Journal of Applied Research on Industrial Engineering
Volume:10 Issue: 2, Spring 2023

Hub location problems (HLP) have multiple applications in logistic systems, the airways industry, supply chain network design, and telecommunication. In the HLP, the selected nodes as hubs perform the principal role in processing the inflow arising from other nodes. So, congestion would be inevitable at hub nodes. This paper considers a p-hub median problem with multiple hub node servers delivering service at variable rates. Since the service rates are limited and variable, a queue is formed at each hub server. To tackle this problem, we developed a mixed-integer linear programming model that optimizes the selected hub nodes to reduce congestion under an allowable defined queue length at each server and minimize the total costs of the model, including transportation and hub establishment costs. We utilized the Civil Aeronautics Board (CAB) dataset containing 25 USA cities, which is a valuable source for designing numerical examples in the HLP, to prove the model's efficiency. The results obtained from the designed sample problems show that strategic decisions on defining the number of hubs and maximum acceptable queue length at each hub server will significantly impact the hub location network design.

Today, one of the topics in supply chain management is "multiple sales channels" and "pricing". In this research, a food producer (west Sahar Dasht Company) has been selected, and several retailers and wholesalers have been considered as the company's customers. This research dynamically solves the model through the game theory method. To obtain the equilibrium point and Stockelberg, the lower level optimal values (retailers and suppliers) are calculated based on the higher-level values (manufacturer), which turns the multi-level model into a single-level model to calculate the higher level optimal values. By presenting a case study and analyzing the sensitivity of the parameters, it was shown that some changes in the parameters have a significant effect on the problem variables, and its equilibrium model is better. Because game theory is proposed to solve problems on a small scale, and because the present problem is so complex, genetic algorithm meta-heuristic and particle aggregation optimization have been used to solve medium and large problems. To validate their results, they are compared with the results obtained from the mathematical model. Finally, comparing the performance of the two meta-heuristic algorithms through statistical analysis has shown that the particle aggregation optimization algorithm performs better than the genetic algorithm.

To enhance the manufacturing process capability of a refractory company, the scope for implementing the Lean Six Sigma (LSS) methodology is analyzed in this work. The DMAIC methodology of Six Sigma is used in this project to determine the Critical to Quality Characteristics (CTQs), defining the possible causes, identifying the variation in sources, establishing the variable relationships, and implementing the control plans. It was found from the DMAIC approach that the quality of Raw Crude, Water Content, and the frequency of using Temperature Calibration Equipment are the main factors responsible for lowering Productivity in Shaft Kiln. To improve the productivity of Kiln, it was suggested to process the raw crude free of mud, remove the moisture content present in the magnesite stones and take action on changing the frequency of measuring the oil feeding calibration equipment.

The purpose of this research is to investigate the safety of inland waterway transportation in Kurutie, Okerenkoko, and Escravos River, Nigeria. The study used a cross-sectional research design, and the study's target group includes passengers who are technical experts, maritime workers, non-academic, academic personnel's and students of Nigeria Maritime University, and self-employed passengers who live in the study locations. Questionnaires and field observations were used to obtain data. 378 questionnaires were delivered throughout the study area. According to the study, most cases of maritime boat mishaps beleaguered the inland waterway in the study area due to unskilled boat drivers, overloading/overcrowding of boats, and a lack of enforcement of safety laws by government agencies within the study area. The study recommended that relevant authorities, such as the Nigeria Inland Waterways Authority, enforce safety regulations among jetty operators and boat drivers; that training and certifying boat drivers are enforced; and that government involvement be increased by developing a sensitization program to educate passengers on the importance of adhering to safety practices along the waterways.

Multiple criteria decision-making (MCDM) is well known nowadays as a methodology in which a set of techniques are integrated to evaluate a set of alternatives with specified criteria for the purpose of selecting or ranking. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is well-established methodology frequently considered in MCDM analyses. TOPSIS has a sound logic that represents the rationale of human choice and is a scalar value simultaneously taking into account both the best and worst alternatives. Moreover, it has a simple computation process that could be easily programmed and finally it has the ability to rank alternatives on attributes to be visualized on a polyhedron, in at least two dimensions. Despite the advantages of this method, the process of ranking alternative according to related criteria may need more consideration. Typically, there are contributions in this article. First, a new similarity measure has been introduced followed by a modification applied to TOPSIS analyses. Second, the modified similarity technique was subsequently extended in the fuzzy context to cope with the uncertainty inherently existing in human judgments. A numerical example of the personnel selection was presented to demonstrate the possible application of the proposed method in human resource management. The outcome of applying fuzzy similarity method showed a significant distinction in ranking alternatives compered to TOPSIS method. Therefore, the modification is sound to be a proper solution.

