Thanh-Tuan Dang

Bio: Thanh-Tuan Dang is an academic researcher from International University, Cambodia. The author has contributed to research in topics: Data envelopment analysis & Multiple-criteria decision analysis. The author has an hindex of 6, co-authored 12 publications receiving 75 citations.

A compromised decision-making approach to third-party logistics selection in sustainable supply chain using fuzzy ahp and fuzzy vikor methods

Abstract: With the effects of the COVID-19 pandemic, the e-commerce trend is driving faster, significantly impacting supply chains around the world. Thus, the importance of logistics and supply chain functions has been amplified in almost every business that ships physical goods. In Vietnam, the logistics service sector has seen rapid expansion. Since more and more businesses are seeking third-party logistics (3PL) providers to outsource the logistics functions, this article aims to offer decision-makers an integrated and consistent model for evaluating and selecting the most efficient 3PLs. To this end, the authors exploit a hybrid multi-criteria method which is fuzzy analytic hierarchy process (FAHP) and fuzzy vlsekriterijumska optimizacija i kompromisno resenje (FVIKOR) while examining the most influential and conflicting criteria regarding economic, service level, environmental, social, and risk aspects. Fuzzy information in the natural decision-making process is considered, linguistic variables are used to mitigate the uncertain levels in the criteria weights. First, FAHP (the weighting method) is adopted to evaluate and calculate each criterion’s relative significant fuzzy weight. FVIKOR (the compromised ranking method) is then used to rank the alternatives. The combination of FAHP and FVIKOR methods provides more accurate ranking results. As a result, reliability and delivery time, voice of customer, logistics cost, network management, and quality of service are the most impactful factors to the logistics outsourcing problem. Eventually, the optimized 3PLs were determined that fully meet the criteria of sustainable development. The developed integrated model offers the complete and robust 3PLs evaluation and selection process and can also be a powerful decision support tool for other industries.

A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam

Abstract: The transition from fossil fuels to more clean, affordable, and sustainable energy resources is of strategic importance for developing Asian countries like Vietnam, in which solar energy is an attractive option. The government of Vietnam is implementing various policy measures and has substantial investment plans to accelerate the national spread of solar energy. In this regard, location optimization for solar power installation is a vital strategic decision that needs to be analysed in-depth to harness solar power. This study aims to determine suitable areas for solar power installation in a case study in Vietnam. For the decision-making that influences solar plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgements in linguistic terms and can be represented through grey numbers. To deal with the complex nature of the prioritization problem posed by the coexistence of multiple factors, this study proposes a combination of the Data Envelopment Analysis (DEA) method, Grey Analytic Hierarchy Process (G-AHP), and Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS). With the utilized integrated approach, both quantitative and qualitative effective criteria are considered to evaluate locations. DEA is used in the first phase to select high-efficiency locations based on various measurable criteria. In the second phase, these locations are further assessed in terms of qualitative criteria: technical, economic, environmental, and socio-political factors. For this evaluation, G-AHP is used for criteria weighting, and G-TOPSIS ranks the locations. While the proposed approach is expected to help save costs and resources towards determining hot spots for solar power plants installation all around the country, it is recommended that governments and local authorities take into account suitability policies and support mechanisms for sustainable solar energy development.

Assessing Renewable Energy Production Capabilities Using DEA Window and Fuzzy TOPSIS Model

Abstract: Climate change and air pollution are among the key drivers of energy transition worldwide. The adoption of renewable resources can act as a peacemaker and give stability regarding the damaging effects of fossil fuels challenging public health as well as the tension made between countries in global prices of oil and gas. Understanding the potential and capabilities to produce renewable energy resources is a crucial pre-requisite for countries to utilize them and to scale up clean and stable sources of electricity generation. This paper presents a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) in order to evaluate the capabilities of 42 countries in terms of renewable energy production potential. Based on three inputs (population, total energy consumption, and total renewable energy capacity) and two outputs (gross domestic product and total energy production), DEA window analysis chose the list of potential countries, including Norway, United Kingdom, Kuwait, Australia, Netherlands, United Arab Emirates, United States, Japan, Colombia, and Italy. Following that, the FTOPSIS model pointed out the top three countries (United States, Japan, and Australia) that have the greatest capabilities in producing renewable energies based on five main criteria, which are available resources, energy security, technological infrastructure, economic stability, and social acceptance. This paper aims to offer an evaluation method for countries to understand their potential of renewable energy production in designing stimulus packages for a cleaner energy future, thereby accelerating sustainable development.

Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

Abstract: E-commerce has become an integral part of businesses for decades in the modern world, and this has been exceptionally speeded up during the coronavirus era. To help businesses understand their current and future performance, which can help them survive and thrive in the world of e-commerce, this paper proposes a hybrid approach that conducts performance prediction and evaluation of the e-commerce industry by combining the Grey model, i.e., GM (1, 1) and data envelopment analysis, i.e., the Malmquist-I-C model. For each e-commerce company, GM (1, 1) is applied to predict future values for the period 2020–2022 and Malmquist-I-C is applied to calculate the efficiency score based on output variables such as revenue and gross profit and input variables such as assets, liabilities, and equity. The top 10 e-commerce companies in the US market are used to demonstrate model effectiveness. For the entire research period of 2016–2022, the most productive e-commerce marketplace on average was eBay, followed by Best Buy and Lowe’s; meanwhile, Groupon was the worst-performing e-commerce business during the studied period. Moreover, as most e-commerce companies have progressed in technological development, the results show that the determinants for productivity growth are the technical efficiency change indexes. That means, although focusing on technology development is the key to e-commerce success, companies should make better efforts to maximize their resources such as labor, material and equipment supplies, and capital. This paper offers decision-makers significant material for evaluating and improving their business performance.

A Two-Stage Multiple Criteria Decision Making for Site Selection of Solar Photovoltaic (PV) Power Plant: A Case Study in Taiwan

Abstract: At the heart of Covid-19 responses, the transition from fossil sources to green energy is an urgent issue for nations to address the crisis and secure sustainable economies. As a country in a seismically active zone that relies heavily on imported fossil fuels, Taiwan is vigorously taking the next step in renewable energy development, which is pivotal to securing its position in global supply chains. Solar energy is today the most suitable renewable energy source for Taiwan. However, land prices and policies, and challenges of scale still hinder its development. In this context, identifying optimal sites for solar photovoltaic (PV) construction is a crucial task for major energy stakeholders. In this paper, a two-stage approach, combining the data envelopment analysis (DEA) models and the analytic hierarchy process (AHP), has been done for the first time to identify the most suitable locations among 20 potential cities and counties of Taiwan for constructing solar PV farms. DEA models were applied to filter out the areas with the most potential by measuring their efficiency indices with temperature, wind speed, humidity, precipitation, and air pressure, as inputs, and sunshine hours and insolation, as outputs. The locations with perfect efficiency scores were then ranked with the AHP method. Five selected evaluation criteria (site characteristics, technical, economic, social, and environmental) and sub-criteria of each were utilized to prioritize the locations with solar energy potential. AHP was used to determine the relative weights of the criteria and sub-criteria and the final weights of the areas. For criteria weighting results, “support mechanisms,” “electric power transmission cost,” and “electricity consumption demand” with weights of 0.332, 0.122, and 0.086, respectively, were found as the most significant sub-criteria. The final ranking suggests Tainan, Changhua, and Kaohsiung as the top three most suitable cities for constructing solar PV energy systems.

A compromised decision-making approach to third-party logistics selection in sustainable supply chain using fuzzy ahp and fuzzy vikor methods

Abstract: With the effects of the COVID-19 pandemic, the e-commerce trend is driving faster, significantly impacting supply chains around the world. Thus, the importance of logistics and supply chain functions has been amplified in almost every business that ships physical goods. In Vietnam, the logistics service sector has seen rapid expansion. Since more and more businesses are seeking third-party logistics (3PL) providers to outsource the logistics functions, this article aims to offer decision-makers an integrated and consistent model for evaluating and selecting the most efficient 3PLs. To this end, the authors exploit a hybrid multi-criteria method which is fuzzy analytic hierarchy process (FAHP) and fuzzy vlsekriterijumska optimizacija i kompromisno resenje (FVIKOR) while examining the most influential and conflicting criteria regarding economic, service level, environmental, social, and risk aspects. Fuzzy information in the natural decision-making process is considered, linguistic variables are used to mitigate the uncertain levels in the criteria weights. First, FAHP (the weighting method) is adopted to evaluate and calculate each criterion’s relative significant fuzzy weight. FVIKOR (the compromised ranking method) is then used to rank the alternatives. The combination of FAHP and FVIKOR methods provides more accurate ranking results. As a result, reliability and delivery time, voice of customer, logistics cost, network management, and quality of service are the most impactful factors to the logistics outsourcing problem. Eventually, the optimized 3PLs were determined that fully meet the criteria of sustainable development. The developed integrated model offers the complete and robust 3PLs evaluation and selection process and can also be a powerful decision support tool for other industries.

