Identify error-sensitive patterns by decision tree

© Springer International Publishing Switzerland 2015. When errors are inevitable during data classification, finding a particular part of the classification model which may be more susceptible to error than others, when compared to finding an Achilles’ heel of the model in a casual way, may help uncover specific error-sensitive value patterns and lead to additional error reduction measures. As an initial phase of the investigation, this study narrows the scope of problem by focusing on decision trees as a pilot model, develops a simple and effective tagging method to digitize individual nodes of a binary decision tree for node-level analysis, to link and track classification statistics for each node in a transparent way, to facilitate the identification and examination of the potentially “weakest” nodes and error-sensitive value patterns in decision trees, to assist cause analysis and enhancement development. This digitization method is not an attempt to re-develop or transform the existing decision tree model, but rather, a pragmatic node ID formulation that crafts numeric values to reflect the tree structure and decision making paths, to expand post-classification analysis to detailed node-level. Initial experiments have shown successful results in locating potentially high-risk attribute and value patterns; this is an encouraging sign to believe this study worth further exploration

Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

In this work, we have attempted to solve two problems concerning the planning and scheduling of crude oil operations: first, on the upstream production planning of crude oil from offshore sources and second, on the scheduling of downstream processing of crude oil at the refinery front-end. The first part is on the offshore oilfield infrastructures planning under both exogenous uncertainty and endogeneous decision-dependent uncertainty. A model representative of the oilfield that is able to select the best routes to obtain the desired objective function is considered. The methodology used is by firstly developing a deterministic model andmodeling it with GAMS, followed by a stochastic one. The results obtained show a high accuracy representation in which the uncertainties in both the exogenous and endogeneous uncertainties in planning are accounted for. The stochastic model is a more thorough representation of the problem because it considers all the uncertainties along with the associated probabilities. Having validated the model formulation and solution obtained with results for standard problems reported in the literature, we believe that the model can be a tool to assist upper-level management in preliminary decision-making on an optimal plan for crude oil production from an offshore operation. The second part is onthe scheduling of crude oiloperations at a refinery front-end. A technique for obtaining globally optimal schedules for the flow of crude is developed. Acontinuous time model based on transfer events is used to represent the scheduling problem and this model is a nonconvex MINLP model which presents multiple local optima. We implement a branch-and-contract algorithm that aims at reducing the size of the search region. In order to obtain a global optimum solution of the problem, an outer-approximation algorithm is proposed, whereby lower and upper bounds on the global optimum are generated, which are converged to a specified tolerance. The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. This solution is the upper bound solution. The application of the proposed algorithm shows significant reduction in the computational effort involved in solving the problem. Slack variables are introduced to overcome the integer infeasibility problem. The optimization model is developed using GAMS and an optimal solution is found with no logical constraints conflicts or error. The main contribution on this work in the first part is to conduct an extensive study onthe implementation ofthe model formulation in Iyer et al. (1998). As well, in the second part, we are focused on investigating effective implementation strategies of the model formulation and solution strategy in Karuppiah et al. (2008) using our choice of the modeling platform GAMS and the best numerical solvers that are available. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original papers so as to provide the readers with the most accurate information possible. V

Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

In this work, we have attempted to solve two problems concerning the planning and scheduling of crude oil operations: first, on the upstream production planning of crude oil from offshore sources and second, on the scheduling of downstream processing of crude oil at the refinery front-end. The first part is on the offshore oilfield infrastructures planning under both exogenous uncertainty and endogeneous decision-dependent uncertainty. A model representative of the oilfield that is able to select the best routes to obtain the desired objective function is considered. The methodology used is by firstly developing a deterministic model and modeling it with GAMS, followed by a stochastic one. The results obtained show a high accuracy representation in which the uncertainties in both the exogenous and endogeneous uncertainties in planning are accounted for. The stochastic model is a more thorough representation of the problem because it considers all the uncertainties along with the associated probabilities. Having validated the model formulation and solution obtained, we believe that the model can be a useful basic tool to assist upper-level management in deciding on an optimal plan for crude oil production from an offshore operation. The second part is on the scheduling of crude oil operations at a refinery front-end. A technique for obtaining globally optimal schedules for the flow of crude is developed. A continuous time model based on transfer events is used to represent the scheduling problem and this model is a nonconvex MINLP model which presents multiple local optima. We implement a branch-and-contract algorithm that aims at reducing the size of the search region. In order to obtain a global optimum solution of the problem, an outer-approximation algorithm is proposed, whereby lower and upper bounds on the global optimum are generated, which are converged to a specified tolerance. The solution obtained from the LB–MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB–NLP. This solution is the upper bound solution. The application of the proposed algorithm shows significant reduction in the computational effort involved in solving the problem. Slack variables are introduced to overcome the integer infeasibility problem. The optimization model is developed using GAMS and an optimal solution is found with no logical constraints conflicts or error. The main contribution on this work in the first part is to conduct an extensive study on the implementation of the model formulation in Iyer et al. (1998). As well, in the second part, we are focused on investigating effective implementation strategies of the model formulation and solution strategy in Karuppiah et al. (2008) using our choice of the modeling platform GAMS and the best numerical solvers that are available. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original papers so as to provide the readers with the most accurate information possible

