Offline and online variants of the Traveling Salesman Problem

In this thesis, we study several well-motivated variants of the Traveling Salesman Problem (TSP). First, we consider makespan minimization for vehicle scheduling problems on trees with release and handling times. 2-approximation algorithms were known for several variants of the single vehicle problem on a path. A 3/2-approximation algorithm was known for the single vehicle problem on a path where there is a fixed starting point and the vehicle must return to the starting point upon completion. Karuno, Nagamochi and Ibaraki give a 2-approximation algorithm for the single vehicle problem on trees. We develop a Polynomial Time Approximation Scheme (PTAS) for the single vehicle scheduling problem on trees which have a constant number of leaves. This PTAS can be easily adapted to accommodate various starting/ending constraints. We then extended this to a PTAS for the multiple vehicle problem where vehicles operate in disjoint subtrees. We also present competitive online algorithms for some single vehicle scheduling problems. Secondly, we study a class of problems called the Online Packet TSP Class (OP-TSP-CLASS). It is based on the online TSP with a packet of requests known and available for scheduling at any given time. We provide a 5/3 lower bound on any online algorithm for problems in OP-TSP-CLASS. We extend this result to the related k-reordering problem for which a 3/2 lower bound was known. We develop a κ+1-competitive algorithm for problems in OP-TSP-CLASS, where a κ-approximation algorithm is known for the offline version of that problem. We use this result to develop an offline m(κ+1)-approximation algorithm for the Precedence-Constrained TSP (PCTSP) by segmenting the n requests into m packets. Its running time is mf(n/m) given a κ-approximation algorithm for the offline version whose running time is f(n)

DroTrack: High-speed Drone-based Object Tracking Under Uncertainty

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202

Real-time extensive livestock monitoring using lpwan smart wearable and infrastructure

Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer’s needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle’s position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

Incorporating A New Class of Uncertainty in Disaster Relief Logistics Planning

In recent years, there has been a growing interest among emergency managers in using Social data in disaster response planning. However, the trustworthiness and reliability of posted information are two of the most significant concerns, because much of the user-generated data is initially not verified. Therefore, a key tradeoff exists for emergency managers when considering whether to incorporate Social data in disaster planning efforts. By considering Social data, a larger number of needs can be identified in a shorter amount of time, potentially enabling a faster response and satisfying a class of demand that might not otherwise be discovered. However, some critical resources can be allocated to inaccurate demands in this manner. This dissertation research is dedicated to evaluating this tradeoff by creating routing plans while considering two separate streams of information: (i) unverified data describing demand that is not known with certainty, obtained from Social media platforms and (ii) verified data describing demand known with certainty, obtained from trusted traditional sources (i.e. on the ground assessment teams). These projects extend previous models in the disaster relief routing literature that address uncertainty in demand. More broadly, this research contributes to the body of literature that addresses questions surrounding the usefulness of Social data for response planning