Shahed UniversityJournal of Quality Engineering and Production Optimization8220231201A bi-objective cash-in-transit pick-up and delivery problem with risk assessment methodology: a case study120444210.22070/jqepo.2024.18159.1269ENRezaRamezanianDepartment of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.0000-0001-7013-7713MeysamArjomandfarDepartment of Industrial Engineering, K. N. Toosi University of Technology, Tehran, IranDaryaAbbasiDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, IranJournal Article20230906In this paper, the Risk-constrained Cash-In-Transit Vehicle Routing Problem (RCITVRP) is addressed with time windows, pickups, and deliveries. It is crucial for Cash-In-Transit (CIT) companies involved in transporting valuable or hazardous goods to identify risks. Therefore, owing to the high level of risk in CIT operations (e.g., armed robbery or attack), a bi-objective mixed-integer non-linear programming (MINLP) model is used to minimize travel costs and the risks associated with transporting valuables. For risk minimization, a new risk measure is developed, which includes: (i) the level of vulnerability for each vehicle, and (ii) the threat probability on each route. In addition, multiple vehicles are considered with capacity limitations. The epsilon-constraint method, a multi-objective exact solution approach, is implemented to solve the proposed model. Furthermore, several numerical examples are generated to evaluate the model and the solution method, which clearly show the best route with minimum cost and minimum risk (cost value = 112, risk value = 64,600). Eventually, a case study is provided to investigate the applicability of the proposed model.https://jqepo.shahed.ac.ir/article_4442_ec2ac25c3ee07213f3867d8c45bcb67d.pdfShahed UniversityJournal of Quality Engineering and Production Optimization8220231201Designing a Closed-loop Green Supply Chain Network Considering Quality Costs of Raw Materials in a Fuzzy Environment2156453310.22070/jqepo.2024.19090.1279ENMahyaTalebzadehDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, IranAliGhodratnamaDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran0000-0003-0091-6337RezaTavakkoli-MoghaddamSchool of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran0000-0002-6757-926XJournal Article20240503When the reverse supply chain—which comprises the steps involved in bringing a product back into the supply chain, such as its collection, recycling, and destruction—is taken into account alongside the forward supply chain, it becomes evident how important this issue has become in recent years and how social and environmental factors have been taken into account to meet economic demands. This paper presents a five-level closed-loop green supply chain network, considering cost minimization, environmental effects, and time delays in sending products and raw materials. The model is presented under uncertainty of some parameters, considering the particular position of purchased raw materials. The tri-objective fuzzy model is converted into a crisp model using the Jiménez et al. (2007) method. The performance and efficiency of the model are analyzed using the Torabi-Hassini method and the augmented epsilon constraint method. GAMS software provides a numerical illustration of this process. Sensitivity analysis is used to the various degrees of confidence. The augmented epsilon-constrained method outperforms the Torabi-Hassini (TH) method for the first and second objective functions and vice versa for the third objective function. The computational time of the augmented epsilon-constrained method is also less than that of the TH method for all confidence levels.https://jqepo.shahed.ac.ir/article_4533_976dd981464e61acbb9eb03ddaccecbe.pdfShahed UniversityJournal of Quality Engineering and Production Optimization8220231201Designing a Mathematical Model for Two-Echelon Allocation-Routing Problem by Applying the Route and Transportation Fleet Conditions5784444110.22070/jqepo.2024.18421.1272ENZahraYadegariDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranSeyyed MohammadHadji MolanaDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University,Tehran, IranAliHusseinzadeh KashanFaculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, IranSeyed EsmaeilNajafiDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranJournal Article20231101A novel mixed integer non-linear mathematical model is presented in this paper for the two-echelon allocation-routing problem by applying the conditions of the route and transportation fleet under uncertainty. The cost of allocating drivers to non-homogeneous vehicles is calculated in this model based on the type of the vehicle, the lifecycle of the car, the experience of the driver, and different degrees of hardness that are defined for various routes. The cost of passing the route is defined based on an initial fixed cost and the degree of hardness of the route. Also, the reliability of the routes in each section is defined as an objective in the second echelon of the model aimed at enhancing the reliability rate. Two metaheuristic algorithms, NSGAII and MOPSO, are utilized to solve the model. Then, their performance rates in problems with different sizes are statistically evaluated and compared by different indices, following the adjustment of their parameters by Taguchi's method, through