Journal of Quality Engineering and Production Optimization
Volume:7 Issue: 1, Winter-Spring 2022

In today's world, one of the most efficient ways to build large projects and infrastructure is related to public-private partnership (3P) projects. This method is based on the partnership between the public and private sectors and exempts the government from providing financial, human and equipment resources along with taking risks in the construction of the project. In this method, after construction and operation, the project is handed over to governments and made available to the public. In this article, following the removal of obstacles to production and development projects, we used 3P approach in the face of intuitive fuzzy uncertainty to bring management decisions closer to the outside world. Meanwhile, an integrated group decision analysis based on intuitionistic fuzzy utility degree method and intuitionistic fuzzy preference evaluation technique is tailored to compute the experts’ weights and criteria importance, respectively. Then, a novel ranking approach based on positive/negative ideal solutions and relative closeness coefficient under intuitionistic fuzzy set theory is proposed to solve the 3P urban development project selection problem. Finally, in order to evaluate the proposed method and determine its efficiency for use in large projects, a case study of urban development is used, and comparisons are made between previous methods and the proposed method.

Different types of pipes are used in various industries, and linear programming of pipe production is an effective subject in this industry. Especially, efficient planning in operational level leads to reduce total cost and improves competitiveness. In this paper, the mixed integer linear programming (MILP) approach is applied for operational planning of pipe production in different periods. To provide, moreover, a mathematical model for the manufacturing line, the model which is described in this study can choose the best supplier among a variety of suppliers. This model also optimizes the amount of raw materials acquired from suppliers as well as the amount of final product manufacturing, reducing final product and raw material inventory level in the factory. The final product inventory level, raw material inventory level, manufacturing capacity, supplier and warehouse capacity for keeping the final product and raw materials are also taken into account. The model is applied in a case study in GRP pipe production plants in Turkey.

The Assembly Line Balancing (ALB) problem is one of the subproblems of the Assembly Planning(AP) problem and is defined as the process of partitioning the assembly operations into a set of tasks and assigning them to assembly workstations such that all workstations approximately have equal times. The most basic model in ALB is the Simple Assembly Line Balancing Problem for type 2 (SALBP2). The mentioned problem is an NP-hard problem and thus many researchers in the field tried to find an effective and efficient solution for it. However, the fitness landscape of this problem has not been yet studied despite the existence of numerous works on solving it. In this article, different statistical correlation and distribution measures are used and calculated in order to analyze the fitness landscape of the SALBP2 problem for 44 test problems. The results reveal that the problem's landscape is approximately uniform based on the distribution of the locally optimal assembly sequences. Therefore, for obtaining an effective and efficient solution to SALBP2 a suitable Hybrid Iterated Local Search (HILS) is designated and used to solve a number of SALBP2 problems. Comparison results with the other approaches in the SALBP2 literature represent that the HILS produces the optimal or best known solutions on most problem instances, and it performs better than other algorithms.

Project managers analyze the factors that affected projects' success, signifying performing a project within the scopes (Time, Quality, and cost) defined in the initial step. The implication of each factor on project success is essential since several of them have been specified in this area. Employing all of them is not feasible, and it may impose outrageous expenses on the organizations. Therefore, this article aims to identify the factors that impact project accomplishment and pinpoint the most contributing factors to facilitate the project's implementation. The main contribution of this paper is representing a framework by combining Machine Learning algorithms and simulation models to detect the effectiveness of leading organizational factors on project accomplishment, beneficially leading to extracting the accurate analysis.   A logistic regression algorithm was employed to build a predictive model. The predictive model was created based on independent variables to predict whether the software project would be successful or fail. Also, Gamification was determined as the most influential factor on the objective by the Logistic regression feature importance method. Then, Gamified and non-Gamified models were compared by the Simulation method and showed Gamification made a 36.26% improvement in the rojects cycle time and a 15% enhancement in the quality of employers' performance by decreasing the projects' bugs. For validating the simulation results, some projects were implemented in the real case study, and the model results clarified the Gamification potential in improving employee engagement leading to better work progress tracking and higher performance quality.

