Simulating Transport Capacity, Delivery Speed, and Routing Efficiency to Predict Economic Growth

This study uses a simulation-based approach to investigate the impact of delivery delays due to constraints on transport capacity, transit speed, and routing efficiencies on an economy with various levels of interdependency among firms. The simulation uses object-oriented programming to create specialized production, consumption, and transportation classes. A set of objects from each class is distributed randomly on a 2D plane. A road network is then established between fixed objects using Prim’s MST (Minimum Spanning Tree) algorithm, followed by construction of an all-pair shortest path matrix using the Floyd Warshall algorithm. A genetic algorithm-based vehicle routing problem solver employs the all-pair shortest path matrix to best plan multiple pickup and delivery orders. Production units utilize economic order quantities (EOQ) and reorder points (ROP) to manage inventory levels. Hicksian and Marshallian demand functions are utilized by consumption units to maximize personal utility. The transport capacity, transit speed, routing efficiency, and level of interdependence serve as 4 factors in the simulation, each assigned 3 distinct levels. Federov’s exchange algorithm is used to generate an orthogonal array to reduce the number of combination replays from 34 to just 9. The simulation results of a 9-run orthogonal array on an economy with 6 mining facilities, 12 industries, 8 market centers, and 8 transport hubs show that the level of firm interdependence, followed by transit speed, has the most significant impact on economic productivity. The principal component analysis (PCA) indicates that interdependence and transit speed can explain 90.27% of the variance in the data. According to the findings of this research, a dependable and efficient regional transportation network among various types of industries is critical for regional economic development.

Location Optimization of a Coal Power Plant to Balance Costs against Plant’s Emission Exposure

The goal of the research is to develop a methodology to minimize the public’s exposure to harmful emissions from coal power plants while maintaining minimal operational costs related to electric distribution losses and coal logistics. The objective is achieved by combining EPA Screen3, ISC3 and Japanese METI-LIS model equations with minimum spanning tree (MST) algorithm. Prim’s MST algorithm is used to simulate an electric distribution system and coal transportation pathways. The model can detect emission interaction with another source and estimate the ground level concentrations of emissions up to distances of 25 kilometers. During a grid search, the algorithm helps determine a candidate location, for a new coal power plant, that would minimize the operational cost while ensuring emission exposure is below the EPA/NIOSH thresholds. The proposed methodology has been coded in form of a location analysis simulation. An exhaustive search strategy delivers a final candidate location for a new coal power plant to ensure minimum operational costs as compared to the random or greedy search strategy. The simulation provides a tool to industrial zone planners, environmental engineers, and stakeholders in coal-based power generation. Using operational and emissions perspectives, the tool helps ascertain a list of compromise locations for a new coal power plant facility.