Reduced cost-based variable fixing in two-stage stochastic programming

The explicit consideration of uncertainty is essential in addressing most planning and operation issues encountered in the management of complex systems. Unfortunately, the resulting stochastic programming formulations, integer ones in particular, are generally hard to solve when applied to realistically-sized instances. A common approach is to consider the simpler deterministic version of the formulation, even if it is well known that the solution quality could be arbitrarily bad. In this paper, we aim to identify meaningful information, which can be extracted from the solution of the deterministic problem, in order to reduce the size of the stochastic one. Focusing on two-stage formulations, we show how and under which conditions the reduced costs associated to the variables in the deterministic formulation can be used as an indicator for excluding/retaining decision variables in the stochastic model. We introduce a new measure, the Loss of Reduced Costs-based Variable Fixing (LRCVF), computed as the difference between the optimal values of the stochastic problem and its reduced version obtained by fixing a certain number of variables. We relate the LRCVF with existing measures and show how to select the set of variables to fix. We then illustrate the interest of the proposed LRCVF and related heuristic procedure, in terms of computational time reduction and accuracy in finding the optimal solution, by applying them to a wide range of problems from the literature

Solving the hazmat transport network design problem

In this paper, we consider the problem of network design for hazardous material transportation where the government designates a network, and the carriers choose the routes on the network. We model the problem as a bilevel network flow formulation and analyze the bilevel design problem by comparing it to three other decision scenarios. The bilevel model is difficult to solve and may be ill-posed. We propose a heuristic solution method that always finds a stable solution. The heuristic exploits the network flow structure at both levels to overcome the difficulty and instability of the bilevel integer programming model. Testing on real data shows that the linearization of the bilevel model fails to find stable solutions and that the heuristic finds lower risk networks in less time. Further testing on random instances shows that the heuristically designed networks achieve significant risk reduction over single-level models. The risk is very close to the least risk possible. However, this reduction in risk comes with a significant increase in cost. We extend the bilevel model to account for the cost/risk trade-off by including cost in the first-level objective. The biobjective-bilevel model is a rich decision-support tool that allows for the generation of many good solutions to the design problem. © 2006 Elsevier Ltd. All rights reserved

Implicit and Explicit Dual Model Predictive Control with an Application to Steel Recycling

We present a formulation for both implicit and explicit dual model predictive control with chance constraints. The formulation is applicable to systems that are affine in the state and disturbances, but possibly nonlinear in the controls. Awareness of uncertainty and dual control effect is achieved by including the covariance of a Kalman Filter state estimate in the predictions. For numerical stability, these predictions are obtained from a square-root Kalman filter update based on a QR decomposition. In the implicit formulation, the incentive for uncertainty reduction is given indirectly via the impact of active constraints on the objective, as large uncertainty leads to large safety backoffs from the constraint set boundary. The explicit formulation additionally uses a heuristic cost term on uncertainty to encourage its active exploration. We evaluate the methods based on numerical simulation of a simplified but representative industrial steel recycling problem. Here, new steel needs to be produced by choosing a combination of several different steel scraps with unknown pollutant content. The pollutant content can only be measured after a scrap combination is molten, allowing for inference on the pollutants in the different scrap heaps. The cost should be minimized while ensuring high quality of the product through constraining the maximum amount of pollutant. The numerical simulations demonstrate the superiority of the two dual formulations with respect to a robustified but non-dual formulation. Specifically we achieve lower cost for the closed-loop trajectories while ensuring constraint satisfaction with a given probability.Comment: Submitted to IEEE Conference on Decision and Control 2022 (CDC 22

Filialų steigimo užsienyje modeliavimas plėtojant universiteto tarptautiškumą

It is argued in literature that the competitiveness of higher education institutions (HEIs) will increasingly depend on their ability to operate internationally in the near future (Delgado-Márquez et al., 2013; De Haan, 2014; De Wit, 2010; Graf, 2009). The emergence of entrepreneurial university phenomena as well as a shift in movement from students to the movement of programmes and universities lead to the emergence of one of the riskiest and unexplored entry modes to international markets in higher education – international branch campus (IBC). Risk reduction strategies of IBC establishment are analysed and suggested in this thesis. The object of present study is international development of HEIs using a branch campus, thus addressing the problem of the lack of comprehensive theoretical and practical frameworks of transnational education activities. The aim of the thesis is to develop a decision support model for international branch campus establishment enhancing the university competitiveness in the foreign market. The dissertation consists of the introduction, three chapters, general conclusions and 8 annexes. Chapter 1 reviews literature on the contemporary issues of internationalisation of higher education focusing on IBC management. Internationalisation theories and foreign market entry modes are analysed in business and higher education. The chapter is finalised with the formulation of the scientific problem of the dissertation. Chapter 2 starts of by reviewing the research methodology for the development of the decision support model for IBC establishment enhancing the university competitiveness. Further, the empirical research for the development of the decision support model is presented. The following research methods have been used: analysis of statistical data, 4 expert surveys (3 on IBC development, 1 on networking), Delphi method, multicriteria decision support method (FARE), semi structured interviews, and computer assisted qualitative data analysis (CAQDAS) using Nvivo software. Chapter 3 suggests and explains in detail the decision support model for the establishment of IBC. The model is based on the synergy of theoretical and empirical research results. During the theoretical analysis of the thesis, the theoretical basis of the model was formed, which was reasoned, elaborated and validated by the empirical research with the participation of four international expert groups. The model itself is presented in the third chapter, describing its empirical validation, as well as revealing the perspectives and limitations of its application. Also the model approbation is presented. 9 scientific articles focusing on the subject discussed in the dissertation have been issued (3 in international journals, 6 in international conference proceedings) and 1 chapter in a book published abroad.Doctoral dissertatio