which results indicated the high efficiency of the model. A sensitivity analysis is ultimately performed on the results obtained from the solution, and some suggestions are made for the development of the model.https://jqepo.shahed.ac.ir/article_4441_cab48dae349a494ca20dc78ae969e941.pdfShahed UniversityJournal of Quality Engineering and Production Optimization8220231201Development of a simulation-based optimization approach to integrate condition-based maintenance, production control and control chart design in deteriorating production processes85118465210.22070/jqepo.2024.19061.1278ENSeyed MohammadHadianDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, IranHiwaFarughiDepartment of Industrial Engineering, Faculty of Engineering, University of Kurdistan,Sanandaj, Iran0000-0001-9745-9691HasanRasayIndustrial Engineering, Kermanshah University of Technology, Kermanshah, IranHadiTalebi GhadikolaeiDepartment of mathematic, Faculty of Science, Mazandaran University of Science and Technology, Behshahr, IranJournal Article20240425A statistical model is proposed to schedule maintenance actions, control the inventory, and design a proper control chart in unreliable manufacturing systems. Process deterioration can lead to quality degradation and affect maintenance scheduling and production control. Integrated control of three aspects can enhance the productivity of production processes. A deteriorating manufacturing system is considered that has two states of in-control and out-of-control. Quality control of the products and process monitoring are implemented by employing a control chart. Maintenance actions are scheduled based on the machine’s condition. The duration of maintenance is considered a continuous random variable that follows a general distribution. The purpose is to schedule the maintenance tasks and control the safety stock with respect to the data collected from the control chart related to the machine condition to minimize the total cost per time unit. A Genetic algorithm (GA) is used as a solution method. Then, to reduce the solution time of the proposed GA, a Monte Carlo simulation method is presented. These two methods are combinted, and as a result a simulation-optimization technique is proposed. The performance of the model is validated by a simulation-based optimization technique. Finally, sensitivity analysis of the model is performed.Shahed UniversityJournal of Quality Engineering and Production Optimization8220231201A new composite index for improved capability analysis of profile coefficients119140463210.22070/jqepo.2024.18982.1277ENAylinPakzadAssistant professor Department of Industrial Engineering, Kosar University of Bojnord, Bojnord, Iran0009-0008-7029-1903Journal Article20240331A simple linear profile (SLP) is a type of quality profile that describes the relationship between a response variable and an explanatory variable using a linear function. This concept is relevant in various industrial applications. Process capability indices (PCIs) are useful tools for measuring the process ability in producing items in conformance within the pre-set specification limits (SLs). In this paper, a composite PCI is presented for a SLP based on its parameters. The performance of the proposed PCI and existing ones are investigated and compared for their accuracy and precision of estimation. The simulation results highlight the superior performance of the proposed composite PCI to existing methods in terms of lower mean absolute error (MAE) and mean square error (MSE) metrics. Two real-world case studies are also analyzed to demonstrate how the proposed method can be applied in practice.https://jqepo.shahed.ac.ir/article_4632_e30347c5600f96ecac58f36e8bb28d16.pdfShahed UniversityJournal of Quality Engineering and Production Optimization8220231201Monitoring Serially Correlated Data by Two CUSUM Charts (Case Study: Numbers of Patients with Covid-19)141150409410.22070/jqepo.2023.16031.1232ENAhmadHakimiDepartment of Industrial Engineering, University of Kurdistan0000-0002-1810-8111HivaFarughiDepartment of Industrial Engineering, University of Kurdistan0000-0001-9745-9691JamalArkatDepartment of Industrial Engineering, University of KurdistanJournal Article20220305Statistical process control charts are utilized in many industries, including manufacturing, environmental monitoring and improvement, disease surveillance, and others. The use of statistical process control charts is common for independent process observations at different times. However, in the case of sequential data, correlation between the data is typically present. Therefore, the creation of control charts specifically for monitoring serially correlated data is essential. The Covid-19 epidemic is a severe global issue, with evidence indicating that infected individuals can transmit the virus to others, whose symptoms may appear several days later. This study aims to monitor the condition of Covid-19 patients over a specific period time using serial data. Two new CUSUM charts are used to track the number of Covid-19 patients in Iran, Japan, and Italy, with separate results presented and explained for each country. Additionally, a sensitivity analysis is conducted on key factors, yielding similar results, and the two control charts are compared.https://jqepo.shahed.ac.ir/article_4094_7c07c683a123484e25844f5cfe764051.pdf