Date of Award

Spring 5-2023

Document Type


Degree Name

Doctor of Philosophy (PhD)


Engineering Management & Systems Engineering


Engineering Management and Systems Engineering

Committee Director

Ghaith Rabadi

Committee Member

Resit Unal

Committee Member

Andrew Collins

Committee Member

Mamadou Seck


The transportation sector is a major greenhouse gas emitter that is heavily regulated to reduce its dependence on oil. These regulations along with the growing customer awareness of global warming have led to the investigation of new transportation problems that consider using eco-friendly vehicle fleets. Promising alternatives to traditional fleets include alternative fuel vehicles (AFVs) and electric vehicles (EVs). These twenty-first-century vehicles offer an appealing advantage of consistently reducing their environmental impact, but due to the current technology, they exhibit bothersome limitations. The short driving range along with limited charging infrastructure may consequently cause issues related to range anxiety, i.e., the fear of not having enough battery charge to reach the desired destination (or the nearest charging station). Fortunately, range anxiety can be effectively mitigated by careful route planning; for example, by planning in advance when and where to recharge the battery so that the total energy consumption and the risk of battery depletion are minimized.

The last decade witnessed the investigation of many new models, formulations and solution approaches pertaining to green logistics. In this dissertation we investigated two variants of electric vehicle routing problem; namely, the Capacitated Electric Vehicle Routing Problem (C-EVRP) and the Electric Vehicle Routing Problem with Time Windows (EVRPTW). C-EVRP is a variant of the standard Capacitated Vehicle Routing Problem (CVRP), where each vehicle is powered exclusively by electricity stored in its rechargeable battery. We propose three exact approaches for solving C-EVRP. The first requires solving a compact polynomial-sized formulation, while the second is a branch-and-cut algorithm. An original feature of this algorithm is that it includes an exact separation procedure for the so-called rounded capacity constraints that is proposed for the first time in the literature. Finally, the third approach is a hybrid algorithm that requires solving an augmented variant of the compact formulation. We provide evidence that the proposed algorithms are able to solve medium-to-large size instances optimally while requiring moderate CPU times. In the case of EVRPTW, where customers should be visited only during fixed time windows, we propose a novel hybrid variable neighborhood search/tabu search metaheuristic making use of a wide range of classical and large neighborhood search operators. Moreover, our algorithm allows visiting infeasible solutions, which is achieved by means of an extended objective function for the evaluation of both feasible and infeasible solutions. In the numerical studies we demonstrate the strong performance of our metaheuristic and show that our algorithm is competitive when compared to existing methods.


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