Exact And Representative Algorithms For Multi Objective Optimization

In most real-life problems, the decision alternatives are evaluated with multiple conflicting criteria. The entire set of non-dominated solutions for practical problems is impossible to obtain with reasonable computational effort. Decision maker generally needs only a representative set of solutions from the actual Pareto front. First algorithm we present is for efficiently generating a well dispersed non-dominated solution set representative of the Pareto front which can be used for general multi objective optimization problem. The algorithm first partitions the criteria space into grids to generate reference points and then searches for non-dominated solutions in each grid. This grid-based search utilizes achievement scalarization function and guarantees Pareto optimality. The results of our experimental results demonstrate that the proposed method is very competitive with other algorithms in literature when representativeness quality is considered; and advantageous from the computational efficiency point of view. Although generating the whole Pareto front does not seem very practical for many real life cases, sometimes it is required for verification purposes or where DM wants to run his decision making structures on the full set of Pareto solutions. For this purpose we present another novel algorithm. This algorithm attempts to adapt the standard branch and bound approach to the multi objective context by proposing to branch on solution points on objective space. This algorithm is proposed for multi objective integer optimization type of problems. Various properties of branch and bound concept has been investigated and explained within the multi objective optimization context such as fathoming, node selection, heuristics, as well as some multi objective optimization specific concepts like filtering, non-domination probability, running in parallel. Potential of this approach for being used both as a full Pareto generation or an approximation approach has been shown with experimental studies

An integrated analysis of carbon capture and storage strategies for power and industry in Europe

Industry is responsible for one-quarter of the global CO2 emissions. In this study, four different climate pathways are analyzed with a cost minimizing multihorizon stochastic optimization model, in order to analyze possible realizations of carbon capture and storage (CCS) in the power sector and main industrial sectors in Europe. In particular, we aim to achieve a deeper understanding of the distribution of capture by country and key sector (power, steel, cement and refinery), as well as the associated transport and storage infrastructure for CCS. Results point to the synergy effect of sharing common CCS infrastructres among power and major industrial sectors. The contribution of CCS is mainly found in three industrial sectors, particularly steel, cement and refineries) but also in the power sector to a lesser extent. It is worth noting that retrofitting of CCS in the power sector was not considered in this study. The geographical location for capture and storage, as well as timing and capacity needs are presented for different socio-economic pathways and corresponding emission targets. It has been shown that contributions of the three industry sectors in emissions reductions are neither geographically nor sector-wise homogeneous across the pathways.acceptedVersio

Generating Pareto Surface for Multi Objective Integer Programming Problems with Stochastic Objective Coefficients

AbstractStochastic multi objective programming problems commonly arise in complex systems such as portfolio analysis, medium- to long-term capacity planning and design applications under uncertainty. The identification of the candidate solution set is a main step in many applications which depends on the nature of uncertainty. This study presents a method to generate Pareto surface for multi-objective integer programs with stochastic coefficients in the objective functions based on minimum expectation and variance criteria. The objective function coefficients are represented through random discrete distributions. The methodology follows a two-phase approach where, in the first phase, the stochastic multiple objectives are converted into deterministic equivalents based on the minimum expectation and variance efficiency concepts. The second phase solves the deterministic multi objective problem, using a Pareto generation methodology which aims at generating the whole Pareto surface of multi objective integer programming problems. We present results of experimental study of applying the proposed method to an assignment problem with three objective functions

Time series forecasting of domestic shipping market: comparison of SARIMAX, ANN-based models and SARIMAX-ANN hybrid model

Seaborne transport forecasting has attracted substantial interest over the years because of providing a useful policy tool for decision-makers. Although various forecasting methods have been widely studied, there is still broad debate on accurate forecasting models and preprocessing. The current paper aims to point out these issues, as well as to establish the forecasting model of the domestic cargo volumes using SARIMAX, MLP, LSTM and NARX and SARIMAX-ANN hybrid models. Based on the domestic cargo volumes of Turkey, findings suggest that SARIMA-MLP models can be considered as an appropriate alternative, at least for time series forecasting of shipping. Pre-processed data provides a significant improvement over those obtained with unpreprocessed data, with the accuracy of the models found to be significantly boosted with the Fourier term of decomposition. The results indicate that SARIMAX-MLP, with a mean absolute percentage error (MAPE) of 4.81, outperforms the closest models of SARIMAX, with a MAPE of 6.14 and LSTM with Fourier decomposition with a MAPE of 6.52. Findings have implications for shipping policymakers to plan infrastructure development, and useful for shipowners in accurately formulating shipping demand

