Evaluating and Improving the Transport Efficiency of Logistics Operations
Abstract: The thesis focuses on evaluating and improving the transport efficiency of two types of logistics operations in the supply chain.One research area is the production of raw material in heavy construction operations, which takes place early in the construction supply chain. The production of raw material, specifically earthmoving operations, involves the moving and processing of the soil surface. In the thesis, methods and tools are developed to provide decision support in improving the transport efficiency performance of earthmoving operations at the individual equipment level and the systems level. To demonstrate the feasibility of improving the transport efficiency/fuel efficiency at the vehicle level, a fuel consumption optimization problem for construction vehicles is formulated and solved (Paper III). Construction vehicles consume large amounts of fuel due to their large mass and the rough operating environment. Using known road topographical information and a GPS unit, an optimal control problem is solved to determine the optimal gear shift sequence and time of shifting. Simulations show that both fuel consumption and travel time can be reduced simultaneously. For decision support at the systems level, a Fleet Performance Simulation (FPS) model is developed (Paper IV) to evaluate transport efficiency performance for a given mix of construction equipment in an earthmoving operation, in terms of productivity, resource utilization and Total Cost of Ownership (TCO). With a long planning time horizon, the FPS system is integrated with an optimization algorithm to solve the optimal fleet composition problem for earthmoving operations (Paper V & VI). Two optimization problems are formulated and solved using the proposed simulation-based optimization framework: TCO minimization and productivity maximization. Construction operations are highly dynamic and the underlying environment is changing constantly, which brings difficulties in decision-making. Using GPS data from construction vehicles, a map inference framework (Papers I & II) is developed to automatically extract relevant information as input to decision support at the vehicle and systems levels, which include the locations of various workstations, driving time distributions and road networks between workstations. Experiments showed that the framework is able to extract the layout of the construction environment and construct 3D road maps at a high resolution without any prior knowledge of the operating environment. The map inference framework can also be used to automatically update the site geometry as the construction site expands.The second research area is the transport efficiency of the urban distribution system, which is in the final phase of the supply chain. An off-peak delivery pilot project in Stockholm is used as the background, designed to evaluate the potential for commercial vehicles to make use of off-peak hours, from the late evening to the early morning for the delivery of goods. The thesis (Paper VII) evaluates the transport efficiency impacts of the Stockholm off-peak pilot. An evaluation framework is defined where transport efficiency is studied in a number of dimensions, including driving efficiency, delivery reliability, energy efficiency and service efficiency. For each dimension, performance indicators are introduced and evaluated. Vehicle GPS probe data, fleet management data, and logistic information are used to assess the impacts. The results suggest that shifting deliveries from daytime peak hours to night hours achieved better transport efficiency in driving efficiency, delivery reliability and energy efficiency.
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