The saving banks of “umbilical cord blood stem cells” are considered as strategic health-based institutions in most countries. Due to the limited capacity of cord blood sample storage tanks, the samples should be evaluated according to their quality. So these banks need a method to assess quality. In this paper, first, the effective factors on the quality index of the extracted cord blood from newborn infants are identified using the electronic records and database of Royan’s umbilical cord blood bank. Then by machine learning and various statistical methods such as multilayer perceptron neural networks, radial basis function neural networks, logistic regression, and C4.5 decision tree, the quality value of blood samples and their proper category (for discarding or freezing) are determined. Two different sets of data have been used to evaluate the proposed methods. The results show that the ensemble of radial basis function neural network with k-means clustering model has the best accuracy compared to other methods, which categorizes the samples with 91.5% accuracy for the first data set and 81.6% accuracy for the second one. The results also show that using this method can save about $1 million annually.

Given the increased competition and turbulence in business environments, the proper management of human resources and employee growth is a significant challenge faced by organizations to achieve competitive advantage. The present study aimed to analyze the influential factors in human resource development (HRD) in state-owned enterprises (SOEs). This was an applied research in terms of objective and a mixed (qualitative-quantitative), exploratory study in terms of design. In the qualitative-quantitative section of the study, content analysis and descriptive-exploratory techniques were applied. Data were collected via semi-structured interviews and by using questionnaires in the qualitative and quantitative sections, respectively. The research population included human resource experts, managers, and experts in the field of human resource planning and SOE management. In total, 22 individuals were selected via purposeful sampling. In the qualitative section, data analysis was carried out using open, axial, and selective coding for the classification of the identified factors into four categories of organizational, occupational, behavioral, and empowerment factors. In addition, screening was performed using the Fuzzy Delphi method, and the correlations between the identified factors and sub-factors were determined using the Fuzzy DEMATEL method. According to the results, empowerment factors were the most significant determinants of HRD, which could be improved by considering the associated influential factors and prioritization of organizational factors. On the other hand, the factor weighting findings based on the fuzzy analytic network process indicated that among the identified factors and sub-factors of knowledge management, empowerment factors had the most significant impact on HRD.

Nowadays, designing a reliable network for blood supply chains by which most blood demands can be supplied is an important problem in the health care systems. In this paper, a multi-objective model is provided to create a sustainable blood supply chain, which contains multiple donors, collection centers, distribution centers, and hospitals at different echelons. Regarding the potential of a blood shortage occurring, the suggested model considers the supply chain's capacity to meet hospitals' blood demands as dependable and a means of achieving the societal purpose. In addition, limiting the overall cost and environmental effect of designing a supply network and blood transportation are considered economical and environmental objectives. To solve the proposed multi-objective model, an improved ε-constraint approach is first employed to construct a single-objective model. Additionally, an imperialist competitive algorithm is developed to solve the single-objective model. Several test cases are analysed to determine the technique's effectiveness. CPLEX is then used to compare the results.

Although the importance of supply chain agility considering the necessity of speed of action, ‎response to customers, progressive changes in the market, consumers’ needs, etc. in many ‎industries is clear both scientifically and experimentally, today organizations have found that the ‎benefit from this cooperation is greater than cases performed without collaboration with relevant ‎organizations. Meanwhile, supply chain management refers to integration of all processes and ‎activities in the supply chain through improving the relations and implementing the organizational ‎processes in order to achieve competitive advantages. On the other hand, uncertainty in the ‎supply chain results in non-optimality of decisions that are made with assumption of certainty. ‎Accordingly, the main aim of this research is to provide a model for supply chain in an agile and ‎flexible state based on uncertainty variables. The method of research has been based on a ‎mathematical model, whose stages of implementation are investigated by breaking down this ‎model step-by-step. For this purpose, in the first stage and after getting familiar with the ‎intended modeling industry, solution and simulation were done. Eventually the results were ‎compared indicating that through reducing the risk-taking (increasing the protection levels), the ‎objective function which was of minimization type worsened. This study also showed that model ‎robustification is very important in order to reduce the risk of decision-making.

Over the past decade, solving complex optimization problems with metaheuristic algorithms has attracted many experts and researchers.There are exact methods and approximate methods to solve optimization problems. Nature has always been a model for humans to draw the best mechanisms and the best engineering out of it and use it to solve their problems. The concept of optimization is evident in several natural processes, such as the evolution of species, the behavior of social groups, the immune system, and the search strategies of various animal populations. For this purpose, the use of nature-inspired optimization algorithms is increasingly being developed to solve various scientific and engineering problems due to their simplicity and flexibility. Anything in a particular situation can solve a significant problem for human society. This paper presents a comprehensive overview of the metaheuristic algorithms and classifications in this field and offers a novel classification based on the features of these algorithms.