The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers. The problem is well-known in the l...
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The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers. The problem is well-known in the literature. In its basic version it covers a wide range of tasks in data analysis. Simultaneously performing feature selection and outlier detection strongly improves the application potential of regression models in more general settings, where data governance is a concern. To trigger this potential, flexible training models are needed, with more parameters under control of decision makers. The use of mathematical programming, although pertinent, is scarce in this context and mostly focusing on the least -squares setting. Instead we consider the least absolute deviation criterion, proposing two mixedinteger linear programs, one adapted from existing studies, and the other obtained from a disjunctive programming argument. We show theoretically and computationally that the disjunctive -based formulation is better in terms of both continuous relaxation quality and integer optimality convergence. We experimentally benchmark against existing methodologies from the literature. We identify the characteristics of contamination patterns, in which mathematical programming is better than state-of-the-art algorithms in combining prediction quality, sparsity and robustness against outliers. Additionally, the mathematical programming approaches allow the decision maker to directly control parameters like the number of features or outliers to tolerate, those based on least absolute deviations performing best. On real world datasets, where privacy is a concern, our approach compares well to state-of-the-art methods in terms of accuracy, being at the same time more flexible.
Public bus transit service (PBTS) is recognized as a highly effective mode of transportation, offering accessibility, affordability, and adaptability that contribute to its critical role in transportation networks. Th...
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Public bus transit service (PBTS) is recognized as a highly effective mode of transportation, offering accessibility, affordability, and adaptability that contribute to its critical role in transportation networks. The extensive literature on PBTS encompasses various aspects, with mathematical programming emerging as a widely employed methodology to tackle the public bus transit network design and operations planning problem (PBTNDP&OPP). In this paper, first, we employ the critical path method (CPM) to visually map the development of existing literature on the application of mathematical programming in PBTND&OPP by focusing on manuscripts published in top-tier journals. The objective is to identify key sub-problems extensively studied in the literature and recently emerging topics. Then, we conduct a comprehensive review of recent applications of mathematical programming in PBTND&OPP, encompassing sustainable and green practices, as well as emerging transportation technologies and modes within PBTS. These two sub-problems have been identified as recently emerged and hot topics in the literature of mathematical programming and PBTND&OPP, based on the provided CPM in the first step. Selected papers for each sub-problem are examined, providing insights into problem formulation, objective functions, decision variables, demand patterns, network structures, and key findings. Based on the literature review, we systematically identify research gaps in each sub-problem and offer directions and suggestions for future studies. While there is a considerable body of literature that has applied mathematical programming to investigate these two emerging topics, our review highlights that the existing literature is still in the early stages of development. Hence, numerous problems relating to these topics remain ripe for exploration through mathematical programming. Examining the effects of sustainable development policies or the introduction of emerging technologies on the reliab
Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathe...
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Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions.
Heat integration is important for energy-saving in the process *** is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and co...
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Heat integration is important for energy-saving in the process *** is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale *** reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such ***,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both *** insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy *** results are obtained from three literature cases of different scales.
Wind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the...
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Wind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the distance between turbines can reduce wake loss but increase cable cost, these two optimizations are coupled and jointly affect the revenue of wind farms. In this paper, we propose a novel nonlinear mathematical programming model based on the 3D Gaussian wake model and use a mathematical programming approach to optimize the layout of the wind farm and the cable layout together, considering both power generation and cable cost. In this method, some of the constraints were linearized to facilitate the solution process. The optimization results show that profit increased by 9.07% when using annual economic efficiency as the objective function, compared with using energy production as the objective function.
Formation of energy-based industrial symbiosis networks (EISNs) is a measure by which industries can address their high energy consumption. EISNs are often designed through mathematical programming (MP) because this m...
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Formation of energy-based industrial symbiosis networks (EISNs) is a measure by which industries can address their high energy consumption. EISNs are often designed through mathematical programming (MP) because this method can represent the integration of numerous entities in a compact model while allowing tradeoff analysis of various EISN design objectives. In view thereof, this study presents a systematic review of MP models for EISN optimization. It addresses the research gap on the lack of studies which review the use of MP for optimizing EISNs involving waste heat as the shared resource. The models were analyzed based on five features: the typology of objective functions, the integrated entities in the EISN, the waste heat use options, the effects of considering distance between entities, and the method for modelling parameter uncertainty. This study has uncovered several gaps in EISN modelling. First, there is no consensus about the most relevant environmental and social impacts to include in EISN optimization. Second, novel approaches to simplify nonconvex models are scarce, thereby hindering the incorporation of more pertinent entities into the models due to the concomitant increase in solution time. Third, models analyzing the tradeoff among the various waste heat utilization pathways are limited. Fourth, most models do not include the implications of considering the physical layout of integrated entities in optimizing EISN design. Finally, the best method to incorporate parameter uncertainty in models is still unsettled. By addressing these gaps, more comprehensive MP models can be developed, thereby supporting better-informed decisions about EISN establishment.
