Nguyen Van Thanh

In the era of Industry 4.0, the deployment of Automated Guided Vehicles (AGVs) is critical for modernizing warehouse systems, yet the selection of an appropriate control system remains a complex challenge due to technical and economic trade-offs. This study addresses the research focus of optimizing this selection process by proposing a hybrid multi-criteria decision-making (MCDM) model that integrates the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) with the Weighted Aggregated Sum Product Assessment (WASPAS) method. The proposed framework evaluates five control strategies - Proportional-Integral-Derivative (PID), Fuzzy Logic, Adaptive Control, Model Predictive Control (MPC), and Neural Network Control- across 12 criteria categorized into technical performance, integration capability, cost, and implementation feasibility. Fuzzy AHP is utilized to manage the subjectivity of expert judgments, while WASPAS provides a robust ranking by combining additive and multiplicative aggregation models. Results reveal that PID control outperforms advanced methods in dynamic warehouse environments by offering an optimal balance of reliability, real-time processing, and implementation feasibility. Sensitivity analysis based on the methodology of Alinezhad and Amini (2011) further confirms the stability and robustness of this ranking across diverse scenarios. Ultimately, this research delivers a practical, data-driven decision-support tool for logistics engineers to optimize technology choices in rapidly evolving automation settings.

The transition toward sustainable construction practices has intensified the demand for intelligent and energy-efficient building components, particularly smart window technologies. While numerous innovations exist, the selection of the most optimal smart window solution is difficult due to the trade-offs among conflicting criteria such as energy performance, economic feasibility, environmental impact, and user comfort. This study proposes an integrated Fuzzy Multi-Criteria Decision-Making (FMCDM) framework for selecting smart window technologies. The methodology combines the Fuzzy Analytic Hierarchy Process (FAHP) to determine criterion weights and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank alternatives. Nine sustainability-related criteria across the environmental, economic, and social dimensions were evaluated using expert input. The results identify thermochromic smart windows as the optimal choice. This research contributes a structured, adaptable, and scalable FMCDM framework for sustainable technology selection, with broader applicability to green product design and decision-making in the construction and energy sectors.

In the context of the architecture and construction industries implementing environmentally friendly sustainable solutions, smart glass is also designed using sustainable materials and sustainable operations, contributing to environmental protection efforts. The application of smart glass in large buildings optimizes the buildings’ operation, helping to reduce reliance on air conditioners that consume large amounts of electricity and emit significant amounts of CO₂ into the environment. Additionally, smart glass enhances energy-saving efficiency by intelligently regulating the indoor temperature and natural lighting, thereby reducing overall energy consumption and operational costs. In this study, the authors propose a fuzzy multi-criteria decision-making (MCDM) model to evaluate and select optimal sustainable smart glass technology based on technical, environmental, economic, and social criteria. The model integrates the fuzzy analytic hierarchy process (FAHP) to calculate the weights of the criteria and the Interactive and Multi-criteria Decision-Making in Portuguese Model (TODIM) to rank different smart glass technology alternatives. The findings highlight the most suitable smart glass technology, offering significant benefits in reducing energy consumption while ensuring aesthetic appeal and durability. This study provides a comprehensive approach to integrating sustainability into smart glass design, contributing to the development of green and energy-efficient buildings.

Vietnam has a coastline of over 3260 km and an exclusive economic zone extending 200 nautical miles, providing favorable conditions for the development of wave energy. Exploring and harnessing this endless energy source to maximize the use of the available resources is essential for sustainable economic development. According to research conducted by the Institute of Marine and Island Research, the total global exploitable wave energy capacity is 212 TWh per year, accounting for nearly 1% of the global total and 90% of Vietnam’s annual electricity consumption needs. However, selecting the optimal location to construct wave energy production plants requires the consideration of various criteria, including efficiency potential, economic and social, technological, transport and environment factors. In this research, the authors propose a hybrid MCDM model including a fuzzy analytic hierarchy process (FAHP) and Interactive Multi-Criteria Decision-Making method (TODIM) under a fuzzy environment for wave power plant location selection in Vietnam. A real-world application of the approach is given to showcase the effectiveness of the proposed method, where three potential locations are assessed based on 14 criteria. The research results propose priority locations for project implementation, while providing a scientific basis for policymakers and investors in the decision-making process. This study contributes to promoting the development of renewable energy and efficiently utilizing Vietnam’s marine resources.

