DOI: 10.14457/tu.the.2020.1002
title: Advanced methodologies for biomass supply chain planning

description: 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. In this dissertation, a supply chain planning model for determining the optimal biomass plant locations is formulated as mixed integer programming. The objective is to maximize the benefits by simultaneously considering transportation cost, purchasing cost, opening fixed cost, inventory cost, penalty cost for lost demand, and uncertainty from factors such as supply capacity and electricity demand. 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 such as IBM ILOG CPLEX optimization studio to determine the solution for the biomass supply chain model might not be practical.This study not only focuses on mathematical model but also develops and applies 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. A simulation was built in Matlab Simulink to analyze and evaluate the dynamic behavior of supply chain systems. One of the characteristics of the simulation model is strongly related to assumption and ruleset. Therefore, to overcome this disadvantage, parameter optimization method was applied by using parameter setting built-in function in Matlab Simulink to improve the system parameters. In addition, parameter optimization is only suitable for a small-scale problem that considers a limited number of parameters. Therefore, a combination of optimization and simulation methods, the hybrid simulation-based optimization (hybrid) method, was developed. The hybrid method has the advantages of simulation and optimization methods and the ability to solve large-scale problems within practical computation time. The proposed method consists of two models, a simulation which was built in Matlab and an optimization model was formulated as mixed-integer programming in OPL CPLEX. The simulation model can represent uncertainty from different factors and evaluate the performance of the system, while the optimization model can optimize system performance based on the required operational constraints. The interaction between the two models continues iteratively until the stopping condition is satisfied. Although simulation-based optimization does not guarantee an optimal solution, it greatly performs when considering the quality of the solution and the run time for large instances. Methods can generate the solutions with the gaps to the optimal solutions are between 0.59% and 8.41%. Besides, the results such as the biomass plant and supplier locations, the size of supplier capacity, etc. were visualized by using Microsoft Power BI.This second part research focuses on a framework that uses machine learning methodologies based on ANN, and the Bayesian network for solving a biomass supply chain network optimization problem was developed. From the data which come from solving the previous related problems, three machine learning models: ANNs, Naïve Bayes, and Bayesian networks were developed to predict the factory locations. Then, factory locations were fed to optimization (CPLEX) as initial search points. As a result, good quality solutions can be determined with practical run time and computer capacity. ANN models were built by using Matlab while Naïve Bayes and the Bayesian network were generated by using “bnlean” library in R -studio. ANN model outperformed the others based on the accuracy, however, when ANN is used, decision-makers could not explore the relationship between input and output factors. As a result, it is hard to provide causal relationship analysis from the results. On the other hand, the Bayesian network could provide a causal relationship among variables and target outputs within a Bayesian network structural diagram. One of the disadvantages of Bayesian networks is that some directed edges between nodes in a Bayesian structural network could be wrong, thus leading to a wrong causal relationship. The results from the machine learning models could be further improved by applying the “Warm start” and RINS approach. The applicability of the proposed approach was evaluated with a case study that considers biomass supply chain planning in Central Vietnam region. The results from the proposed framework shows that the optimal biomass plan for biomass supply chain can be determined with accuracy up to 96% with a decrease in runtime 37.19% on average.