Exploratory Data Analysis (EDA) is the interactive process of gaining insights from a dataset. Comparisons are popular insights that can be specified with comparison queries, i.e., specifications of the comparison of ...
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Exploratory Data Analysis (EDA) is the interactive process of gaining insights from a dataset. Comparisons are popular insights that can be specified with comparison queries, i.e., specifications of the comparison of subsets of data. In this work, we consider the problem of automatically computing sequences of comparison queries that are coherent, significant and whose overall cost is bounded. Such an automation is usually done by either generating all insights and solving a multi-criteria optimization problem, or using reinforcement learning. In the first case, a large search space has to be explored using exponential algorithms or dedicated heuristics. In the second case, a dataset-specific, time and energy-consuming training, is necessary. We contribute with a novel approach, consisting of decomposing the optimization problem in two: the original problem, that is solved over a smaller search space, and a new problem of generating comparison queries, aiming at generating only queries improving existing solutions of the first problem. This allows to explore only a portion of the search space, without resorting to reinforcement learning. We show that this approach is effective, in that it finds good solutions to the original multi-criteria optimization problem, and efficient, allowing to generate sequences of comparisons in reasonable time.
Emergency Medical Services (EMS) play a crucial role in healthcare systems, managing pre-hospital or out-of-hospital emergencies from the onset of an emergency call to the patient's arrival at a healthcare facilit...
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Emergency Medical Services (EMS) play a crucial role in healthcare systems, managing pre-hospital or out-of-hospital emergencies from the onset of an emergency call to the patient's arrival at a healthcare facility. The design of an efficient ambulance location model is pivotal in enhancing survival rates, controlling morbidity, and preventing disability. Key factors in the classical models typically include travel time, demand zones, and the number of stations. While urban EMS systems have received extensive examination due to their centralized populations, rural areas pose distinct challenges. These include lower population density and longer response distances, contributing to a higher fatality rate due to sparse population distribution, limited EMS stations, and extended travel times. To address these challenges, we introduce a novel mathematical model that aims to optimize coverage and equity. A distinctive feature of our model is the integration of equity within the objective function, coupled with a focus on practical response time that includes the period required for personal protective equipment procedures, ensuring the model's applicability and realism in emergency response scenarios. We tackle the proposed problem using a tailored genetic algorithm and propose a greedy algorithm for solution construction. The implementation of our tailored Genetic Algorithm promises efficient and effective EMS solutions, potentially enhancing emergency care and health outcomes in rural communities.
Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs...
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Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs without expensive submarine cables and offshore substations. The export pathway of OWF-DHP consists of collection pipelines, transmission pipelines, and offshore compressor stations. In this paper, mathematical programming is introduced to explore the export pathway planning problem of OWF-DHP. Firstly, the area of OWF is discretized into multiple grids with the center of the grids being the candidate locations of the offshore compressor station. Then, a binary integer quadratic programming problem is established to optimize both the offshore compressor station location and pipeline construction with different topologies. Further, the mathematical model is solved by the branch and cut algorithm integrated with the proposed dimension reduction methods. Finally, the effectiveness of the proposed export pathway planning approach is verified by the actual data of Baltic Eagle OWF in Germany, which would support the construction of the OWF-DHP project.
In this study, first, a new mathematical programming formulation for generating Sudoku puzzles is proposed. It is possible to generate specially-configured puzzle instances using the proposed formulation which is flex...
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In this study, first, a new mathematical programming formulation for generating Sudoku puzzles is proposed. It is possible to generate specially-configured puzzle instances using the proposed formulation which is flexible enough to control not only the numbers of the Sudoku matrix entries shown in each column, row and sub-matrix, but also the times each number appears by setting up the corresponding model parameters accordingly. The initially developed non-linear program with a quadratic constraint is reformulated as a linear-integer program by using appropriate variate transformations. The resulting mathematical program is then solved to generate Sudoku puzzles and its computational performance is analyzed through computational experiments. It is noted that the formulation is fast enough to generate Sudoku puzzles in reasonable time periods using a commercial solver on a personal computer. The study then discusses how to ensure the uniqueness of a solution for a puzzle instance generated by a hybrid approach that integrates the mathematical program with a heuristic algorithm. In the final part of the study, the idea of the proposed hybrid approach is extended and a backtracking algorithm-based puzzle generation procedure is designed and implemented by developing a standalone mobile-web game application.
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