Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

Researchers who investigate in any area related to computational algorithms (both dening new algorithms or improving existing ones) usually nd large diculties to test their work. Comparisons among dierent researches in this eld are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the eld of routing problems. In this paper this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This eld has been chosen because of its importance in real world, and its relevance in the actual literature

The GHG emission reduction toolkit : a case study of Blacktown City, Australia

This PhD thesis is in line with Australia’s national policy of a 26-28% reduction in its greenhouse gas emissions to 2005 levels. According to a review of its climate change policy in 2017, the Australian Government is committed to tackling climate change, while maintaining a strong economy, providing affordable energy and security for industries. This requires new initiatives in existing technologies to reduce greenhouse gas emissions or the emergence of new technologies altogether. Whatever the strategy, the final goal is to mitigate greenhouse gas emissions. This national target is now disseminated among different sectors and governmental bodies in Australia, requesting them to submit their action plans against climate change. This includes all Australian City Councils and incorporates Blacktown City Council as the Case Study for this study. As part of the Blacktown City Council’s commitment to reduce greenhouse gas emissions, this research study is the result of collaboration between the Council and Western Sydney University. The authorities of both sides have signed a research collaboration agreement, ample evidence of a local university tackling local problems. This research agreement is unique as it opens a door for other local Councils to collaborate with universities. Blacktown City Council, on the other side of this agreement, can improve its body of knowledge through a comprehensive investigation of greenhouse gas mitigation using its available tools. Therefore, this research study developed a toolkit to help reduce the Council’s GHG Emission

Learning Sampling-Based 6D Object Pose Estimation

The task of 6D object pose estimation, i.e. of estimating an object position (three degrees of freedom) and orientation (three degrees of freedom) from images is an essential building block of many modern applications, such as robotic grasping, autonomous driving, or augmented reality. Automatic pose estimation systems have to overcome a variety of visual ambiguities, including texture-less objects, clutter, and occlusion. Since many applications demand real time performance the efficient use of computational resources is an additional challenge. In this thesis, we will take a probabilistic stance on trying to overcome said issues. We build on a highly successful automatic pose estimation framework based on predicting pixel-wise correspondences between the camera coordinate system and the local coordinate system of the object. These dense correspondences are used to generate a pool of hypotheses, which in turn serve as a starting point in a final search procedure. We will present three systems that each use probabilistic modeling and sampling to improve upon different aspects of the framework. The goal of the first system, System I, is to enable pose tracking, i.e. estimating the pose of an object in a sequence of frames instead of a single image. By including information from previous frames tracking systems can resolve many visual ambiguities and reduce computation time. System I is a particle filter (PF) approach. The PF represents its belief about the pose in each frame by propagating a set of samples through time. Our system uses the process of hypothesis generation from the original framework as part of a proposal distribution that efficiently concentrates samples in the appropriate areas. In System II, we focus on the problem of evaluating the quality of pose hypotheses. This task plays an essential role in the final search procedure of the original framework. We use a convolutional neural network (CNN) to assess the quality of an hypothesis by comparing rendered and observed images. To train the CNN we view it as part of an energy-based probability distribution in pose space. This probabilistic perspective allows us to train the system under the maximum likelihood paradigm. We use a sampling approach to approximate the required gradients. The resulting system for pose estimation yields superior results in particular for highly occluded objects. In System III, we take the idea of machine learning a step further. Instead of learning to predict an hypothesis quality measure, to be used in a search procedure, we present a way of learning the search procedure itself. We train a reinforcement learning (RL) agent, termed PoseAgent, to steer the search process and make optimal use of a given computational budget. PoseAgent dynamically decides which hypothesis should be refined next, and which one should ultimately be output as final estimate. Since the search procedure includes discrete non-differentiable choices, training of the system via gradient descent is not easily possible. To solve the problem, we model behavior of PoseAgent as non-deterministic stochastic policy, which is ultimately governed by a CNN. This allows us to use a sampling-based stochastic policy gradient training procedure. We believe that some of the ideas developed in this thesis, such as the sampling-driven probabilistically motivated training of a CNN for the comparison of images or the search procedure implemented by PoseAgent have the potential to be applied in fields beyond pose estimation as well

