Solving arc routing problems for winter road maintenance operations

For winter road maintenance, a fleet of snowplow trucks is operated by government agencies to remove snow and ice on roadways and spread materials for anti-icing, de-icing, or increasing friction. Winter road maintenance is essential for providing safe and efficient service for road users (Usman et al., 2010). It is also costly due to the high cost of equipment, crew, and materials. Optimizing winter road maintenance operations could result in significant cost savings, improved safety and mobility, and reduced environmental and social impacts (Salazar-Aguilar et al., 2012). The first topic in this study focuses on designing routes for winter maintenance trucks from a single depot. Real-world winter road maintenance constraints, including road segment service cycle time, heterogeneous vehicle capacity, fleet size, and road-vehicle dependency, are taken into consideration. The problem is formulated as a variation of the capacitated arc routing problem (CARP) to minimize total travel distance. A metaheuristic algorithm, memetic algorithm (MA), is developed to find nearly optimal solutions. This is the first study that developed the model that includes all the constraints listed. This is the first study that used the MA to solve the routing problem with all those constraints, and the first study that developed the route split procedure that satisfies all those constraints. In addition, a paralleled metaheuristic algorithm is proposed to enhance the solution quality and computation efficiency. The second topic of this study focuses on designing routes from multiple depots with intermediate facilities. The service boundaries of depots are redesigned. Each truck must start and end at its home depot, but they can reload at other depots or reload stations (i.e., intermediate facilities). This problem is a variation of the multi-depot CARP with intermediate facilities (MDCARPIF). The second topic includes all constraints employed in the first topic. Since the trucks can be reloaded at any stations, a constraint that restricts the length of work time for truck drivers is also included in this topic. This is the first study that developed the model that includes all the constraints listed. This is the first study that uses the MA to solve the problem and the first study that developed the route split procedure that satisfies all those constraints. The proposed algorithms are implemented to solve real-world problems. Deadhead (travelling without servicing) speed, service speed, and the spreading rate are estimated by the sample from historical winter road maintenance data. Eighteen traffic networks are used as instances for the first topic. The optimized route in the first topic reduced 13.2% of the deadhead distance comparing to the current practice. Comparing to the single core result, the parallel computation improved the solution fitness on 2 of the 18 instances tested, with slightly less time consumed. Based on the optimized result in the first topic, the reduction of the deadhead distance of the second topic is insignificant. This could be due to the network structure and depot location of the current operation. A test instance is created to verify the effectiveness of the proposed algorithm. The result shows 10.4% of deadhead distance can be saved by using the reload and multiple depot scenario instead of the single depot scenario on the test instance

Impact on Travelers Hedonic and Utilitarian Shopping Behavior by Adoption of Mobile Application: Results from a Quasi-experiment

The continuing development of mobile technology has led to an explosion of mobile applications, which have exposed a broader consumer base to mobile consumption. It is currently unclear how mobile apps using will affect travelers’ shopping behavior, particularly from the perspective of the hedonic and utilitarian shopping behavior of travelers. Using a special quasi-experiment launching by an airline, we collected the datasets of more than 10000 travelers and to investigate the impact of mobile app on the travelers’ shopping behavior. The results suggested that mobile apps adoption improved travelers’ hedonic shopping behavior (e.g., ancillary services purchasing), while the utilitarian shopping conduct (e.g. booking tickets in advance) decreased. It was also found that the mobile app adoption increased hedonic shopping in males but decreased hedonic and utilitarian shopping in frequent flyers and members. This investigation can help with the management of travelers’ purchasing habits and provide guidance for industrial decision makers

Three novel α-L-iduronidase mutations in 10 unrelated Chinese mucopolysaccharidosis type I families

Mucopolysaccharidosis type I (MPS I) arises from a deficiency in the α-L-iduronidase (IDUA) enzyme. Although the clinical spectrum in MPS I patients is continuous, it was possible to recognize 3 phenotypes reflecting the severity of symptoms, viz., the Hurler, Scheie and Hurler/Scheie syndromes. In this study, 10 unrelated Chinese MPS I families (nine Hurler and one Hurler/Scheie) were investigated, and 16 mutant alleles were identified. Three novel mutations in IDUA genes, one missense p.R363H (c.1088G > A) and two splice-site mutations (c.1190-1G > A and c.792+1G > T), were found. Notably, 45% (nine out of 20) and 30% (six out of 20) of the mutant alleles in the 10 families studied were c.1190-1G > A and c.792+1G > T, respectively. The novel missense mutation p.R363H was transiently expressed in CHO cells, and showed retention of 2.3% IDUA activity. Neither p.W402X nor p.Q70X associated with the Hurler phenotype, or even p.R89Q associated with the Scheie phenotype, was found in this group. Finally, it was noted that the Chinese MPS I patients proved to be characterized with a unique set of IDUA gene mutations, not only entirely different from those encountered among Europeans and Americans, but also apparently not even the same as those found in other Asian countries