This study aims to evaluate and rank the performance of the currency units of the bank by using the integrated methods of the balanced scorecard, cross-efficiency data envelopment analysis, and game theory in a cooperative-competitive environment. In this regard, by studying the indices used to evaluate the efficacy of banks and with the help of experts in foreign exchange, seven indices are selected as inputs and outputs from four perspectives of the balanced scorecard approach. Then, by applying the proposed Nash bargaining game model in cross-efficiency in a competitive-cooperative environment, the efficiency of decision-making units is evaluated. In this way, the bank's branches compete in pairs. As a result, each branch tends to prioritize the other branch over the criteria in which they have a more significant advantage and allocate higher weight. This leads to the higher efficiency of the branch. Thus, the cross-performance matrix is ​​complemented by the performance of the bargaining model, rather than being filled by the performance of the conventional data envelopment analysis model. The proposed approach presents a new aspect of measuring performance based on the cross-efficiency model. The real case study of Isfahan Bank Melli branches is used to show the process of implementation of the model as well as the ability of the proposed approach.

Using a case study in scaffold production, this paper aimed to develop an integrated multi-objective mathematical model for a dynamic and sustainable cellular manufacturing system under fuzzy uncertainty. In this field, most studies have considered only the economic component or at most two components of sustainable production. Furthermore, with increasing concerns about global warming, environmental issues have become particularly important in manufacturing products and goods. On the other hand, customer satisfaction as one of the social responsibility cases is crucial because customer satisfaction is one of the factors of sustainability of organizations and companies in a competitive environment. Accordingly, sustainable production in this article consists of three components: economic, environmental, and social responsibility. We also subjected sustainable production in a dynamic cellular manufacturing system to fuzzy uncertainty. A sustainable multi-objective mathematical model with objective functions of minimizing costs, minimizing CO2 emissions, and minimizing product shortages (customer satisfaction) was proposed. A case study on scaffolding production was solved in GAMS software with CPLEX solver and augmented Epsilon-constraint method, and its basic variables were investigated to validate the proposed model. Then, due to the high complexity of the cellular manufacturing model, the NSGA-II and MOPSO meta-heuristic algorithms were applied to solve larger problems. As the results can be shown, the NSGA-II algorithm performed better than the MOPSO algorithm. Therefore, the results are then analyzed on the NSGA-II approach. The results indicate the proper performance of the proposed solution approach

Nowadays, enhancing the products' quality and gaining market share are the primary purposes of any company in a competitive market. So, applying a proper management approach could help companies to make optimal decisions. One of the efficient approaches is supply chain management that can manage the flow of final products and services continually. The present study develops a supply chain with integrating production and distribution activities and a multi-period routing problem. Also, in this problem, a Stackelberg competition occurs between the suppliers under normal and critical situations (in which procurement costs of materials are increased and the suppliers encounter the shortage). Therefore, some parameters are considered uncertain, and a two-stage stochastic optimization model is constructed. The model is also multi-objective to reduce cost, lost sales, and defective products. The GAMS software is used for solving a case study for the medicine industry. Due to the NP-hardness, we consider Non-Dominated Sorting Genetic Algorithms II (NSGA_II), Multiple Objective Particle Swarm Optimization (MOPSO), and a hybrid algorithm for the large-sized instances. Subsequently, the performances of the proposed algorithms are considered. The obtained results reveal that the hybrid algorithm has a better function for solving the model in medium and large-sized instances.The GAMS software is used for solving a case study for medicine industry. Due to the NP-hardness, we consider Non-Dominated Sorting Genetic Algorithms II (NSGA_II), Multiple Objective Particle Swarm Optimization (MOPSO), and a hybrid algorithm for the large-sized instances. Subsequently, the performances of the proposed algorithms are considered. The obtained results reveal that the hybrid algorithm has a better function for solving the model in medium and large-sized instances.

