality of goods in the process of transfer between members of the supply chain. For this purpose, a suitable mathematical model has been designed to manage the supply channel route of the supply chain problem and the problem has been solved using a genetic algorithm. In the present study, real-world conditions such as vehicle traffic constraints as well as product quality are considered by considering returned items, and also the Markov chain is used to investigate the possibility of transfer between members of the supply chain. The innovation of this research is the introduction of the channel selection system for transportation planning in the supply chain. The results show that the method used in this study has a good performance and the optimal way of product flow in a distribution network using genetic algorithm is presented. 

Abstract Control charts are one of the most important tools for evaluating process performance and monitoring. In the classic design of a control chart, the cost of quality depends on whether the quality characteristic is inside or outside the control. The use of loss function in the design of control charts, as an estimator of the cost of production of defective products, contributes to a more comprehensive assessment and better management decisions. Therefore, in this article, the combination of loss function and economic statistical design of control charts. The loss functions used so far in this field have been symmetric functions, but in many cases overestimating or underestimating the ideal value for a quality characteristic does not produce the same losses. Therefore, for the first time in the literature on the design of control charts, this paper uses the asymmetric loss function of Linux. Using a practical example, the performance of quadratic, linear, exponential and linear loss functions are compared. The result of these comparisons showed that the Linex loss function has the lowest cost in statistical-economic design of the control chart compared to other loss functions.

Abstract Abstract Control charts are used in process monitoring to identify any changes that may affect process quality. In many cases, it is assumed that the process data has a normal distribution, which may not be the case in practice. In this paper, we examine the economic statistical design of the   X ̅ control diagram when the qualitative characteristic distribution is not normal with the Markov chain approach. In this regard, we use the distribution as a model for the variable distribution of process quality. Due to the flexibility of its components, this distribution can model many distributions, including the normal distribution. We also show the design performance by analyzing the sensitivity of process parameters and based on the values ​​of skewness and elongation of the community, using genetic algorithm, for industrial application. 

Abstract The growing need for systems with high availability/reliability has led to numerous studies in recent years on reliability optimization (availability, if the system is repairable). The use of different redundancy policies and adding extra components are generally considered effective ways to increase system availability. When the system is multi-state, due to the computational complexity involved, the methods used to calculate system availability play a crucial role in providing an acceptable solution. This paper aims to minimize costs for multi-state systems under the constraint that system availability must exceed an acceptable threshold. The redundancy allocation problem is modeled as heterogeneous, meaning that components in such a system can differ from one another. Both components and the system can have multiple states. To compute system availability, the Universal Generating Function (UGF) algorithm is employed, and to optimize the system structure, the Genetic Algorithm (GA) is used.

Abstract Given the dynamics of the competitive market and changing customer needs, quality plays a key role in meeting customer requirements, and delivering high-quality products is essential for the survival and growth of manufacturing organizations. In this paper, process planning and advanced scheduling are considered, where scheduling is performed based on the orders received by the system. The production system has flexible machines and operators, and orders must be scheduled according to the current status of the machines and available operators so that, by assigning appropriate machines and operators, quality-related costs are minimized. To achieve the desired quality, the Taguchi quality loss function is developed by linking the quality characteristics of operations to the assigned operator and machine. Since the orders have precedence-required operations and, in one aspect, are similar to multiple traveling salesman problems, providing an exact mathematical model for these problems is often complex. Moreover, when dealing with flexible machines and operators, this complexity increases. In most works related to integrated process planning and scheduling, the mathematical models presented (because they define, for operations, a set of binary ordered pairs indicating precedence or non-precedence between operations) lack efficiency for solving with optimization software. Therefore, in this study, the problem is modeled with a new approach, and the problem is solved using GAMS software. For larger-scale problems, given the existing complexities, a genetic algorithm is used.