Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem

Heavy goods vehicles are vital backbones of the supply chain delivery system but also contribute significantly to carbon emissions with only 60% loading efficiency in the United Kingdom. Collaborative vehicle routing has been proposed as a solution to increase efficiency, but challenges remain to make this a possibility. One key challenge is the efficient computation of viable solutions for co-loading and routing. Current operations research methods suffer from non-linear scaling with increasing problem size and are therefore bound to limited geographic areas to compute results in time for day-to-day operations. This only allows for local optima in routing and leaves global optimisation potential untouched. We develop a reinforcement learning model to solve the three-dimensional loading capacitated vehicle routing problem in approximately linear time. While this problem has been studied extensively in operations research, no publications on solving it with reinforcement learning exist. We demonstrate the favourable scaling of our reinforcement learning model and benchmark our routing performance against state-of-the-art methods. The model performs within an average gap of 3.83% to 8.10% compared to established methods. Our model not only represents a promising first step towards large-scale logistics optimisation with reinforcement learning but also lays the foundation for this research stream

A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

Based on a real-life container transport problem, a model of Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is proposed in this paper. In a wide planning horizon, which is divided into a number of shifts, a fixed number of trucks are scheduled to complete container transportation tasks between terminals subject to time constraints. In this problem, the routes traveled by trucks are open, as returning to the starting depot is not required in every single shift but every two shifts.Our study shows that it is unrealistic to address this large scale and nonlinearly constrained problem with exact search methods. A Reinforcement Learning Based Variable Neighbourhood Search algorithm (VNSRLS) is developed for OPVRPTW. The initial solution is constructed with an urgency level-based insertion heuristic, while different insertion selection strategies are compared. In the local search phase of VNS-RLS, reinforcement learning is used to guide the search, adjusting the probabilities of operators being invoked adaptively according to the change of generated solutions’ feasibility and quality. In addition, the impact of sampling neighbourhood space in single solution-based algorithms is also investigated. Three indicators are designed in the proposed Sampling module to set the starting configuration of local search.Experiment results on different sizes of real and artificial benchmark instances show that, the proposed Sampling scheme and feasibility indicator decrease the infeasible rate during the search. However, Sampling’s contribution to solution quality improvement is not significant in this single solution-based algorithm. Comparing to the exact search and two state-of-the-art algorithms, VNS-RLS produces promising result

Transverse fracture properties of green wood and the anatomy of six temperate tree species

© Institute of Chartered Foresters, 2016. All rights reserved. The aim of this study was to investigate the effect of wood anatomy and density on the mechanics of fracture when wood is split in the radial-longitudinal (RL) and tangential-longitudinal (TL) fracture systems. The specific fracture energies (Gf, J m-2) of the trunk wood of six tree species were studied in the green state using double-edge notched tensile tests. The fracture surfaces were examined in both systems using Environmental Scanning Electron Microscopy (ESEM). Wood density and ray characteristics were also measured. The results showed that Gf in RL was greater than TL for five of the six species. In particular, the greatest degree of anisotropy was observed in Quercus robur L., and the lowest in Larix decidua Mill. ESEM micrographs of fractured specimens suggested reasons for the anisotropy and differences across tree species. In the RL system, fractures broke across rays, the walls of which unwound like tracheids in longitudinal-tangential (LT) and longitudinal-radial (LR) failure, producing a rough fracture surface which would absorb energy, whereas in the TL system, fractures often ran alongside rays

Characteristics of future air cargo demand and impact on aircraft development: A report on the Cargo/Logistic Airlift Systems Study (CLASS) project

Current domestic and international air cargo operations are studied and the characteristics of 1990 air cargo demand are postulated from surveys conducted at airports and with shippers, consignees, and freight forwarders as well as air, land, and ocean carriers. Simulation and route optimization programs are exercised to evaluate advanced aircraft concepts. The results show that proposed changes in the infrastructure and improved cargo loading efficiencies are as important enhancing the prospects of air cargo growth as is the advent of advanced freighter aircraft. Potential reductions in aircraft direct operating costs are estimated and related to future total revenue. Service and cost elasticities are established and utilized to estimate future potential tariff reductions that may be realized through direct and indirect operating cost reductions and economies of scale

PTL: A Model Transformation Language based on Logic Programming

In this paper we present a model transformation language based on logic programming. The language, called PTL (Prolog based Transformation Language), can be considered as a hybrid language in which ATL (Atlas Transformation Language)-style rules are combined with logic rules for defining transformations. ATL-style rules are used to define mappings from source models to target models while logic rules are used as helpers. The implementation of PTL is based on the encoding of the ATL-style rules by Prolog rules. Thus, PTL makes use of Prolog as a transformation engine. We have provided a declarative semantics to PTL and proved the semantics equivalent to the encoded program. We have studied an encoding of OCL (Object Constraint Language) with Prolog goals in order to map ATL to PTL. Thus a subset of PTL can be considered equivalent to a subset of ATL. The proposed language can be also used for model validation, that is, for checking constraints on models and transformations. We have equipped our language with debugging and tracing capabilities which help developers to detect programming errors in PTL rules. Additionally, we have developed an Eclipse plugin for editing PTL programs, as well as for debugging, tracing and validation. Finally, we have evaluated the language with several transformation examples as well as tested the performance with large models