Credit incentives are crucial tools in supply chain and inventory management. Using this strategy, the buyer could pay the purchase cost with a delay. Therefore, it will increase the order quantity and the buyer's satisfaction. This paper investigates the economic production model considering the incentive conditions for supplier credit, variable demand, deteriorating items, and shortages. It is assumed that the supplier sends the ordered items to the manufacturer on time; however, he receives the purchase price of the products after a permitted delay. Furthermore, the deterioration rate is a fixed percentage of the inventory level. Therefore, a nonlinear programming model is proposed for figuring out replenishment policy by minimizing the total inventory cost. The best replenishment policy is examined by employing Wolfram Mathematica. Moreover, a genetic algorithm is suggested due to the model's nonlinearity. Numerical analyses show that while the results do not significantly differ, the proposed GA reaches near-optimum solutions in less CPU time.

Earned Value Management (EVM) is a technique that provides decision-makers with efficient control, analysis, and monitoring of the performance as well as the progress of a project to prevent delays and cost overruns. Earned Schedule (ES), as an extension of EVM, is introduced to deal with the problems of EVM schedule performance indicators. Using statistical quality control principles has proved to enhance the efficiency of EVM and ES. In previous approaches, schedule and cost indicators were considered independent indices, and thus the relationship between these two variables was ignored. The failure to take into account the dependency between dependent parameters can result in unrealistic and misleading results. Therefore, in the proposed approach, the relationship between two basic elements of EVM and ES, i.e. time and cost is also considered in order to more precisely analyze the results obtained from these methods. This paper proposes a multivariate quality control chart (MQCC) alongside univariate quality control charts (UQCCs) for analyzing, managing, and monitoring projects to improve the capability, accuracy, and efficiency of EVM and ES. Furthermore, to show the applicability and superiority of the proposed approach, three construction projects as case studies were applied. . The results show considerable improvement.

In this research, lifetime performance index (LPI) data are used to present a quick switching sampling (QSS) plan based on a type‐II censoring life test and the assumption that the lifetime of units follows the Weibull distribution. In this proposed QSS plan, it is also assumed that the sample size (n) and the acceptance criterion (k) are the same for both the normal and the tightened inspections of the QSS plan, but the failures (r) during the normal and tightened inspections are different in number. The equations needed to calculate the operating characteristic (OC) curve are presented for the proposed QSS along with an optimization model to minimize the average failure number (AFN). In this regard, the constraints of producer and consumers' risks are incorporated into the model. To show the performance of the proposed QSS plan, numerical analyses are performed and the studies conducted in this field are compared. The introduced QSS sampling plan can significantly reduce the cost of manufacturers at the level of industrial organizations.

Metro is one of the most important urban transportation systems in Tehran. Before the pandemic, almost two million trips were made by metro daily. A crowded metro is one of the most important sources of the outbreak. In this paper, a discrete event model is introduced to simulate the spread of the COVID-19 in Tehran metro. Three types of passengers are defined: Healthy passengers and infected passengers with acute conditions and not acute conditions. Two important ways of preventing virus transmission are self-protection (e.g., wearing a mask) and social distancing. Different scenarios of social distancing and self-protection are surveyed based on multiple replications of the proposed model. Results of the simulation showed that noncompliance with social distancing has an exponentially negative effect on the number of infected passengers. In addition, the compliance of infected passengers with the social distancing and self-protection tips is almost twice important as the compliance of healthy passengers.

This study proposes a novel sustainable multi-objective agri-food supply chain in Mushroom industry due to the lack of economic, environmental, and social aspects that the prior studies neglected. The proposed study examines a four-echelon model including suppliers, intermediate manufacturers, final manufacturers and markets (plus secondary market). The model is also validated to provide insights into a relevant industry. The results indicated that investment in the oyster mushroom would lead to economic and social improvements. Moreover, investing in the button mushroom was observed to improve all three sustainability aspects. In the case of investing in the oyster and button mushroom, increasing the capacity of compost factories and sales price would lead to different results. Furthermore, the profitability of the supply chain was found to rise when waste is sold in the secondary market. Therefore, managers can adopt different strategies under different circumstances based on their priorities to raise supply chain profitability.