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

In this work, we have attempted to solve two problems concerning the planning and scheduling of crude oil operations: first, on the upstream production planning of crude oil from offshore sources and second, on the scheduling of downstream processing of crude oil at the refinery front-end. The first part is on the offshore oilfield infrastructures planning under both exogenous uncertainty and endogeneous decision-dependent uncertainty. A model representative of the oilfield that is able to select the best routes to obtain the desired objective function is considered. The methodology used is by firstly developing a deterministic model andmodeling it with GAMS, followed by a stochastic one. The results obtained show a high accuracy representation in which the uncertainties in both the exogenous and endogeneous uncertainties in planning are accounted for. The stochastic model is a more thorough representation of the problem because it considers all the uncertainties along with the associated probabilities. Having validated the model formulation and solution obtained with results for standard problems reported in the literature, we believe that the model can be a tool to assist upper-level management in preliminary decision-making on an optimal plan for crude oil production from an offshore operation. The second part is onthe scheduling of crude oiloperations at a refinery front-end. A technique for obtaining globally optimal schedules for the flow of crude is developed. Acontinuous time model based on transfer events is used to represent the scheduling problem and this model is a nonconvex MINLP model which presents multiple local optima. We implement a branch-and-contract algorithm that aims at reducing the size of the search region. In order to obtain a global optimum solution of the problem, an outer-approximation algorithm is proposed, whereby lower and upper bounds on the global optimum are generated, which are converged to a specified tolerance. The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. This solution is the upper bound solution. The application of the proposed algorithm shows significant reduction in the computational effort involved in solving the problem. Slack variables are introduced to overcome the integer infeasibility problem. The optimization model is developed using GAMS and an optimal solution is found with no logical constraints conflicts or error. The main contribution on this work in the first part is to conduct an extensive study onthe implementation ofthe model formulation in Iyer et al. (1998). As well, in the second part, we are focused on investigating effective implementation strategies of the model formulation and solution strategy in Karuppiah et al. (2008) using our choice of the modeling platform GAMS and the best numerical solvers that are available. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original papers so as to provide the readers with the most accurate information possible. V

An integrated decision support system based on simulation and mathematical programming of Petroleum transportation logistics

Discrete Event simulation (DES), mathematical programming (MP) and analysis of variance (ANOVA) are among the popular tools in operational research (OR) used in dynamic industry like petroleum industry. The integration of these methods even becomes more significant to managerial application in the industry. The objective of this thesis is to present an integrated decision support system by which a decision maker should be able to choose the optimal number of tanks, tank size and truck arrival rate to maximize average total profit and minimize the total transportation cost for an oil refinery terminal operations. The petroleum transportation management system (PTMS) is developed as a DSS using a discrete-event simulation program with ARENA software, mathematical linear programming (LP) with I-Log software and analysis of variance (ANOVA) with SPSS software, and these models are combined in complex program developed using visual basic software (VB). The simulation model represents the logistics operations from oil arriving to the refinery terminal to the supply points. The model process used as a decision support tool to help in evaluating and improving the comprehensive oil terminal operations. And also understanding and assessing of the different steps in a simulation process. An optimization model was formulated with the objective to minimize the total transportation cost. In the model formulation, hard constraints were considered and the linear programming (LP) technique was used. Result obtained suggests the use of certain types of trucks can reduce the operation costs, if compared to that of the current situation. The reduction of costs is due to the reduction of travelling trips as based on the problem constraints. Overall, output of this study has given positive impacts on the transportation operations. The effect of the changes can help the management of the transportation company to make efficient decisions. Multifactor ANOVA is used to determine whether different levels of the three-factors and their interactions significantly impact the oil refinery terminal's profit. ANOVA is also used to determine the flow rate of oil into the tanks station; tank and truck fill rate and a cost and revenue structure. The final step is to expand the model to cover the whole models (DES, LP and ANOVA) and create the integrated user interface. To sum up the combination of these techniques which allows evaluating the actual feasibility of supply planning considering all operations restrictions and variability of the supply logistics and the total transportation cost. In another words, a DSS have been developed to support a decision maker, who is planning to build a new facility or expand an existing oil refinery terminal, should be able to choose the optimal value for all important factors. The PTMS is able to predict with 99% confidence a set of factor levels that yields the highest average total profit