With rapid urbanization and industrialization, Vietnam is facing many challenges in solid waste management and increasing energy demand. In this context, the development of renewable energy from solid waste not only solves the problem of environmental pollution but also makes an important contribution to energy security and sustainable economic development. Solid waste to energy is a system of solid waste reatment by thermal methods, in which the heat generated from this treatment process is recovered and utilized to produce energy. Site selection is one of the biggest challenges for renewable energy projects. In addition to technical factors, this decision must also consider environmental impacts, including protecting ecosystems, minimizing noise, and limiting impacts on public health. To solve this problem, multi-criteria decision making (MCDM) methods combined with fuzzy numbers are often used. These methods allow planners to evaluate and balance competing factors, thereby determining the most optimal location for the project. In this study, the authors proposed a Spherical Fuzzy Multi-Criteria Decision-making Model (SFMCDM) for site selection in solid waste-to-energy projects. In the first stage, all criteria affecting the decision-making process are defined based on literature review, experts and triple bottom line model (social, environmental, and economic), and analytic hierarchy process (AHP), and fuzzy theory is applied for calculating the weights in the second stage. The weighted aggregated sum product assessment (WASPAS) method is utilized for ranking four potential locations in the final stage. The contribution of the proposed process is its structured, systematic, and innovative approach to solving the location selection problem for renewable energy projects. Choosing the right location not only ensures the success of the project but also contributes to the sustainable development of renewable energy.

Steel production is one of the major focuses of Vietnamese government’s industrial development plans in the last few decades and the industry has experienced sustained growth where enterprises have made great strides in terms of scale, output, and technology. The growth and expansion of the steel industry, however, must be carefully evaluated due to the sizeable amount of waste, including wastewater, gas, and solid waste, as well as the significant emission of pollutants. Vietnamese steel companies are confronted with the difficulty of establishing sustainable practices to satisfy the sustainability requirements of stakeholders. Green suppliers evaluation and selection is a complex process where multiple quantitative and qualitative criteria must be considered. In this study, a Multicriteria Decision Making (MCDM) model is developed to support the green suppliers evaluation process in the steel manufacturing industry in Vietnam. The proposed model combines Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Tomada de Decisao Interativa Multicriterio (TODIM) methods to determine the most suitable suppliers based on relevant criteria. The main contribution of this study is to develop a Fuzzy MCDM model to support decision makers in the steel industry in green steel suppliers evaluation proceess. The proposed model is then applied to a case study in Vietnam to demonstrate its application feasibility. The results suggested that among the potential alternatives, Supplier 5 (Viet Uc Steel) is identified as the optimal supplier.

The International Energy Agency (IEA) assesses Vietnam as the second largest electricity user in Southeast Asia. The energy consumption growth rate in the region is one of the fastest in the world, with demand growing at a steady 6% per year for the past 20 years. Within the context that domestic fossil fuel production cannot keep up with demand and climate change is occurring, to avoid depending on imported energy to operate the power system, the Government of Vietnam has turned to developing renewable energy. The potential for developing geothermal energy in Vietnam is promising with more than 200 sources of water around 40 to 100 °C, which is sufficient for the development of geothermal energy projects. Today, a method to conserve natural resources and invest in renewable energy is provided by the government by controlling the importation of gas and coal while investing in building geothermal power plants. One of the hardest problems, however, is determining a suitable place to construct a geothermal power plant (GPP). For this main reason, in the hope of solving this problem, the author has proposed a decision-making model that utilizes a fuzzy set under multi-criteria conditions. It uses two methods, Fuzzy Analytic Hierarchy Process (FAHP) followed by Weighted Aggregated Sum Product Assessment (WASPAS), for a GPP site selection decision. The FAHP model is first used to determine the weights of each criterion and sub-criterion. Then, the ranking of each alternative is calculated by the WASPAS model. A final alternative is determined to be the best solution based on calculations from the methods above for identifying suitable locations for a GPP. This research has contributed a method of using a fuzzy multi-criteria decision-making model for determining a suitable location selection. This contribution also shows a development of flexibility towards decision making for other renewable energy projects worldwide.

