Logistics of Earthmoving Operations : Simulation and Optimization

Abstract: Earthworks are a fundamental part of heavy construction engineering and involve the moving and processing of the soil surface of earth. Normally, earthmoving operations are carried out during the early stages of heavy construction projects. To a large extent, the success of the fundamental earthmoving determines the sequence of the remaining parts of a project. Furthermore, the operations require expensive heavy equipment as well as manpower. Thus, improving the efficiency of earthmoving operations is a primary target from the point of view of the project management.This thesis develops simulation and optimization methods for logistics of earthmoving operations. Modeling earthmoving operations correctly is essential to ensure the credibility of simulation, and the well-known CYCLONE modeling methodology is employed to represent the earthmoving logistics. Discrete event simulation techniques are used to capture the interaction between resources and the randomness of the earthmoving activities. A prototype has been developed (Paper I) to demonstrate that the capability of the simulation system of evaluating alternative operating strategies and resource utilizations for earthmoving operations at a detailed level, as well as conducting productivity estimation and Total Cost of Ownership (TCO) calculations. The simulation system is then integrated with optimization to solve the optimal fleet selection problem for earthmoving operations (Paper II and III). Two optimization objectives are formulated and solved using the proposed simulation-based optimization framework and a genetic optimization algorithm: TCO minimization and maximization of productivity. The case studies show that the proposed mechanism can effectively allocate an optimal equipment combination for earthmoving operations and hence serve as an efficient tool for construction management. The main aim of the integrated simulation-based optimization platform is to act as a sales tool to help customers optimize their fleet and eventually their sites.In addition to the simulation-based optimization framework for earthmoving logistics, the thesis examines the possibility of reducing fuel consumption for articulated haulers which are the most fuel consuming machines in earthmoving (Paper IV). Fuel consumption has become one of the main focuses for automobile manufacturers and several studies have been carried out over the last years to evaluate the possibility of using topographical information and positioning systems to aid look-ahead control systems for road vehicles. Based on the assumption of available road slope information and positioning system, an optimal control problem is formulated to determine the optimal gear shift sequence and time of shifting. Model Predictive Control algorithms together with Dynamic Programming techniques are employed to solve the optimal gear shifting problem. Computer simulations show that both fuel consumption and travel time can be reduced simultaneously. In addition, the optimal gear shift sequence resembles the behavior of an experienced driver.