Efficient algorithms for solving multi-objective optimization and large-scale transportation problems

Abstract

In this thesis, we address two challenges: solving multi-objective integer programs and solving large-scale transportation problems. In Chapter 2, we present a novel fast and robust algorithm for solving bi-objective mixed integer programs that extends and merge ideas from two existing methods: the $\epsilon$-Tabu Method and the Boxed Line Method. In Chapter 3, we study a new service network design problem in which the number of vehicles that can simultaneously load or unload at a hub is limited. We propose a non-trivial integer programming model for solving the problem, and, to be able to solve real-world instances, we design and implement two heuristics: (1) a metaheuristic, and (2) a hybrid matheuristic. In Chapter 4, we introduce a novel incremental network design problem: the \textit{incremental network design problem with multi-commodity flows}. We model the problem as an integer program, propose and analyze greedy heuristics and develop an exact solution approach. We use the proposed methodology to solve instances of the hub capacity expansion problem derived from real-world data from a large package express carrier and we consider a variant of the problem in which temporary capacity expansions are allowed.