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Title: Route and speed optimization problems under uncertainty and environmental concerns
Author: Nasri, Moncef Ilies
ISNI:       0000 0004 7656 2413
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2018
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This thesis studies logistics problems with the overall aim to reduce the emission of greenhouse gases. These problems are formalized, modeled and solved to derive useful insight for both logistics companies and policy makers. Chapter 1 introduces the background, presents the research aims and objectives as well as the research context. Chapter 2 studies The Pollution-Routing Problem under traffic uncertainty. The problem assumes uncertain traffic conditions and aims at reducing the cost of emissions, fuel consumption and travel times. Stochastic programming has been used to propose new mathematical models capable of considering traffic conditions as a discrete set of random scenarios. Extensive computational experiments are carried out, to quantify the savings yielded by the stochastic approach over a deterministic approach, and by controlling speed. Chapter 3 reconsiders the problem dened in Chapter 2. However, instead of solving it with commercial solvers, new solution techniques based on decomposition, and more precisely integer L-shaped algorithm that uses cuts, lower-bounds and local-branching are proposed. Chapter 4 focuses on the speed optimization problem that consists of choosing the optimal speed on each leg of a given vehicle route represented by a fixed sequence of customers. The objective function accounts also for the pollution emitted by the vehicles. Each customer in the sequence has a service time window. Early and late starts of service are allowed, but at the expense of penalties. A natural model of the problem in the form of a non-linear program is presented, which is then linearized in several ways. Several algorithms are described based on the use of time-space networks. Managerial insight is derived for maritime and road transportation. Chapter 5 concludes by summarizing the key findings and contributions of this thesis, discusses the limitations of this work and suggests future directions of research.
Supervisor: Bektas, Tolga Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available