Applying an Ordinal Priority Approach Based Neutrosophic Fuzzy Axiomatic Design Approach to Develop Sustainable Geothermal Energy Source

Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.

Optimization Model in Manufacturing Scheduling for the Garment Industry

The garment industry in Vietnam is one of the country’s strongest industries in the world.However,the production process still encounters problems regarding scheduling that does not equate to an optimal process.The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint.A number of constraints were considered in the model and is applied to a real case study of a factory in order to viewhowthe tardiness and latenesswould be affected which resulted in optimizing the scheduling time better.Specifically,the constraints considered were order assignments,production time,and tardiness with an objective function which is to minimize the total cost of delay.The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given.The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.

A Model for Selecting a Biomass Furnace Supplier Based on Qualitative and Quantitative Factors

In developing countries,solar energy is the largest source of energy,accounting for 35%–45%of the total energy supply.This energy resource plays a vital role in meeting the energy needs of the world,especially in Vietnam.Vietnam has favorable natural conditions for this energy production.Because it is hot and humid,and it has much rainfall and fertile soil,biomass develops very quickly.Therefore,byproducts from agriculture and forestry are abundant and continuously increasing.However,byproducts that are considered natural waste have become the cause of environmental pollution;these include burning forests,straw,and sawdust in the North;and rice husks dumped into rivers and canals in the Mekong Delta region.Biomass energy is provided in a short cycle,is environmentally safe to use and is encouraged by organizations that support sustainable development.Taking advantage of this energy source provides energy for economic development and ensures environmental protection.Due to the abovementioned favorable conditions,many biomass energy plants are being built in Vietnam.Like other renewable energy investment projects,the selection of the construction contractor,the selection of equipment for the installation of the power plant,and the choice of construction site are complex multi-criteria decisions.In this case,decisionmakers must evaluate many qualitative and quantitative factors.These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems,especially in a fuzzy decision-making environment.Therefore,in this study,the authors use a Multi-Criteria Decision-Making(MCDM)model that uses a Fuzzy Analytic Hierarchy Process(FAHP)model and the Combined Compromise Solution(CoCoSo)algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors.Furthermore,the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.

Flow-Shop Scheduling with Transportation Capacity and Time Consideration

Planning and scheduling is one of the most important activity in supply chain operation management.Over the years,there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains.This includes two commonly used methods which are mathematical programming models and heuristics algorithms.Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain.A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between twomachines.The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time,to propose a heuristic optimization algorithm to solve the problem,and to develop an applicable program of the heuristics algorithm.AMixed Integer Programming(MIP)model and a heuristics optimization algorithmwas developed and tested using a randomly generated data set for feasibility.The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

Supplier Selection Fuzzy Model in Supply Chain Management:Vietnamese Cafe Industry Case

Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization.Choosing the right supplier can help businesses increase productivity,competitiveness in the market,and profits without having to lower the quality of the products.However,choosing a supplier is not a simple matter,it requires businesses to consider many aspects about their suppliers.Therefore,the goal of this study is to propose an integrated model consisting of two models:Fuzzy Analytics Network Process(Fuzzy-ANP)model and Weighted Aggregated Sum Product Assessment(WASPAS)to solve the problem above.The Fuzzy-ANP model was developed to evaluate the weightings of the supplier selection criteria,and the WASPAS Model was used to rank the suppliers.An example of supplier selection in the coffee industry in Vietnam was studied to validate the model,namely 5 main criteria,with 16 sub-criteria,and 7 suppliers.The model test results show that the Fuzzy ANP and WASPAS integration model was suitable.In future,these developing models can apply to other industries or integrate with other models.

Optimization Model for Selecting Temporary Hospital Locations During COVID-19 Pandemic

The two main approaches that countries are using to ease the strain on healthcare infrastructure is building temporary hospitals that are specialized in treating COVID-19 patients and promoting preventive measures.As such,the selection of the optimal location for a temporary hospital and the calculation of the prioritization of preventive measures are two of the most critical decisions during the pandemic,especially in densely populated areas where the risk of transmission of the virus is highest.If the location selection process or the prioritization of measures is poor,healthcare workers and patients can be harmed,and unnecessary costs may come into play.In this study,a decision support framework using a fuzzy analytic hierarchy process(FAHP)and a weighted aggregated sum product assessment model are proposed for selecting the location of a temporary hospital,and a FAHP model is proposed for calculating the prioritization of preventive measures against COVID-19.A case study is performed for Ho Chi Minh City using the proposed decision-making framework.The contribution of this work is to propose a multiple criteria decision-making model in a fuzzy environment for ranking potential locations for building temporary hospitals during the COVID-19 pandemic.The results of the study can be used to assist decisionmakers,such as government authorities and infectious disease experts,in dealing with the current pandemic as well as other diseases in the future.With the entire world facing the global pandemic of COVID-19,many scientists have applied research achievements in practice to help decision-makers make accurate decisions to prevent the pandemic.As the number of cases increases exponentially,it is crucial that government authorities and infectious disease experts make optimal decisions while considering multiple quantitative and qualitative criteria.As such,the proposed approach can also be applied to support complex decision-making processes in a fuzzy environment in different countries.

