Fast Ejection Chain Algorithms for Vehicle Routing with Time Windows

This paper introduces new ejection chain strategies to effectively target vehicle routing problems with time window constraints (VRPTW). Ejection chain procedures are based on the idea of compound moves that allow a variable number of solution components to be modified within any single iteration of a local search algorithm. The yardstick behind such procedures is the underlying reference structure, which is the structure that is used to coordinate the moves that are available for the local search algorithm. The main contribution of the paper is a new reference structure that is particularly suited in order to handle the asymmetric aspects in a VRPTW. The new reference structure is a generalization of the doubly rooted reference structure introduced by Glover, resulting in a new, powerful neighborhood for the VRPTW. We use tabu search for the generation of the ejection chains. On a higher algorithmic level, we study the effect of different meta heuristics to steer the tabu chain ejection process. Computational results confirm that our approach leads to very fast algorithms that can compete with the current state of the art algorithms for the VRPTW.operations research and management science;

Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 minutes for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well

Routing problem with service choices

June 1986"--CoverAlso issued as an Ph.D. thesis, Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1986Includes bibliographical references (p. 97-100)This thesis finds solutions to the routing problem with service choices which is formulated as a capacitated minimum cost flow circulation problem with GUB constraints. The routing problem with service choices is solved using a specialized GUB branch and bound algorithm. Methods for node and GUB set selection are presented. A heuristic for finding good feasible solutions to initiate the branch and bound using vehicle size cuts is also derived. Furthermore, a network reduction scheme is formalized to reduce the size of the problem. This reduction is applied between pairs of nodes whose ground arcs have infinite upper-bounds. Initial experiments using the GUB branch and bound on several medium scale test problems appear promising. A variable tracking scheme which updates the status of the branching variables is included, which can be used to fully automate the branch and bound. This work supports the use of LP based GUB branch and bound for solving combinatorial problems with GUB constraints. Extensions to several related problems are also given

Doctor of Philosophy

dissertationThis dissertation aims to develop an innovative and improved paradigm for real-time large-scale traffic system estimation and mobility optimization. To fully utilize heterogeneous data sources in a complex spatial environment, this dissertation proposes an integrated and unified estimation-optimization framework capable of interpreting different types of traffic measurements into various decision-making processes. With a particular emphasis on the end-to-end travel time prediction problem, this dissertation proposes an information-theoretic sensor location model that aims to maximize information gains from a set of point, point-to-point and probe sensors in a traffic network. After thoroughly examining a number of possible measures of information gain, this dissertation selects a path travel time prediction uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework. To better measure the quality of service for ransportation systems, this dissertation investigates the path travel time reliability from two perspectives: variability and robustness. Based on calibrated travel disutility functions, the path travel time variability in this research is represented by its standard deviation in addition to the mean travel time. To handle the nonlinear and nonadditive cost functions introduced by the quadratic forms of the standard deviation term, a novel Lagrangian substitution approach is introduced to estimate the lower bound of the most reliable path solution through solving a sequence of standard shortest path problems. To recognize the asymmetrical and heavy-tailed travel time distributions, this dissertation proposes Lagrangian relaxation based iterative search algorithms for finding the absolute and percentile robust shortest paths. Moreover, this research develops a sampling-based method to dynamically construct a proxy objective function in terms of travel time observations from multiple days. Comprehensive numerical experiment results with real-world travel time measurements show that 10-20 iterations of standard shortest path algorithms for the reformulated models can offer a very small relative duality gap of about 2-6%, for both reliability measure models. This broadly-defined research has successfully addressed a number of theoretically challenging and practically important issues for building the next-generation Advanced Traveler Information Systems, and is expected to offer a rich foundation beneficial to the model and algorithmic development of sensor network design, traffic forecasting and personalized navigation