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.

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.

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.