UNDERSTANDING THE EVOLUTION OF INFORMATION SYSTEMS RESEARCH FROM THE PERSPECTIVE OF CO-AUTHORSHIP NETWORK: A COMPREHENSIVE DATA ANALYSIS FROM 1993 TO 2012

Based on the articles published in three top journals in the field of information systems (MISQ, ISR and JMIS) from 1993 to 2012, we conduct a research of the structure, characteristics and development trend of co-authorship network through scientometrics and social network analysis approaches. We gain a number of insights after synthetical analysis. In the last two decades the whole co-authorship network density in information systems faces a tendency of decrease. The co-authorship network presents properties of “small world”. The number of articles published by scholars and institutions in the three elite journals all display a ‘long tail’ phenomenon. The field of information systems has a stable development in the biggest component, and has not yet went into a mature and steady stage. Quite a lot of outstanding scholars and educational resources came from USA, Canada and Hong Kong, and USA has held eight institutions of the top ten. The ranking of an entire institution can be influenced by even one or two authors, indicating that outcome from one level might propagate to the next level

Model Debiasing via Gradient-based Explanation on Representation

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these approaches may suffer from incomplete disentanglement and overlook proxy attributes (proxies for sensitive attributes) when processing real-world data, especially for unstructured data, causing performance degradation in fairness and loss of useful information for downstream tasks. In this paper, we propose a novel fairness framework that performs debiasing with regard to both sensitive attributes and proxy attributes, which boosts the prediction performance of downstream task models without complete disentanglement. The main idea is to, first, leverage gradient-based explanation to find two model focuses, 1) one focus for predicting sensitive attributes and 2) the other focus for predicting downstream task labels, and second, use them to perturb the latent code that guides the training of downstream task models towards fairness and utility goals. We show empirically that our framework works with both disentangled and non-disentangled representation learning methods and achieves better fairness-accuracy trade-off on unstructured and structured datasets than previous state-of-the-art approaches

A Causal Framework to Unify Common Domain Generalization Approaches

Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years. A large number of approaches have been proposed. Different approaches are motivated from different perspectives, making it difficult to gain an overall understanding of the area. In this paper, we propose a causal framework for domain generalization and present an understanding of common DG approaches in the framework. Our work sheds new lights on the following questions: (1) What are the key ideas behind each DG method? (2) Why is it expected to improve generalization to new domains theoretically? (3) How are different DG methods related to each other and what are relative advantages and limitations? By providing a unified perspective on DG, we hope to help researchers better understand the underlying principles and develop more effective approaches for this critical problem in machine learning

A Study on the Use of Simulation in Synthesizing Path-Following Control Policies for Autonomous Ground Robots

We report results obtained and insights gained while answering the following question: how effective is it to use a simulator to establish path following control policies for an autonomous ground robot? While the quality of the simulator conditions the answer to this question, we found that for the simulation platform used herein, producing four control policies for path planning was straightforward once a digital twin of the controlled robot was available. The control policies established in simulation and subsequently demonstrated in the real world are PID control, MPC, and two neural network (NN) based controllers. Training the two NN controllers via imitation learning was accomplished expeditiously using seven simple maneuvers: follow three circles clockwise, follow the same circles counter-clockwise, and drive straight. A test randomization process that employs random micro-simulations is used to rank the ``goodness'' of the four control policies. The policy ranking noted in simulation correlates well with the ranking observed when the control policies were tested in the real world. The simulation platform used is publicly available and BSD3-released as open source; a public Docker image is available for reproducibility studies. It contains a dynamics engine, a sensor simulator, a ROS2 bridge, and a ROS2 autonomy stack the latter employed both in the simulator and the real world experiments.Comment: 8 pages, 7 figure

Using simulation to design an MPC policy for field navigation using GPS sensing

Modeling a robust control system with a precise GPS-based state estimation capability in simulation can be useful in field navigation applications as it allows for testing and validation in a controlled environment. This testing process would enable navigation systems to be developed and optimized in simulation with direct transferability to real-world scenarios. The multi-physics simulation engine Chrono allows for the creation of scenarios that may be difficult or dangerous to replicate in the field, such as extreme weather or terrain conditions. Autonomy Research Testbed (ART), a specialized robotics algorithm testbed, is operated in conjunction with Chrono to develop an MPC control policy as well as an EKF state estimator. This platform enables users to easily integrate custom algorithms in the autonomy stack. This model is initially developed and used in simulation and then tested on a twin vehicle model in reality, to demonstrate the transferability between simulation and reality (also known as Sim2Real).Comment: 10 pages,5 figures,submitted to ECCOMAS Thematic Conference on Multibody Dynamic