• Energy Research

In this study, the Transit Network Design Problem (TNDP) is studied to determine the set of routes and frequency on each route for public transportation systems. To ensure the important concerns of planners like route length, route configuration, demand satisfaction, and attractiveness of the transit routes, the TNDP is solved to generate a set of routes by proposing an initial route set generation (IRSG) procedure embedded into the NSGA-II algorithm. The proposed IRSG algorithm aims to produce high-quality initial route set solutions to reach better optimization procedures. Moreover, the Multi-Objective Mixed-Integer Non-Linear Programming (MOMINLP) model is proposed to formulate the frequency setting problem on each route by minimizing the total travel time of passengers (user costs) and operator costs simultaneously, while maximizing the service coverage area near all the bus stops. The MOMINLP model is solved by applying the NSGA-II algorithm to produce a Pareto front between the first and the second objective functions. The model was applied to the Fargo-Moorhead Area (FMA), a small urban area. Results were compared with the existing transit network to measure the efficiency of the NSGA-II solution methodology. The proposed algorithm was found to considerably decrease the total travel time of passengers.

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable Bioethanol Supply Chain Network (SBSCN). We use ML methods, such as Random Forest (RF), Extreme Gradient Boosting Method (XGBoost), and Ensemble learning algorithm (Bagging), to project the bioethanol demand. We select the RF method as a superior method to forecast the bioethanol demand as inputs to the model by comparing the performance criteria for these three methods. We then propose a Mixed-Integer Linear Programming (MILP) model to meet the sustainability criteria defined by three objective functions. We present a case study to demonstrate the applicability of the proposed approach. The sensitivity analysis confirms that the costs of establishing preprocessing and biorefinery centers constitute 37% of the total costs of the network. More importantly, we find that the square bale harvest method is among the methods that utilized the most switchgrass land area. More interestingly, our model determined that the square bale harvest method led to 18,450 tons of switchgrass loss in the case study. Finally, our results can be utilized by policymakers and investors to develop efficient SBSCNs.