Supply chain model for renewable energy resource from biomass in Vietnam

Duc, Duy Nguyen; Nananukul, Narameth

doi:10.1109/iea.2018.8387156

Cited by 8 publications

(11 citation statements)

References 17 publications

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“…The plant can keep biomass as inventory for use in the future, with a specified holding cost for each type of biomass. More details on the biomass supply chain model can be found in Duc and Nananukul [16]. The electricity demand in each period must be satisfied, and there is a penalty cost for lost demand.…”

Section: Problem Statementmentioning

confidence: 99%

“…The stochastic model is extended from Duc and Nananukul [16], who considered uncertainty In addition, different types of biomass have different characteristics. For example, heat value is one important factor that impacts the amount of energy biomass can generate.…”

Section: Stochastic Optimization Modelmentioning

confidence: 99%

“…The stochastic model is extended from Duc and Nananukul [16], who considered uncertainty from supplier capacity; note that the percentage used to define the values of supplier capacity for low and high scenarios is 10%. The probability of scenarios for uncertain supplier capacity is the same as the probability of scenarios for demand.…”

Section: Stochastic Optimization Modelmentioning

confidence: 99%

“…The biomass plant capacity was assumed to be 540,000 MWh per month with fixed cost equal to 694 million United States Dollar (USD). More details of how the relevant data for the model were prepared can be found in [16].…”

Section: Case Studymentioning

confidence: 99%

“…The first is based on a stochastic optimization model. The proposed model is extended from Duc and Nananukul [16], where uncertainty from supplier capacity is considered. The second is based on a solution methodology from a simulation method called parameter optimization, which is used for optimizing a set of parameters used in the simulation model.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Methodologies for Biomass Supply Chain Planning

Duc

Nananukul

2019

Processes

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Renewable energy resources have received increasing attention due to environmental concerns. Biomass, one of the most important renewable energy resources, is abundant in agricultural-based countries. Typically, the biomass supply chain is large due to the huge amount of relevant data required for building the model. As a result, using a standard optimization package to determine the solution for the biomass supply chain model might not be practical. In this study, the focus is on developing and applying advanced methodologies that can be used to determine a solution for the biomass supply chain model efficiently. The decisions related to plant selection, and distribution of biomass from suppliers to plants require optimization. The methodologies considered in this research are based on stochastic programming, parameter search, and simulation-based optimization. Computational results and managerial insights based on case studies from different regions of Vietnam are provided. The results show that parameter search is suitable for small problems only, while stochastic programming is suitable for small and medium problems. For large problem, simulation-based optimization performs better when considering the quality of the solution and the run time, although, this method does not guarantee an optimal solution. It provides good solutions where the gaps to the optimal solutions are between 0.59% and 8.41%.

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Section: Problem Statementmentioning

confidence: 99%

Section: Stochastic Optimization Modelmentioning

confidence: 99%

Section: Stochastic Optimization Modelmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Methodologies for Biomass Supply Chain Planning

Duc

Nananukul

2019

Processes

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A decision-support system to assist forest industry planning and investment when data are scarce: the case of hardwood veneering in subtropical eastern Australia

Venn

McGavin

2021

Australian Forestry

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A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem

Duc

Nananukul

2020

J. Phys.: Conf. Ser.

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This paper presents an advanced methodology that integrates a machine learning methodology into an optimization process. The framework of an interactive machine learning algorithm was developed to meet the challenges in solving large-scale optimization problems. An artificial neural network (ANN) is used with the knowledge gained from solving previous problems with different scenarios to define a good starting point for a solution searching process. By using an initial solution, known as “warm start”, the search space can be reduced to get more opportunity to find an optimal solution. The applicability of the proposed method was evaluated by using it to determine the optimal facility locations for a biomass supply chain problem using a real case study from Central Vietnam. The supply chain planning model is based on an optimization model, where the goal is to maximize the benefits from meeting the electricity demand minus the total cost from facility cost, penalty cost from lost demand, and operational costs form the supply chain. The structure of the ANN, the number of intermediate layers and the number of processing nodes, was determined by comparing the accuracy from different configurations. The ANN with two intermediate layers possesses the best performance from the training and testing datasets. The proposed model succeeded in predicting the facility location with more than 98% prediction accuracy. The results from our framework provide optimal solutions while saving runtime.

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Supply chain model for renewable energy resource from biomass in Vietnam

Cited by 8 publications

References 17 publications

Advanced Methodologies for Biomass Supply Chain Planning

Advanced Methodologies for Biomass Supply Chain Planning

A decision-support system to assist forest industry planning and investment when data are scarce: the case of hardwood veneering in subtropical eastern Australia

A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem

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