Journal of Quality Engineering and Management

Abstract Purpose: In recent years, environmental management, with a focus on environmental protection, has become one of the main priorities of governments and organizations. One key area in this regard is the design of supply chain networks with environmental approaches, which helps integrate production processes with sustainability goals.
Methodology: This study aims to investigate the feasibility of using palm tree pruning waste in the production of Medium-Density Fiberboard (MDF). As one of the richest plant resources in the country, the palm tree has high potential for application in the wood industry and for reducing environmental waste.
Findings: First, the biological and structural characteristics of palm trees are examined. Then, using this raw material, a supply chain network design model for MDF production is developed. The model's performance is evaluated and compared under different operational and environmental conditions.
Originality/Value: The findings indicate that using palm waste in MDF production not only helps reduce environmental waste but also contributes to the design of an efficient and green supply chain. Moreover, the study highlights a lack of prior research on this specific topic, underscoring the innovative aspect of the work.

Abstract Couplings are widely used in the industry and this equipment are always subject to defects and failures due to continuous rotation. Vibration analysis is a suitable technique for failure analysis and failure detection of rotating equipment. The purpose of this research is to analyze the failures that occurred in a coupling, whose data was collected in normal state and three failure states with four sensors connected to the coupling. For this purpose, two different types of feature extraction have been used, and seven machine learning algorithms and one deep learning algorithm have been used to classify situations. In this research, the performance of each of the implemented algorithms and the importance of extracted features have been investigated, and the role of sensors and their importance to reduce the number of sensors have been investigated. From the results of this research, we can point out the high importance of the features of the frequency domain in the accuracy of the implemented models, as well as the high efficiency of two sensors for classification.

Abstract Emphasizing the importance of competition and cooperation in supply chains caused a resurgence of game theory ‎as a tool for the analysis of interactions in a supply chain. The development of the business and the ‎complexity of retailing requires a change in the advertising approaches. Affiliate advertising is one of the ways that ‎manufacturers and distributors can jointly participate in advertising programs. Therefore, a variable is defined as ‎the participation rate, which is a percentage of local advertising costs that the producer agrees to pay. In this ‎research, we consider cooperation in advertising during two different advertising function models along with pricing ‎decisions, warranty period and quality level, in a two-level supply chain. Therefore, demand will be affected by ‎price, advertising, warranty period and quality level. Manufacturer and retailer can have cooperative advertising. ‎The theory of non-cooperative and cooperative games is the tool used to solve this problem. Due to the complexity ‎of the model, a meta-heuristic algorithm, the optics inspired optimization, which is a population-based search ‎algorithm, is used to solve the problem. In this research, it was observed that the sum of the profits claimed by both ‎parties in the non-cooperative game is smaller than the profit of the whole system in the cooperative game, and the ‎profit of each individual is also lower in the non-cooperative state than in the cooperative state. Therefore, ‎cooperation between two players increases their profits. Also, sensitivity analysis has been done on some ‎parameters, including parameters related to product quality.‎

Abstract As time goes on and crises increase in societies, organizations are increasingly exposed to disruption. These crises can be of natural (such as earthquakes, floods, and fires) or human (such as terrorist attacks, infectious diseases, and intentional or inadvertent employee errors). Therefore, organizations need to be resilient to protect themselves from harmful consequences. The basic aspect of resilience involves the ability of an element to return to normal after disruption and resource allocation. Obviously, in any organization, the primary goal is to allocate the least resources to recover operations and to bring activities back to the tolerance threshold so that destructive events do not stop vital activities. In this paper, a quantitative model for resource allocation is presented, which minimizes the lack of resilience. The problem has a basic assumption, that there is a shortage of resources in at least one of the available resources due to excessive demand. After solving the model by numerical experiment, the results of the model were described and it was found that destructive events were retrieved before the tolerance threshold.

Abstract Process analysis is closely related to process optimization and in the optimization, the discussion of resources plays a major role. Since the optimal set of resources, especially in the event of unexpected events will have variable effects on risk, process performance and efficiency can be similar to the optimal stock portfolio selection model. The purpose of this paper is to provide a mathematical model for allocating human resources using the Markowitz model to the days of the week based on skills and costs, which the objective function minimizes the risk and maximizes the efficiency of resources. The proposed model includes a combination of risk and return assessments to find the best resource portfolio in critical situations. The study also used the Epsilon constraint method. The innovations of this research is the application of Markowitz model to optimize resource allocation and risk-based allocation of resources in the process. The proposed model has been used in a case study of a urology subspecialty kidney center. Numerical results show that using the designed model, resources can be allocated in such a way that it has optimal returns and risk, in order to produce the maximum amount of output in critical situations.
 

Abstract In today's competitive market, many products are sold under the basic warranty, and in order to increase profit margins and customer satisfaction after the end of the basic warranty, manufacturers offer warranty extensions to customers at a specific price and time. In this research, the aim is to maximize the producer profit by determining the optimal values of product price, length of basic warranty period, length of warranty renewal, repair level in incomplete repair and amount of spare parts (for demand of products under basic warranty, warranty extension and out of warranty). . In order to better model the real situation, it is assumed that the product can be repaired with three types of minimum, incomplete and complete repairs at the time of failure, and the percentage of products that are repaired each time is also considered as a decision variable. have became. The product reliability function is modeled at the time of incomplete repair with the Kijima virtual life model approach. The problem-solving approach is based on the simulation-based optimization approach in three stages. In the first stage, decision variables such as product price, basic warranty period, warranty renewal period, warranty renewal price, repair level and the probability of what kind of repair each product It is determined by a meta-heuristic algorithm. Then, using Monte Carlo simulation, the number of products that have purchased the warranty extension and the number of product failures is calculated, and finally, the production of spare parts is optimized using a dynamic programming algorithm. This model has also been analyzed for LG vacuum cleaner product and Goldiran after-sales service. 

Abstract Today, manufacturers use warranty as an important advertising tool to increase market share. Offering the product to the market with a longer warranty, although attracting more customers, increases the manufacturer's costs. In the real world, manufacturers can provide buyers with an option to renew their warranty for a specified period of time after the end of the basic warranty period. Thus, products that do not renew their warranty as products out of warranty are the main source of profitability of the manufacturer from the sale of spare parts. In this research, a new model is presented that helps the manufacturer to increase profits and considering the level of customer satisfaction and the effect of selling spare parts to products out of warranty, such as pricing of the main product, length of basic warranty period, length of warranty renewal period. Optimize the price of the warranty extension. The developed particle swarm algorithm and the sports league algorithm have been used to solve the model. Due to the fact that Goldiran Company in Iran, as the after-sales service representative of LG, has provided the possibility of extending the basic warranty for products under this brand, the proposed model has been solved with data related to the LED product. The results show that Goldiran Company can help increase the producer profit margin by changing the values of the basic warranty period and extending the warranty according to the product life cycle and planning time, while now the company has fixed the warranty period and extended warranty period for all its products. is considered.