Journal of Optimization in Industrial Engineering
Volume:14 Issue: 31, Summer and Autumn 2021

Accelerated Life Testing (ALT) is very important in evaluating the reliability of highly reliable products. According to ALT procedure, products undergo higher stress levels than normal conditions to reduce the failure times. ALTs have been studied for various conditions and stresses. In addition to common stress such as temperature and humidity, random usage can also be considered as another stress that can cause failure. Design of ALT plan for products which are exposed to random usage process have not been studied in the literature. Therefore, a procedure for designing ALT plan for these products is studied in this paper. To do so, hazard rate of products is formulated based on the random usage process and other stresses. Then, the variance of the hazard rate is estimated over a predetermined time period. Optimum stress levels and the number of units at every stress level are obtained by numerically minimizing the variance of the hazard rate estimate. Numerical example and sensitivity analysis are performed to show the application and robustness of the model to parameter deviations. The results show that the proposed procedure is robust to parameter changes and can be used for ALT planning of products under random usage.

The wind turbine has grown out to be one of the most common Renewable Energy Sources (RES) around the world in recent years. This study was intended to position the Wind Turbine (WT) on a wind farm to achieve the highest performance possible in Electric Distribution Network (EDN). In this paper a new optimization algorithm namely Salp Swarm Algorithm (SSA) is applied to solve the problem of optimal integration of Distributed Generation (DG) based WT (location and sizing) in EDN. The proposed algorithm is applied on practical Algerian EDN in Constantine city 73-bus in presence single and multiple WT-DGs for reducing the total active power loss. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization algorithms. A numerical simulation including comparative studies was presented to demonstrate the performance and applicability of the proposed algorithm.

In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the health care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization. Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used was a multilayer perceptron network using the error propagation learning algorithm.After predicting the stock price with the neural network, the forecast price data in the second phase has been used to optimize the stock portfolio.In this phase, a multi-objective genetic algorithm is used to optimize the portfolio, and the optimal weights are assigned to the stock and the optimal stock portfolio is created.Having a regression model, the design of the relevant genetic algorithm has been done using MATLAB software.The results show that the stock portfolio created by MOPSO algorithm has a better performance compared to the algorithms used in the article under comparison under all four risk criteria except the criterion of conditional risk exposure. In all models, except the conditional risk-averaged value model, the stock portfolios created by the MOPSO algorithm used in the research have more and more appropriate performance.

In this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method.

Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental criteria. Then, the suppliers were evaluated and ranked on the basis of those criteria. The score table derived for the green criteria was one of the inputs to the algorithm. Moreover, membership functions and minimum support values ​​were specified for each criterion as another input to the algorithm. The supplier ranking index was also obtained based on the score assigned to supplier's performance. Then, the hidden relationships between data were discovered and association rules were achieved and analyzed to identify the most important green criterion for selecting green suppliers.

Nowadays, the citrus supply chain has been motivated by both industrial practitioners and researchers due to several real-world applications. This study considers a four-echelon citrus supply chain, consisting of gardeners, distribution centers, citrus storage, and fruit market. A Mixed Integer Non-Linear Programming (MINLP) model is formulated, which seeks to minimize the total cost and maximize the profit of the Citrus supply chain network. Due to the complexity of the model when considering large-scale samples, two well-known meta-heuristic algorithms such as Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms have been utilized. Additionally, a new multi-objective ACO algorithm based on a set of non-dominated solutions form the Pareto frontier developed to solve the mathematical model. An extensive comparison based on different measurements analyzed to find a performance solution for the developed problem in the three sizes (small, medium, and large-scale). Finally, the various outcomes of numerical experiments indicate that the MOACO algorithm is more reliable than other algorithms.

The supply chain network design not only assists organizations production process (e.g.,plan, control and execute a product’s flow) but also ensure what is the growing need for companies in a longterm. This paper develops a three-echelon supply chain network problem including multiple plants, multiple distributors, and multiple retailers with amulti-mode demand satisfaction policy inside of production planning and maintenance. The problem is formulated as a mixed-integer linear programming model. Because of its NP-hardness, three meta-heuristic algorithms(i.e., tabu search, harmony search and genetic algorithm) are used to solve the given problem. Also, theTaguchi method is used to choose the best levels of the parameters of the proposedmeta-heuristic algorithms. The results show that HS has abetter solution quality than two other algorithms.

The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). In this study, the criteria for evaluation are obtained by using the Fuzzy Delphi method. Due to the social, economic and environmental problems of health care organizations, the importance of sustainability criteria is evident in the case study indicators. We utilized the multiplicative DEA approach to measure the efficiency of a general system and a heuristic technique was used to convert non-linear models into linear models. Ultimately, this paper concentrates on the interval efficiency to rank the units.

The purpose of this research studies the impact of business intelligence on the financial reporting quality of listed companies in the Tehran Stock Exchange using structural equation modeling. The instruments of this research were the business Intelligence Questionnaire (Provich, 2012) and the financial statements of listed companies in The Tehran Stock Exchange to study of the financial reporting quality. For this purpose, the data of 182 listed companies in the Tehran Stock Exchange in 2018 was collected and processed. To analyze the data, Partial Least Squares Method and PLS-3 software were used. The findings of the research showed that each of the components of business intelligence including data integrity, analytical capabilities, information content quality, information access quality, use of information in business process, and Analytical decision - making culture has a positive and significant effect on the financial reporting quality

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.

The current research has been conducted to provide a model for customer experience management in the mobile banking industry for customers of commercial banks in Dubai. An explorative mixed methods research (qualitative and quantitative) was used in the research. Data were gathered in both qualitative phase (based on grounded theory) and quantitative phase (based on cross-sectional survey method). In the qualitative phase, population consisted of academic specialists and experts (university professors in the field of management) selected by judgmental sampling method of snowball sampling type. Data were gathered using a semi-structured interview. Data gathering reached theoretical data saturation in the twenty-fifth interview, so interviews were stopped at this point. The results of coding based on grounded theory led to the identification of 170 open codes, 24 axial codes, and 7 selective codes including value, cognitive, motivational, sensory, physical, behavioral, and communicative ones. In the quantitative phase, population consisted of 100,000 users (equal numbers of men and women) of mobile banking services. Given that the community variance was not available, Morgan and Krejcie table were used to determine the sample size that was calculated at 384 individuals. Data analysis in the quantitative phase confirmed the findings of qualitative research according to chi-square (x2), goodness of fit (GFI), adjusted goodness of fit (AGFI), and root mean squared error of approximation (RMSEA) indices.

Standby redundancy is a common and fundamental technique for increasing the reliability and availability of various systems. Cold-standby state is one of the most important strategies that are well used in non-repairable systems and plays an important role in mission-critical systems reliability, such as space exploration and satellite systems. In this paper, closed-form equations are derived using the Markov method to calculate the reliability function and the meantime to failure of a 1-out-of-n cold-standby system with non-repairable components under imperfect switching. While it is assumed that the failures of the switch and its associated active components are independent of each other, a constant failure rate is considered for the components and an increasing constant failure rate for the switch as it is used more frequently. In the end, numerical examples are solved for a system with various numbers of components to demonstrate the application of the closed-form equations.