Improving Supply Chain Performance Through Supplier Selection and Order Allocation Problem

Suppliers play the vital role of ensuring the continuous supply of goods to themarket for businesses.If businesses do not maintain a strong bond with their suppliers,they may not be able to secure a steady supply of goods and products for their customers.As a result of failure to deliver products,the production and business activities of the business can be delayed which leads to the loss of customers.Normally,each trading enterprise will have a variety of commodity supply chains withmultiple suppliers.Suppliers play an important role and contribute to the value of the entire supply chain.Should any supplier encounters a problem,the whole supply chain of businesses will be affected and could lead to not guaranteeing the stable supply to the market.Thus,suppliers can be seen as a threat to businesses where they have the ability to increase input prices or decrease the quality of the required products and services they provide.The quantity of the business,and the supply lead time directly affect the operations and reduce the profitability of the business.The paper mainly focuses on the supplier selection problemunder a variety of price level and product families when using a two-phase fuzzy multi-objective linear programming.The objectives of the proposed model are to minimize the total purchasing and ordering cost in order to reduce the quantity of defective materials and the late-delivery components from suppliers.Moreover,the piecewise linear membership function is applied in themodel to determine an optimal solution which is based on the requirement of decision makers under their fuzzy environment.The results of this study can be applied in various business environment and provide a reliable decision tool for choosing potential suppliers relating to these objectives.Based on the results,the company canmake a good decision on supplier selection;therefore,the company can improve the quality and quantity of their final product.This is because,the best supplier can supply raw material using just-in-time application and reduce production risk on the manufacturing process.

Sustainable Supplier Selection Model in Supply Chains During the COVID-19 Pandemic

As global supply chains become more developed and complicated,supplier quality has become increasingly influential on the competitiveness of businesses during the Covid-19 pandemic.Consequently,supplier selection is an increasingly important process for any business around the globe.Choosing a supplier is a complex decision that can result in lower procurement costs and increased profits without increasing the cost or lowering the quality of the product.However,these decision-making problems can be complicated in caseswithmultiple potential suppliers.Vietnam’s textile and garment industry,for example,has made rapid progress in recent years but is still facing great difficulties as the supply of raw materials and machinery depends heavily on foreign countries.Therefore,it is extremely important for textile and garment manufacturing companies in Vietnam to implement an effective supplier evaluation and selection process.While multicriteria decision-making models are frequently employed to assist with supplier evaluation and selection problems,few of these models consider the problem under the condition of a fuzzy decision-making environment.The aim of this paper is to create a hybrid MCDM model using the Fuzzy Analytical Hierarchy Process(FAHP)model and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)to assist the supplier selection process in the garment industry in a fuzzy decision-making environment.In this study,the FAHP method is used to evaluate the performance and the weight of each criterion.TOPSIS is then used to rank all potential suppliers.The proposed model is then applied to a real-world case study to demonstrate both the process of calculation as well as its real-world applicability.The results from the case study provide empirical evidence that the model is feasible.The proposed approach can also be used in combination with other MCDM models to better support decision makers and can be modified to be applied in similar supplier selection processes for different industries.

Two-Stage Production Planning Under Stochastic Demand:Case Study of Fertilizer Manufacturing

Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stage 1.Second,the thesis pays attention tomaximizing total profit based on information from customer demand,as well as being informed regarding concerns about system cost at Stage 2.

Fuzzy MCDM for Improving the Performance of Agricultural Supply Chain

Fertilizer industry in Vietnam and globally have entered the saturation phase.With the growth rate slowing down,this poses challenges for the development impetus of the fertilizer industry in the next period.In fact,over the past few decades,Vietnam’s crop industry has abused excessive investment in chemical fertilizers,with organic fertilizers are rarely used or not at all,limiting crop productivity,increasing pests and diseases.To develop sustainable agriculture,Vietnam’s crop industry must limit the use of chemical fertilizers,increase the use of environmentally friendly organic and natural mineral fertilizers to produce clean agricultural products which is safe.Therefore,it is necessary to consider and choose the right supplier to ensure the goal of sustainable development.Spherical Fuzzy Analytic Hierarchy Process(SF-AHP),and the combinative distance-based assessment(CODAS)are new Multicriteria Decision Making(MCDM)method which can be used to solve supplier selection problem.This paper uses an effective solution based on a combined the concept of triple bottom line(TBL),SF-AHP and CODAS approach to help agriculture companies that need to select the best fertilizer supplier.This research can support supply chain managers to achieve supply chain systems that reduce not only sourcing costs,but also develop sustainable agriculture.

Fuzzy Multi-Criteria Decision Making for Solar Power Plant Location Selection

Vietnam is one of Southeast Asian countries with a rapid GDP growth rate,ranging from 6.5%to 7%annually,leading to an average increase in energy demand of 11%per year.This demand creates many new opportunities in the energy industry,especially renewable energy,to ensure sustainable development in the future for the country with applications of solar energy growing at the present,and other opportunities to expand in the future.In Vietnam,thanks to favorable weather,climate,terrain characteristics and many preferential support policies,there are many great opportunities in the field of solar energy exploitation and application.Location selection is an important problem in all renewable energy projects.Therefore,the author proposed a fuzzy Multi-criteria Decision-Making Model(MCDM)model for solar power plant location selection in this study,and as a result,location 5 is the optimal solution.The contribution of this study is to propose a MCDM for solar power plant location selection in Vietnam under fuzzy environmental conditions.

