46 research outputs found

    Corridor Location: Generating Competitive and Efficient Route Alternatives

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    The problem of transmission line corridor location can be considered, at best, a "wicked" public systems decision problem. It requires the consideration of numerous objectives while balancing the priorities of a variety of stakeholders, and designers should be prepared to develop diverse non-inferior route alternatives that must be defensible under the scrutiny of a public forum. Political elements aside, the underlying geographical computational problems that must be solved to provide a set of high quality alternatives are no less easy, as they require solving difficult spatial optimization problems on massive GIS terrain-based raster data sets.Transmission line siting methodologies have previously been developed to guide designers in this endeavor, but close scrutiny of these methodologies show that there are many shortcomings with their approaches. The main goal of this dissertation is to take a fresh look at the process of corridor location, and develop a set of algorithms that compute path alternatives using a foundation of solid geographical theory in order to offer designers better tools for developing quality alternatives that consider the entire spectrum of viable solutions. And just as importantly, as data sets become increasingly massive and present challenging computational elements, it is important that algorithms be efficient and able to take advantage of parallel computing resources.A common approach to simplify a problem with numerous objectives is to combine the cost layers into a composite a priori weighted single-objective raster grid. This dissertation examines new methods used for determining a spatially diverse set of near-optimal alternatives, and develops parallel computing techniques for brute-force near-optimal path enumeration, as well as more elegant methods that take advantage of the hierarchical structure of the underlying path-tree computation to select sets of spatially diverse near optimal paths.Another approach for corridor location is to simultaneously consider all objectives to determine the set of Pareto-optimal solutions between the objectives. This amounts to solving a discrete multi-objective shortest path problem, which is considered to be NP-Hard for computing the full set of non-inferior solutions. Given the difficulty of solving for the complete Pareto-optimal set, this dissertation develops an approximation heuristic to compute path sets that are nearly exact-optimal in a fraction of the time when compared to exact algorithms. This method is then applied as an upper bound to an exact enumerative approach, resulting in significant performance speedups. But as analytic computing continues to moved toward distributed clusters, it is important to optimize algorithms to take full advantage parallel computing. To that extent, this dissertation develops a scalable parallel framework that efficiently solves for the supported/convex solutions of a biobjective shortest path problem. This framework is equally applicable to other biobjective network optimization problems, providing a powerful tool for solving the next generation of location analysis and geographical optimization models

    Techniques for Multiobjective Optimization with Discrete Variables: Boxed Line Method and Tchebychev Weight Set Decomposition

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    Many real-world applications involve multiple competing objectives, but due to conflict between the objectives, it is generally impossible to find a feasible solution that optimizes all, simultaneously. In contrast to single objective optimization, the goal in multiobjective optimization is to generate a set of solutions that induces the nondominated (ND) frontier. This thesis presents two techniques for multiobjective optimization problems with discrete decision variables. First, the Boxed Line Method is an exact, criterion space search algorithm for biobjective mixed integer programs (Chapter 2). A basic version of the algorithm is presented with a recursive variant and other enhancements. The basic and recursive variants permit complexity analysis, which yields the first complexity results for this class of algorithms. Additionally, a new instance generation method is presented, and a rigorous computational study is conducted. Second, a novel weight space decomposition method for integer programs with three (or more) objectives is presented with unique geometric properties (Chapter 3). The weighted Tchebychev scalarization used for this weight space decomposition provides the benefit of including unsupported ND images but at the cost of convexity of weight set components. This work proves convexity-related properties of the weight space components, including star-shapedness. Further, a polytopal decomposition is used to properly define dimension for these nonconvex components. The weighted Tchebychev weight set decomposition is then applied as a “dual” perspective on the class of multiobjective “primal” algorithms (Chapter 4). It is shown that existing algorithms do not yield enough information for a complete decomposition, and the necessary modifications required to yield the missing information is proven. Modifications for primal algorithms to compute inner and outer approximations of the weight space components are presented. Lastly, a primal algorithm is restricted to solving for a subset of the ND frontier, where this subset represents the compromise between multiple decision makers’ weight vectors.Ph.D

    Multi-objective network optimization: models, methods, and applications

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    There can be an array of planning objectives to consider when identifying alternatives for using, modifying, or restoring natural or built environments. In this respect, multi-objective network optimization models can provide decision support to both managers and users of the system. While there can be an infinite number of feasible solutions to any multi-objective optimization problem in large networks (e.g., urban transportation systems), the efficient ones are usually more desirable in the decision-making process. However, identification of efficient solutions can be challenging in practical applications. To address this issue, this dissertation details mathematical formulations and solution algorithms for a range of real-world planning problems in the context of intelligent transportation systems, vehicle routing problem, natural conservation and landscape connectivity. While the combination of objectives being optimized is unique for each application, the underlying phenomena involves modeling movement between origins and destinations of a networked system. To demonstrate the type of insights that can be achieved using these modeling approaches, the location and number of times solutions appear in different realizations of system and given different solution approaches (e.g., exact and approximate methods) are visualized on network using a commercial geographic information system

    Proceedings of the XIII Global Optimization Workshop: GOW'16

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    [Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

    Applied (Meta)-Heuristic in Intelligent Systems

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    Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

    A Polyhedral Study of Mixed 0-1 Set

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    We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

    OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

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    Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work
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