Design for reusability of medical equipment for optimal modularization using an endoscope as case study

The reprocessing of reusable medical equipment (RME) is complex due to the difficulty of eliminating infections. Healthcare providers have expressed dissatisfaction with the difficulty faced in cleaning and disinfecting medical equipment after use. Reprocessed RMEs include endoscopes, valves, adapters etc. This research aims to increase the ease of reprocessing and decrease the risk of infection using a collaborative modular architecture framework. The methodology is divided into four steps. First, we identify and define the product’s functional and physical decompositions. Secondly, based on stakeholders’ input, parameters such as design, human factors, and cost were identified to be the main factors affecting the reprocessing of an endoscope. The parameters’ subsequent metrics are selected for performance requirements. Thirdly, surveys are developed to collect data about the performance of different endoscope models. Fourthly, we utilize a linear multi-objective optimization model which aims at generating representative solutions on the true Pareto front of the problem that maximizes the similarity among module members in terms of the factors. Finally, we combine the module information with efficiency feedback to derive recommendations for the users. A case study is presented using hospital data. The results indicate that there is a high risk of infection caused by human errors during reprocessing for endoscopes clustered in Module 1 compared to Module 2 and 3. To ensure that the safety and quality of medical care rendered to patients are not compromised, we recommend that healthcare providers utilize endoscopes in Modules 2 and 3 as they are safe and easy to reprocess

EMPIRE: An open-source model based on multi-horizon programming for energy transition analyses

Energy and power system models represent important insights on the technical operations of energy technologies that supply the energy consumption in time steps with hourly resolution. This paper presents the European Model for Power system Investments with Renewable Energy (EMPIRE) that combines short-term operations with the representation of long-term planning decisions including infrastructure expansion. The EMPIRE model has an unique mathematical modelling structure based on multi-horizon stochastic programming, which means investment decisions are subject to short-term uncertainty represented by different realizations of operational scenarios. The model is open source and ready to use to analyze energy transition scenarios towards 2050 and beyond. This paper outlines the building blocks of the model and its software structure. We also present an illustrative example of results from using the software

Atrial Fibrillation Management in Acute Stroke Patients in Türkiye: Real-life Data from the NöroTek Study

Objective: Atrial fibrillation (AF) is the most common directly preventable cause of ischemic stroke. There is no dependable neurology-based data on the spectrum of stroke caused by AF in Turkiye. Within the scope of NoroTek-Turkiye (TR), hospital-based data on acute stroke patients with AF were collected to contribute to the creation of acute-stroke algorithms.Materials and Methods: On May 10, 2018 (World Stroke Awareness Day), 1,790 patients hospitalized at 87 neurology units in 30 health regions were prospectively evaluated. A total of 929 patients [859 acute ischemic stroke, 70 transient ischemic attack (TIA)] from this study were included in this analysis.Results: The rate of AF in patients hospitalized for ischemic stroke/TIA was 29.8%, of which 65% were known before stroke, 5% were paroxysmal, and 30% were diagnosed after hospital admission. The proportion of patients with AF who received "effective" treatment [international normalization ratio >= 2.0 warfarin or non-vitamin K antagonist oral anticoagulants (NOACs) at a guideline dose] was 25.3%, and, either no medication or only antiplatelet was used in 42.5% of the cases. The low dose rate was 50% in 42 patients who had a stroke while taking NOACs. Anticoagulant was prescribed to the patient at discharge at a rate of 94.6%; low molecular weight or unfractionated heparin was prescribed in 28.1%, warfarin in 32.5%, and NOACs in 31%. The dose was in the low category in 22% of the cases discharged with NOACs, and half of the cases, who received NOACs at admission, were discharged with the same drug.Conclusion: NoroTekTR revealed the high but expected frequency of AF in acute stroke in Turkiye, as well as the aspects that could be improved in the management of secondary prophylaxis. AF is found in approximately one-third of hospitalized acute stroke cases in Turkiye. Effective anticoagulant therapy was not used in three-quarters of acute stroke cases with known AF. In AF, heparin, warfarin, and NOACs are planned at a similar frequency (one-third) within the scope of stroke secondary prophylaxis, and the prescribed NOAC dose is subtherapeutic in a quarter of the cases. Non-medical and medical education appears necessary to prevent stroke caused by AF