Journal of Applied Research on Industrial Engineering
Volume:9 Issue: 1, Winter 2022

The objective of this study is to model a dynamic redesigning closed-loop supply chain network with capacity planning in order to minimize the costs of the network. The structure of this model consists of existing facilities including manufacturing plants, distribution and reworking centers. Any such structure should change due to fluctuations in demand in order to meet customer demand. Establishing new facilities, closing the existing ones, and adding discrete capacity levels to facilities, are among the decisions which lead to necessary changes in network structure. To make the issue more realistic, it is assumed that demand and returned products are stochastic. To solve the problem, a two-stage stochastic mixed integer linear programming is modelled, followed by writing a robust counterpart of the MILP model program. An accelerated Benders decomposition algorithm is proposed to solve this model. To increase the convergence trend of this proposed algorithm, valid-inequalities and Pareto optimal cut are combined to the model. The expected performance improvement based on applying valid-inequalities and Pareto optimal cut is expressed through numerical results obtained from different samples.

The present study investigated the role of strategic cost management as a moderating variable on the relationship between supply chain practices, Top Management Support (TMS), and financial performance improvement. Financial performance improvement was considered as a dependent variable, while supply chain practices, strategic cost management, and TMS were taken as the main independent variables. Besides, the financial structure and the firm size were considered as control variables. The research sample included 165 companies that were selected using random sampling from among companies listed on the Tehran Stock Exchange. The data were collected using the Senior Management Survey (SMS) to measure supply chain practices, TMS, and strategic cost management. The results of the structural equation modeling after discriminant tests showed that strategic cost management had a positive and significant effect on the relationship between different subscales of supply chain practices and different measures of financial performance improvement. The results also showed that the relationship between strategic cost management, senior management support, supply chain activities and improving financial performance is non-linear and so that Supply Chain Integration (SCI) is more effective at low and medium levels and at higher levels. The above has a lesser or even negative effect.

The service life of engineering structure is mostly affected by the quality and strength of the welded joints. Tensile tests are performed for several reasons. The results are used in selecting materials for engineering applications. Tensile properties are frequently included in material specifications to ensure quality. The tests are measured during development of new materials and processes, so that different materials and processes can be compared. The objective of this study is to predict and optimize the tensile strength of a butt joint weldment on mild steel plate using Response Surface Methodology (RSM). The RSM was applied to predict and optimize the maximum tensile strength of a butt joint weldment on an I-section mild steel plate using Tungsten Inert Gas (TIG) welding process. The mild steel plate was cut into dimension 60mm x 40mm x 10mm with a power hacksaw, grinded and cleaned before the welding process. The experimental matrix was made of twenty (20) runs, generated by the design expert 11.1.0.1 software adopting the central composite design. The response (tensile strength) was measured and then modelled using the RSM. The result obtained show that the current and voltage has a very strong influence on the tensile strength. Based on the findings, the maximum tensile strength of 450Mpa was attained at the welding voltage (V) of 24V, current of 170A and gas flow rate of 13lit/min respectively. This study will serve as a guide to welding operators on parameter settings selection.

In this study, the effects of corrosion rate on post welded annealed heat-treated medium carbon steel in seawater was investigated. The medium carbon steel samples were butt-welded by using the Shielded Metal Arc Welding (SMAW) technique and, afterwards, heat treated by annealing at different annealing temperature was carried out. The microstructure of the unwelded and post welded heated samples was characterised by means of optical microscopy. The as received (control), unwelded and post welded annealed medium carbon steel samples were immersed in sea water for a duration of one hundred (100) days, and this was to stimulate the effect on equipment in offshore and food processing applications. Post welded heat treatment on the microstructure, weight loss and corrosion rate were evaluated. The results obtained showed an initial increase in both the weight loss and corrosion rate of samples up to 40 days and started decreasing afterwards. It was equally observed that the post welded annealed samples showed more corrosion activities than the un-welded annealed samples. Above and beyond, corrosion activity was more prominent in samples with the highest annealing temperature. More so, the unwelded annealed medium carbon steel showed a dispersion of coalescence cementite and ferrite grain while the post welded annealed medium carbon steel samples showed a martensite (light area marked by arrows) distributed in the ferrite (dark area) matrix.

The main purpose of this study is to identify the delay factors and evaluate the type ranking of delays in natural gas distribution projects. We have investigated 274 projects in Khuzestan gas company from 2015 to 2020. Projects investigated in this study included urban, rural, transmission lines, industrial pipelines, and construction. We identified 22 delay factors and categorized them into owner, contractor, and other related factors. This research shows that Most of the delay factors are related to owner causes. In addition to identifying the factors, this study also deals with the extent of their relationship. In addition to identifying the delay factors, this study also deals with the extent of their relationship. We showed that delay factors may not be independent and there is a significant association between them. These Relations clearly showed that reduction or elimination of many delay factors lead to eliminating many others. The findings of the research were validated and implemented by company experts.