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Tier 1 Highway Security Sensitive Material Dynamic Risk Management

Each year, over 2 billion tons of hazardous materials are shipped in the United States, with over half of that being moved on commercial vehicles. Given their relatively poor or nonexistent defenses and inconspicuousness, commercial vehicles transporting hazardous materials are an easy target for terrorists. Before carriers or security agencies recognize that something is amiss, their contents could be detonated or released. From 2006 to 2015, the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA) recorded 144,643 incidents involving a release of hazardous materials. Although there were no known instances of terrorism being the cause, accidental releases involving trucks carrying hazardous materials are not an uncommon occurrence. At this time, no systems have been developed and operationalized to monitor the movement of vehicles transporting hazardous materials. The purpose of this dissertation is to propose a comprehensive risk management system for monitoring Tier 1 Highway Security Sensitive Materials (HSSMs) which are shipped aboard commercial vehicles in the U.S. Chapter 2 examines the history and current state of hazardous materials transportation. Since the late 19th century, the federal government often introduced new regulations in response to hazardous materials incidents. However, over the past 15 years few binding policies or legislation have been enacted. This demonstrates that government agencies and the U.S. Congress are not inclined to introduce new laws and rules that could hamper business. In 2003, the Federal Motor Carrier Safety Administration (FMCSA) and other agencies led efforts to develop a prototype hazardous materials tracking system (PHTS) that mapped the location of hazardous materials shipments and quantified the level of risk associated with each one. The second half of this chapter uses an in-­‐depth gap analysis to identify deficiencies and demonstrate in what areas the prototype system does not comply with government specifications. Chapter 3 addresses the lack of customized risk equations for Tier 1 HSSMs and develops a new set of risk equations that can be used to dynamically evaluate the level of risk associated with individual hazardous materials shipments. This chapter also discusses the results of a survey that was administered to public and private industry stakeholders. Its purpose was to understand the current state of hazardous materials regulations, the likelihood of hazardous materials release scenarios, what precautionary measures can be used, and what influence social variables may have on the aggregate consequences of a hazardous materials release. The risk equation developed in this paper takes into account the survey responses as well as those risk structures already in place. The overriding goal is to preserve analytical tractability, implement a form that is usable by federal agencies, and provide stakeholders with accurate information about the risk profiles of different vehicles. Due to congressional inaction on hazardous 3 materials transportation issues, securing support from carriers and other industry stakeholders is the most viable solution to bolstering hazardous materials security. Chapter 4 presents the system architecture for The Dynamic Hazardous Materials Risk Assessment Framework (DHMRA), a GIS-­‐based environment in which hazardous materials shipments can be monitored in real time. A case study is used to demonstrate the proposed risk equation; it simulates a hazardous materials shipment traveling from Ashland, Kentucky to Philadelphia, Pennsylvania. The DHMRA maps risk data, affording security personnel and other stakeholders the opportunity to evaluate how and why risk profiles vary across time and space. DHRMA’s geo-­‐fencing capabilities also trigger automatic warnings. This framework, once fully implemented, can inform more targeted policies to enhance the security of hazardous materials. It will contribute to maintaining secure and efficient supply chains while protecting the communities that live nearest to the most heavily trafficked routes. Continuously monitoring hazardous materials provides a viable way to understand the risks presented by a shipment at a given moment and enables better, more coordinated responses in the event of a release. Implementation of DHRMA will be challenging because it requires material and procedural changes that could disrupt agency operations or business practices — at least temporarily. Nevertheless, DHRMA stands ready for implementation, and to make the shipment of hazardous materials a more secure, safe, and certain process. Although DHMRA was designed primarily with terrorism in mind, it is also useful for examining the impacts of accidental hazardous materials releases. Future iterations of DHMRA could expand on its capabilities by incorporating modeling data on the release and dispersion of toxic gases, liquids, and other substances

University of Windsor Graduate Calendar 2022 Spring

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University of Windsor Graduate Calendar 2021 Fall

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University of Windsor Graduate Calendar 2022 Fall

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