Energy-Efficient Timely Transportation of Long-Haul Heavy-Duty Trucks

Abstract

We consider a timely transportation problem where a heavy-duty truck travels between two locations across the national highway system, subject to a hard deadline constraint. Our objective is to minimize the total fuel consumption of the truck, by optimizing both route planning and speed planning. The problem is important for cost-effective and environment-friendly truck operation, and it is uniquely challenging due to its combinatorial nature as well as the need of considering hard deadline constraint. We first show that the problem is NP-complete; thus exact solution is computational prohibited unless P = NP. We then design a fully polynomial time approximation scheme (FPTAS) to solve it. While achieving highly-preferred theoretical performance guarantee, the proposed FPTAS still suffers from long running time when applying to national-wide highway systems with tens of thousands of nodes and edges. Leveraging elegant insights from studying the dual of the original problem, we design a heuristic with much lower complexity. The proposed heuristic allows us to tackle the energy-efficient timely transportation problem on large-scale national highway systems. We further characterize a condition under which our heuristic generates an optimal solution. We observe that the condition holds in most of practical instances in numerical experiments, justifying the superior empirical performance of our heuristic. We carry out extensive numerical experiments using real-world truck data over the actual U.S. highway network. The results show that our proposed solutions achieve 17% (resp. 14%) fuel consumption reduction, as compared with a fastest path (resp. shortest path) algorithm adapted from common practice.

Forecasting Nonlinear Systems with LSTM: Analysis and Comparison with EKF

“…The encoder and decoder are composed of LSTM recursive structures. Using LSTM layers, it is possible to extract long-term and non-Markovian trends and show their potential in estimation problems [ 16 , 33 , 34 , 35 ]. However, other types of dynamic systems have other particular conditions of information or number of measured states, and the architecture proposed in [ 19 ] does not have to be suitable with all systems; thereby, Algorithm 2 proposes a configuration method to adapt [ 19 ]’s neural architecture to a specific case.…”

E2Pilot: An energy-efficient navigation system for long-haul timely truck transportation

“…Request permissions from permissions@acm.org. e-Energy '22, June 28-July Timely Transportation (E2T2) problem [3,4]. E2T2 aims to minimize the overall energy consumption for a truck traveling over the national highway network from the origin to the destination via optimizing path planning and speed planning, subject to a strict deadline constraint.…”

“…E2T2 aims to minimize the overall energy consumption for a truck traveling over the national highway network from the origin to the destination via optimizing path planning and speed planning, subject to a strict deadline constraint. Previous works [3,4] show that a well-designed operating strategy can significantly reduce the energy consumption of heavy-duty trucks, as much as 20%.…”

A Novel Dynamic Programming Approach to the Train Marshalling Problem

“…Inexact techniques, on the other hand, can find reasonable suboptimal solutions in polynomial time. Inexact methods themselves can be grouped into two families: approximation algorithms, which guarantee to return a (suboptimal) solution that is within a certain factor of the optimal solution (see, e.g., [18], [19]), and heurstic/metahueristic approaches, which do not offer any performance guarantee but have been found to be very successful in solving NPhard problems (see, e.g., [3], [12], [13], [20]- [23]). It should be noted that some tractable cases of the TMP have been identified in the literature [24], [25].…”