Journal of Quality Engineering and Management

Abstract Abstract: In design of the supply chain, the use of returned products and their re-cycles in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network, determining the amount and location of facilities and planning of transportation in conditions of uncertainty of demand. So that: Maximize total profit of operation, Minimize Adverse environmental effects, Maximize customer & supplier service level. In order to deal with the uncertainty of the model, a scenariobased robust planning is used and to solve the model with the actual data of the case study in the steel industry, a meta-heuristic algorithm (NSGA-II) is utilized. The results of the model obtained from the actual data set and data validation indicate that the model can be integrated in optimizing the objectives and determining the amount and location of the necessary facilities in the steel industry. 

Abstract The purpose of this article is to design a Non-Linear Mixed Integer Multilevel TwoObjective Mathematical Model to minimize the costs and maximize the reliability of blood supply chain. In this research, the reliability is measured according to the conditions and safety of transportation, temperature fluctuations, packaging standards, laboratory equipment and the demand. To test the model, the problem is modeled and solved by different dimensions using real data. In addition, the sensitivity analysis of the outputs is carried out to parameters changes. To solve the proposed mathematical model, Baron Solver of GAMS 24.9 is used. This model determines the product sent from blood center to hospital, the amount of production in blood center, the amount of blood donated from donors, the number of collection centers, the amount of product inventory in each center and hospital to minimize the costs and maximize the reliability. Given the fact that the first objective function is the maximization and the second one the minimization, there is a conflict between these two functions. That is, the costs will be minimized by maximizing the reliability. The model developed in this study determines the variables of decision so that by maximizing the reliability of supply chain, the costs will be well controlled and the waste and lack of blood minimized.  

Abstract The use of a Functional Block Diagram is usually one of the most commonly used methods to estimate the reliability of products. This method is not responsive in many missionoriented complex systems. Because at each stage of each mission, different sections and subsystems work and then stop at different times. This is precisely the problem of this research, which is a mission-centered submarine for rescue.  For this purpose, in this paper, a method for calculating the reliability of this submarine, which has four 9-step subsystems, has been designed and presented. At first, the functions of each stage are extracted from the subsystems (electricity, secondary, radio-electronics and navigation) during the meetings with experts, then a Functional Block Diagram is drawn for each step. In the following, the potential failure states for each function are determined in the form of a Failure Mode and Effect Analysis tool. Also, for risk analysis, the severity-probability matrix and the average RPN number have been used and good suggestions for improving the design presented. Further, calculating the failure rate for each step by Kim formula, we calculated the reliability of each stage of each subsystem. Then, the reliability of each subsystem is computed. Finally, in order to calculate the reliability of the entire submarine, first, the reliability of each step is obtained by multiplying the reliability values of each of the four subsystems in each step, eventually multiplied by the successive steps. According to the calculations, the total submarine reliability in the design stage is approximately 0.6, which is considered by the experts to be reasonable. 
       

Abstract The design of the closed loop supply chain network is one of the most important and fundamental strategic decisions, with the proper design of which creates a desirable structure and facilitates the efficient management of the chain effectively. One of the most fundamental problems in the design of the supply chain network is the closed loop of uncertainty in the quality of the return products, due to the emergence of this problem, the lack of accurate and accurate information as well as the dynamics and complexity of the chain components. This research in the design space the closed loop supply chain network is for durable products, which in the short term cannot be damaged and allow the reuse of parts in the production of new products, recycling or sales on the secondary market. The main purpose of this study is to use a two-stage randomized programming model and maximize expected earnings for all of the quality status scenarios in which the target function is a combination of revenue from the sale of products and recycled materials and components Recovered, in addition to fixed costs for centers, processes, logistics and transportation. Due to the complexity of the model, the problem was used with the Lp-shape and CPLEX algorithms and the GAMS software was used to solve the problem. Based on the results of the research, the substantive response introduced by CPLEX for the C3 to C6 test questions is significantly far from the optimal responses obtained by the L-Shape method. 

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 In the past years, researchers considered the proceeds from selling items or services as the most important source of corporate profits, because there was not much competition among companies. Nowadays customers are the most important source of revenue in the business institutions and service companies. Thereupon, customer satisfaction must be plan by company managers in order to preserve current customer and develop new customer in today's competitive conditions. However forecasting the future manner of customer can be useful to allocate budget and limited resource for preservation of the most profitable customers that will do a great help to the managers in order to gain market and increase profitability. In this paper, we present the approach to determinate current customer lifetime value and introduce the developed model to predict the future of customer lifetime value. At the first, the current lifetime value of the customers is determined based on developed RFM and using hierarchy weight method. Then in order to model the downfall probability of customers based on the geometric probability distribution for waiting time, customers must be group based on their characteristics by clustering approach. In this research, it is compared some clustering criteria for determining the best number of clusters. We are used some technical instruments such as Rapid Miner software for data preprocessing and also such as IBM SPSS and expert Choice for clustering analysis and compared theirs abilities. After that, we are modelled customer behavior via Markov chain procedure. Then customer lifetime value estimated for the future customers. The power of this research is the usage of developed RFM in order to weight customers before grouping. Because of this, the optimum number of clusters can be carefully determined. In order to demonstrate the applicability of this approach, the research used on the insurance company employed as the case study.