CFD Analysis and Optimum Design for a Centrifugal Pump Using an Effectively Artificial Intelligent Algorithm

Abstract: In this study, we proposed a novel approach to improve centrifugal pump performance with regard to the pump head, pump efficiency, and power. Firstly, to establish constraints, an optimal numerical model that accounted for factors such as pump efficiency and the head was considered. The pump was designed, and an artificial intelligence algorithmic approach was applied to the pump before performing experiments. We considered a set of models by selecting the parameters of the centrifugal pump casing section area, the interference of the impeller, the volute tongue length, and the volute tongue angle. The weights of the factors of safety and displacement on the optimization indices were estimated. The matrix of the weights for the optimal process was less than 38% or greater than 62%. This approach guarantees a complicated multi-objective optimization problem. The results show that the centrifugal pump performances were improved.

A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam

Abstract: The transition from fossil fuels to more clean, affordable, and sustainable energy resources is of strategic importance for developing Asian countries like Vietnam, in which solar energy is an attractive option. The government of Vietnam is implementing various policy measures and has substantial investment plans to accelerate the national spread of solar energy. In this regard, location optimization for solar power installation is a vital strategic decision that needs to be analysed in-depth to harness solar power. This study aims to determine suitable areas for solar power installation in a case study in Vietnam. For the decision-making that influences solar plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgements in linguistic terms and can be represented through grey numbers. To deal with the complex nature of the prioritization problem posed by the coexistence of multiple factors, this study proposes a combination of the Data Envelopment Analysis (DEA) method, Grey Analytic Hierarchy Process (G-AHP), and Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS). With the utilized integrated approach, both quantitative and qualitative effective criteria are considered to evaluate locations. DEA is used in the first phase to select high-efficiency locations based on various measurable criteria. In the second phase, these locations are further assessed in terms of qualitative criteria: technical, economic, environmental, and socio-political factors. For this evaluation, G-AHP is used for criteria weighting, and G-TOPSIS ranks the locations. While the proposed approach is expected to help save costs and resources towards determining hot spots for solar power plants installation all around the country, it is recommended that governments and local authorities take into account suitability policies and support mechanisms for sustainable solar energy development.

Assessing Renewable Energy Production Capabilities Using DEA Window and Fuzzy TOPSIS Model

Abstract: Climate change and air pollution are among the key drivers of energy transition worldwide. The adoption of renewable resources can act as a peacemaker and give stability regarding the damaging effects of fossil fuels challenging public health as well as the tension made between countries in global prices of oil and gas. Understanding the potential and capabilities to produce renewable energy resources is a crucial pre-requisite for countries to utilize them and to scale up clean and stable sources of electricity generation. This paper presents a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) in order to evaluate the capabilities of 42 countries in terms of renewable energy production potential. Based on three inputs (population, total energy consumption, and total renewable energy capacity) and two outputs (gross domestic product and total energy production), DEA window analysis chose the list of potential countries, including Norway, United Kingdom, Kuwait, Australia, Netherlands, United Arab Emirates, United States, Japan, Colombia, and Italy. Following that, the FTOPSIS model pointed out the top three countries (United States, Japan, and Australia) that have the greatest capabilities in producing renewable energies based on five main criteria, which are available resources, energy security, technological infrastructure, economic stability, and social acceptance. This paper aims to offer an evaluation method for countries to understand their potential of renewable energy production in designing stimulus packages for a cleaner energy future, thereby accelerating sustainable development.

Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach

Abstract: On the heels of the online shopping boom during the Covid-19 pandemic, the electronic commerce (e-commerce) surge has many businesses facing an influx in product returns. Thus, relevant companies must implement robust reverse logistics strategies to reflect the increased importance of the capability. Reverse logistics also plays a radical role in any business’s sustainable development as a process of reusing, remanufacturing, and redistributing products. Within this context, outsourcing to a third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies for today’s organizations, especially e-commerce players. The objective of this study is to develop a decision support system to assist businesses in the selection and evaluation of different 3PRLPs by a hybrid fuzzy multicriteria decision-making (MCDM) approach. Relevant criteria concerning the economic, environmental, social, and risk factors are incorporated and taken into the models. For obtaining more scientific and accurate ranking results, linguistic terms are adopted to reduce fuzziness and uncertainties of criteria weights in the natural decision-making process. The fuzzy analytic hierarchy process (FAHP) is applied to measure the criteria’s relative significance over the evaluation process. The fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) is then used to rank the alternatives. The prescribed method was adopted for solving a case study on the 3PRLP selection for an online merchant in Vietnam. As a result, the most compatible 3PRLP was determined. The study also indicated that “lead time,” “customer’s voice,” “cost,” “delivery and service,” and “quality” are the most dominant drivers when selecting 3PLRLs. This study aims to provide a more complete and robust evaluation process to e-commerce businesses and any organization that deals with supply chain management in determining the optimized reverse logistics partners.