In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.

During an emergency evacuation, most people will use their vehicles to evacuate. However, there is a group of people who do not have access to reliable transportation or for some reason cannot drive, even if they have their own automobiles - the carless. There are different groups of carless (disabled, medically homebound, poor or immigrant populations, etc.) who require different forms of transportation assistance during an emergency evacuation. In this study we focus on those carless who are physically intact and able to walk to a set of designated locations for transportation during an emergency, and we propose using public transit and school buses to evacuate this carless group. A model has been developed to accommodate the use of public transit and school buses to efficiently and effectively evacuate the carless. The model has two parts. Part 1 is a location problem which aims at congregating the carless at some specific locations called evacuation sites inside the affected area. To achieve this goal, the affected area is partitioned into zones and this congregating of the carless has been formulated as a Single Source Capacitated Facility Location Problem. Changes in the demand of the carless in zones over different periods of a day and over different days of the week have been considered and included in the model. A walking time constraint is explicitly considered in the model. A heuristic developed by Klincewicz and Luss (1986) has been used to solve this location model.Part 2 is a routing problem which aims at obtaining itineraries of buses to pick up the carless at evacuation sites and transport them to safe locations outside the affected area, such that the total number of carless evacuated with the given time limit is maximized. A Tabu search heuristic has been developed for solving the routing problem. Computational results show that the Tabu search heuristic efficiently and effectively solves the routing problem; in particular, the initial heuristic produces a high quality initial solution in very short time. This study has also made slight contribution to the development of the Tabu search technique.

In this paper,we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP).The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm out performs existing solution methods for the 2E-VRP and achieves excellent results on the LRP.

Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.