Real-Time Optimization for Dynamic Ride-Sharing

Throughout the last decade, the advent of novel mobility services such as ride-hailing, car-sharing, and ride-sharing has shaped urban mobility. While these types of services offer flexible on-demand transportation for customers, they may also increase the load on the, already strained, road infrastructure and exacerbate traffic congestion problems. One potential way to remedy this problem is the increased usage of dynamic ride-sharing services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously. This leads to more efficient vehicle utilization, reduced prices for customers, and less traffic congestion at the cost of slight delays compared to direct transportation in ride-hailing services. In this thesis, we consider the planning and operation of such dynamic ride-sharing services. We present a wider look at the planning context of dynamic ride-sharing and discuss planning problems on the strategical, tactical, and operational level. Subsequently, our focus is on two operational planning problems: dynamic vehicle routing, and idle vehicle repositioning. Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing and present a solution procedure. Our algorithmic approach consists of two phases: a fast insertion heuristic, and a local search improvement phase. The former handles incoming trip requests and quickly assigns them to suitable vehicles while the latter is responsible for continuously improving the current routing plan. This way, we enable fast response times for customers while simultaneously effectively utilizing available computational resources. Concerning the idle vehicle repositioning problem, we propose a mathematical model that takes repositioning decisions and adequately reflects available vehicle resources as well as a forecast of the upcoming trip request demand. This model is embedded into a real-time planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an adaptive parameter calculation process, our algorithm dynamically adapts to changes in the current system state. To evaluate our algorithms, we present a modular simulation-based evaluation framework. We envision that this framework may also be used by other researchers and developers. In this thesis, we perform computational evaluations on a variety of scenarios based on real-world data from Chengdu, New York City, and Hamburg. The computational results show that we are able to produce high-quality solutions in real-time, enabling the usage in high-demand settings. In addition, our algorithms perform robustly in a variety of settings and are quickly adapted to new application settings, such as the deployment in a new city

Adaptive forecast-driven repositioning for dynamic ride-sharing

In dynamic ride-sharing systems, intelligent repositioning of idle vehicles often improves the overall performance with respect to vehicle utilization, request rejection rates, and customer waiting times. In this work, we present a forecast-driven idle vehicle repositioning algorithm. Our approach takes a demand forecast as well as the current vehicle fleet configuration as inputs and determines suitable repositioning assignments for idle vehicles. The core part of our approach is a mixed-integer programming model that aims to maximize the acceptance rate of anticipated future trip requests while minimizing vehicle travel times for repositioning movements. To account for changes in current trip demand and vehicle supply, our algorithm adapts relevant parameters over time. We embed the repositioning algorithm into a planning service for vehicle dispatching. We evaluate our forecast-driven repositioning approach through extensive simulation studies on real-world datasets from Hamburg, New York City, Manhattan, and Chengdu. The algorithm is tested assuming a perfect demand forecast and applying a naïve forecasting model. These serve as an upper and lower bound on state-of-the-art forecasting methods. As a benchmark algorithm, we utilize a reactive repositioning scheme. Compared to this, our forecast-driven approach reduces trip request rejection rates by an average of 3.5 percentage points and improves customer waiting and ride times

A Digital Measuring and Load Planning System for Large Transport Assets

Recently, the efforts involved in the digitization and digitalization of logistics processes have grown tremendously. In line with such efforts, we investigate the potential of the process-integrated measuring and load planning of large transport assets. More precisely, considering the case of a German timber processor and retailer, we implement a digital measuring system, which performs precise measuring of regularly shaped wooden assets. The cognitive system uses laser and vision sensors, and measurements can be performed during the asset’s transportation on a forklift. The resulting data can be used to conduct a comprehensive load planning for scheduled delivery tours. The performance of our measurement system is evaluated using a small example dataset of the use case at hand. The a-priori set goal of maximum deviations of 5 cm, 7 cm and 14 cm in height, width and length, respectively, are achieved in 89% of the test cases. The proposed load planning algorithm is integrated in a commercial tour planning service to verify the feasibility of serving several customers within the same tour. We present the method’s applicability to our described use case of integrated measurement and planning

A Digital Measuring and Load Planning System for Large Transport Assets

Recently, the efforts involved in the digitization and digitalization of logistics processes have grown tremendously. In line with such efforts, we investigate the potential of the process-integrated measuring and load planning of large transport assets. More precisely, considering the case of a German timber processor and retailer, we implement a digital measuring system, which performs precise measuring of regularly shaped wooden assets. The cognitive system uses laser and vision sensors, and measurements can be performed during the asset’s transportation on a forklift. The resulting data can be used to conduct a comprehensive load planning for scheduled delivery tours. The performance of our measurement system is evaluated using a small example dataset of the use case at hand. The a-priori set goal of maximum deviations of 5 cm, 7 cm and 14 cm in height, width and length, respectively, are achieved in 89% of the test cases. The proposed load planning algorithm is integrated in a commercial tour planning service to verify the feasibility of serving several customers within the same tour. We present the method’s applicability to our described use case of integrated measurement and planning

A Digital Measuring and Load Planning System for Large Transport Assets

Recently, the efforts involved in the digitization and digitalization of logistics processes have grown tremendously. In line with such efforts, we investigate the potential of the process-integrated measuring and load planning of large transport assets. More precisely, considering the case of a German timber processor and retailer, we implement a digital measuring system, which performs precise measuring of regularly shaped wooden assets. The cognitive system uses laser and vision sensors, and measurements can be performed during the asset’s transportation on a forklift. The resulting data can be used to conduct a comprehensive load planning for scheduled delivery tours. The performance of our measurement system is evaluated using a small example dataset of the use case at hand. The a-priori set goal of maximum deviations of 5 cm, 7 cm and 14 cm in height, width and length, respectively, are achieved in 89% of the test cases. The proposed load planning algorithm is integrated in a commercial tour planning service to verify the feasibility of serving several customers within the same tour. We present the method’s applicability to our described use case of integrated measurement and planning