The aim of this paper is to propose a mathematical model for two dental centers in a competitive market of dental tourism. Dental tourists are looking for cheaper treatment with proper quality, and dental centers are looking to maximize their profits by providing services to tourists. Government also monitors dental centers by setting tariffs (subsidies or taxes). This problem is modeled and solved in the form of Stackelberg (or Leader-Follower) game. The government as the leader determines the amount of tariffs and then the dental centers as the followers simultaneously determine the price and quality level of their services. To solve the game, first the equilibrium values related to the price and quality level of the services of the dental centers have been calculated by Nash equilibrium. Then, according to the equilibrium values obtained for dental centers, the optimal amount of tariffs are calculated. Finally, to clarify the proposed model a numerical example is provided and sensitivity analysis is performed on some parameters. In this paper, for the first time a mathematical model is developed for pricing and determining the quality of services in a competitive market of dental tourism. The obtained results indicate that increasing the amount of subsidy will lead to a decrease in the prices of service provided by the dental centers. Moreover, by increasing the amount of subsidies allocated to the dental centers, the government can expand the dental tourism industry.

The airline industry plays a fundamental role in the economic and social development of societies and it also has an important impact on improving foreign trade. The airline industry is one of the main infrastructures for sustainable development of the country. The quality of the reverse engineering service of the airline industry will be effective in increasing the safety and health of structures, reducing the impact of disasters and reducing costs. The airline industry needs its own defined models to measure the quality of service which has the ability to assess the quality of the services of this industry. Purpose of this research is to present a developed SERVQUAL model to assess the quality of services in the air industry. The Developed Reverse Logistic Airline Service Quality (RL AIRQUAL) model was a descriptive and cross-sectional analysis of reverse-customer support services of six organizational units of the air industry company using a 35-item questionnaire with standard 5-point Likert scale and adjusted SERVQUAL in eight dimensions of tangibility, reliability, assurance, empathy, responsiveness, safety and security, customer satisfaction and customer loyalty dimensions. Data were analyzed by paired t-test using SPSS software. Based on the determination of reverse service support quality, the responsiveness had the highest gap and safety and security had the lowest gap. The results can be used to assess the competitive position of the organization and identifying opportunities for improving the airline industry. With the help of the results of the modified model evaluation, applicable and suitable solutions can be developed to improve customer satisfaction.

The number of pipeline construction industry and electricity prices in Indonesia from 2013 to 2019 has increased. In order for the pipeline construction industry to be sustainable and competitive, the industry must strive to reduce electricity consumption, one of which is the ISO 50001 energy management system approach. This research identifies energy intensity data; energy performance data from Significant Energy Use (SEU) in the production process of the pipeline construction industry and comparing it with baseline values; targets to be achieved; and analyzing the effects of machines, materials, methods, human and environmental factors on energy consumption. Based on the results of the discussion, this research can conclude that the construction of piping work requires electricity for the cutting and welding process. The highest electrical intensity intensity is in the welding process, which averages 16.4 KJ/cm (88.2% of total energy consumption) and the cutting process is 2.2 KJ/cm (11.8% of total energy consumption). So a significant use of energy for corrective action is that the pipe work process is a welding process. Factors that cause high energy consumption/energy intensity in pipe construction work include method and material factors. Result of every factors can reduce the electricity consumption of the welding process from 16.4 KJ/cm to 7.5 KJ/cm (54.3%).

This study aimed to identify factors affecting the green management process readiness of banks and determining the interactions and priority of these factors. To this end, factors affecting the green management process readiness in the banking industry were extracted by using an in-depth study of the extant research and qualitative content analysis. Also, fuzzy DEMATEL method was used to explain and assess the interrelationships between the identified factors. The research sample included 14 experts in the process management field in the industry and academicians with knowledge of the concepts of emerging technologies and more than 10 years of experience at the level of managerial activities in the banking industry. Application of DEMATEL revealed that “green awareness”, “green attitude”, “green governance”, and “green technology” are the influential factors, while “green operation”, “green infrastructure”, “green lifecycle”, “green strategy”, and “green policies” are permeable factors. It was also found that “green awareness” has the greatest impact on other factors and "green operations "is the most permeable factor. The obtained results might help to raise the awareness of individuals including managers, policymakers of the organizations toward establishing and fostering a green attitude to adopting green operations.

Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.