The plastics business has grown rapidly in recent years, with annual growth rates ranging from 16% to 18% each year (second only to the telecommunications and textile industries). The plastic industry is regarded as a dynamic industry in the Vietnamese economy due to its rapid development rate. Its high growth stems from a big market with significant expansion potential, since Vietnam’s plastic sector is still in its early stages in comparison to the rest of the globe and plastic goods are pushed and utilized in many aspects of life. In order to ensure sustainable development and comply with the provisions of the Law on Environment Protection, plastic manufactures and importers must ensure to fulfill two responsibilities: (1) proper product and packaging recycling—applicable to products and packaging with recyclable value; and (2) the collection and treatment of waste—applicable to products and packaging containing hazardous substances, difficult to recycle, causing difficulties for waste collection and treatment. Therefore, raw materials supplier selection in the plastic industry is a complex decision, and decision makers must consider many qualitative factors, quantitative factors and environmental attributes during the decision making. As a result, the goal of this study is to present an integrated multicriteria decision making model (MCDM) strategy for sustainable supplier selection in the plastics sector under fuzzy environment circumstances. This paper makes a contribution by proposing a hybrid fuzzy analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) approach model for raw material supplier selection in the plastics sector. This research also provided a useful guideline for supplier selection in other industries.

Among the growth of the Industrial Revolution 4.0 and moving towards 5.0, the application of science and technology is a decisive factor in catching up with new business trends. In particular, the application of the enterprise resources planning (ERP) towards enterprise management is extremely important. ERP systems help businesses save time and effort in managing projects and utilizing resources. Instead of manual handling, the processes are carried out automatically. At the same time, the system operates correctly and efficiently which avoids errors. In this work, the author explored the application of a fuzzy multicriteria decision making (FMCDM) method including Fuzzy Analytic Hierarchy Process model (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model for the evaluation of various ERP software (mainly warehouse and inventory management module). First, the fuzzy AHP model is applied to calculate the weights of criteria, and then, the obtained weights are used in the TOPSIS method for ranking of the alternatives. This study would assist decision makers in an ERP implementation environment in deciding a suitable ERP software for various industries.

As the global market develops along with technological advances, especially the opening of markets in developing and underdeveloped countries, the logistics industry is considered by managers to be a useful tool to link different functions of corporate strategy. Logistics is the process of planning, implementing and controlling the movement of goods or information that are related to raw materials (inputs) and final products (outputs) from the point of origin to the point of consumption in order to meet customer requirements. The logistics industry brings about many positive effects, not only for the national economy but also for creating equal business opportunities for businesses of all economic sectors. However, large companies in the logistics industry still depend on EDI or APIS to exchange secure authentication data, which increase the security of operations within the industry, but this is the cause of a lot of confusion with serious consequences on the supply chain when it is too dependent on these systems. To minimize the risks and optimize the problems mentioned above, blockchain technology could help the logistics industry to operate optimally. In an era when everything is digitized, from personal information to financial transactions, choosing a system with high reliability and security becomes extremely important. Currently, there are many blockchain development services providers and it is essential to choose the investment in this technology that suits the characteristics and purposes of the logistics organization in question. This study aimed to propose a two-stage fuzzy multicriteria decision-making model for the assessment of blockchain development services providers for logistics organizations. The hybrid MCDM model was evaluated through a real-world case study at a logistics organization. The contribution of this work is the provision of useful guidelines for the evaluation and selection of blockchain technology services providers for logistics companies.