Location Optimization with Uncertainty for Industrial Project Using Discrete Block Model and Spatial Meshing Algorithm

Abstract

The layout of industrial construction projects is crucial in meeting production requirements while minimizing operational costs. The purpose of this study is to answer the question of whether the proposed discrete block model and spatial meshing (DBSM) algorithm can improve transportation efficiency and reduce total direct transportation costs. The DBSM algorithm was implemented in 20 different investigation scenarios that considered the uncertainty in the model variables for a construction materials production company based on its geological, transportation, and production data. These analytical scenarios varied the grid sizes of spatial meshing, vertical distance of computational topology blocks, and practical transportation distance at each dimension increment throughout the initial design and planning period. The algorithm was then validated by statistically comparing its performance with that of a contemporary location-selection model that does not consider three-dimensional (3D) spiral transportation routes and differential material quality while maximizing the model performance. The DBSM algorithm outperformed the existing model due to its ability to select an optimized location. The algorithm resulted in a reduced total weighted sum of distances (WSD) for all trucks and an annual savings of 1.62% on direct transportation costs. This research contributes a new method that integrates spatial block modeling and location meshing to solve the construction-materials stationary plant site-selection problem with uncertain routes and dynamic loading points. It is an effective and practical method of location optimization for real-world production problems.