Fuzzy Multi-criteria Decision Making for Decision Support in Port Capacity Upgrade

In many port capacity upgrade projects,choosing a supplier of equipment is a complicated decision,project managers must consider many criteria to choose a supplier to ensure the project is completed on time,optimal in terms of benefit and cost.Therefore,selecting the equipment supplier in this project is a multi-criteria decision-making process.The multicriteria decision-making(MCDM)model is applied in many fields to select the optimal solution,but there are very few studies using the MCDM model to support project managers in evaluating and selecting optimal solutions in port capacity upgrade project.In this research,the authors combine Fuzzy Analytic Network Process model and Weighted Aggregated Sum Product Assessment concepts to develop a decision support system in port capacity upgrade project.The scientific and practical contribution of this study is to successfully propose a decision support model in a fuzzy environment.The results of the study will be a useful guideline to assist decision makers in port capacity upgrading projects in Taiwan as well as in other countries around the world.

Assessing Asian Economies Renewable Energy Consumption Efficiency Using DEA with Undesirable Output

Along with vast non-fossil potential and significant expertise,there is a question of whether Asian nations are attaining efficient consumption and exploitation of renewable resources.From this perspective,the paper aims to evaluate the efficiency of 14 potential Asia countries in renewable energy consumption during the six-year periods(2014-2019).In analyzing the performance of the renewable energy sector,the data envelopment analysis(DEA)with an undesirable output model approach has been widely utilized to measure the efficiency of peer units compared with the best practice frontier.We consider four inputs and two outputs to a DEA-based efficiency model.Labor force,total energy consumption,share of renewable energy,and total renewable energy capacity are inputs.The outputs consist of CO_(2)emissions as an undesirable output and gross domestic product as a desirable output.The results show that United Arab Emirates,Saudi Arabia,Japan,and South Korea consistently outperform in the evaluation,achieving perfect efficiency scores during the research period.Uzbekistan is found to have the lowest average efficiency of renewable energy utilization.

Heuristic Scheduling of Job Orders in a Build-to-Order Manufacturing System

With the continuous development of technology,traditional manual work has been becoming more and more automated.Most large or medium-sized companies have applied Enterprise Resource Planning(ERP)software into their business and production activities.However,since many small firms cannot afford ERP because of its expensive cost,they often still employ manual work for the same tasks this software resolves,especially for scheduling.This paper aims to provide a possible solution for small businesses to try automated scheduling and discover whether it can help much.There are two main ways to make this determination:a mathematical model and a heuristic model,which are suitable for assessing low-and medium-sized workloads,respectively.This case study was carried out in a small domestic interior furniture company,particularly in scheduling for their customized products in two-stage flow shop.Normally,they produce according to the sequence of customers’orders.However,when we applied these supportive tools with batch-processing machines,they experienced enhanced production performance due to diminishing setup time for distinctive items and a more streamlined arrangement of job sequences.These changes were implemented for some small companies that do not use many production stages and have a suitable number of jobs and customers.If this method were applied to larger demands,it would need further improvement and development to become a complete tool that can perform like a part of an ERP system.

Scheduling Optimization Modelling:A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules,including coordinating and assigning activities to each person,group of people,or machine and arranging work orders in each workplace.Production scheduling must solve all problems such as minimizing customer wait time,storage costs,and production time;and effectively using the enterprise’s human resources.This paper studies the application of flexible job shop modelling on scheduling a woven labelling process.The labelling process includes several steps which are handled in different work-stations.Each workstation is also comprised of several identical parallel machines.In this study,job splitting is allowed so that the power of work stations can be utilized better.The final objective is to minimize the total completion time of all jobs.The results show a significant improvement since the new planning may save more than 60%of lead time compared to the current schedule.The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process.The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries.A practical case study demonstrates the effectiveness of the proposed model.

Scheduling Flexible Flow Shop in Labeling Companies to Minimize the Makespan

In the competitive global marketplace,production scheduling plays a vital role in planning in manufacturing.Scheduling deals directly with the time to output products quickly and with a low production cost.This research examines case study of a Radio-Frequency Identification labeling department at Avery Dennison.The main objective of the company is to have a method that allows for the sequencing and scheduling of a set of jobs so it can be completed on or before the customer’s due date to minimize the number of late orders.This study analyzes the flexible flow shop scheduling problem with a sequence dependent setup by modifying the processing time and setup time to minimize the makespan on multiple machines.Based on the defined mathematical model,this study includes an alternative approach and application of heuristic algorithm with the input being big data.Both optimization programs are used in this study and compared to determine which method can better solve the company’s problems.The proposed algorithm is able to improve machine utilization with large-scale problems.