Route and speed optimization for autonomous trucks

Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed

Route and speed optimization problems under uncertainty and environmental concerns

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 defined 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.<br/

Solving the Air Conflict Resolution Problem under Uncertainty using an Iterative Bi-Objective Mixed Integer Programming Approach

International audienceIn this paper, we tackle the aircraft conflict resolution problem under uncertainties. We consider errors due to the wind effect, the imprecision on the aircraft speed prediction, and the delay in the execution of maneuvers. Using a geometrical approach, we derive an analytical expression for the minimum distance between aircraft, along with the corresponding probability of conflict. These expressions are incorporated into an existing deterministic model for conflict resolution. This model solves the problem as a maximum clique of minimum weight in a graph whose vertices represent possible maneuvers and where edges link conflict-free maneuvers of different aircraft. We then present a solution procedure focusing on two criteria, namely fuel efficiency and the probability of re-issuing maneuvers in the future: we iteratively generate solutions of the Pareto front to provide the controller with a set of possible solutions where he/she can choose the one corresponding the most to his/her preferences. Intensive Monte-Carlo simulations validate the expressions derived for the minimum distance and the probability of conflict. Computational results highlight that up to 10 different solutions for instances involving up to 35 aircraft are generated within three minutes