Many-objective design of reservoir systems - Applications to the Blue Nile

This work proposes a multi-criteria optimization-based approach for supporting the negotiated design of multireservoir systems. The research addresses the multi-reservoir system design problem (selecting among alternative options, reservoir sizing), the capacity expansion problem (timing the activation of new assets and the filling of new large reservoirs) and management of multi-reservoir systems at various expansion stages. The aim is to balance multiple long and short-term performance objectives of relevance to stakeholders with differing interests. The work also investigates how problem re-formulations can be used to improve computational efficiency at the design and assessment stage and proposes a framework for post-processing of many objective optimization results to facilitate negotiation among multiple stakeholders. The proposed methods are demonstrated using the Blue Nile in a suite of proof-of-concept studies. Results take the form of Pareto-optimal trade-offs where each point on the curve or surface represents the design of water resource systems (i.e., asset choice, size, implementation dates of reservoirs, and operating policy) and coordination strategies (e.g., cost sharing and power trade) where further benefits in one measure necessarily come at the expense of another. Technical chapters aim to offer practical Nile management and/or investment recommendations deriving from the analysis which could be refined in future more detailed studies

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and use of a performance-based approach in transportation planning and programming. This research introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to (1) improve bridge management, (2) support state departments of transportation with their transition efforts to comply with the MAP-21 requirements, (3) determine short- and long-term intervention strategies and funding requirements, and (4) facilitate trade-offs between funding levels and performance. The proposed methodology focuses on one transportation asset class (i.e., bridge) and is structured around the following five modules: 1. Data Processing Module, 2. Improvement Module, 3. Element-level Optimization Module, 4. Bridge-level Optimization Module, and 5. Network-level Optimization Module. To overcome computer memory and processing time limitations, the methodology relies on the following three distinct screening processes: 1. Element Deficiency Process, 2. Alternative Feasibility Process, and 3. Solution Superiority Screening Process. The methodology deploys an independent deterioration model (i.e., Weibull/Markov model), to predict performance, and a life-cycle cost model, to estimate life-cycle costs and benefits. Life-cycle (LC) alternatives (series of element improvement actions) are generated based on a new simulation arrangement for three distinct improvement types: 1. maintenance, repair and rehabilitation (preservation); 2. functional improvement; and 3. replacement. A LC activity profile is constructed separately for each LC alternative action path. The methodology consists of three levels of optimization assessment based on the Pareto optimality concept: (1) an element-level optimization, to identify optimal or near-optimal element intervention actions for each deficient element (poor condition state) of a candidate bridge; (2) a bridge-level optimization, to identify combinations of optimal or near-optimal element intervention actions for a candidate bridge; and (3) a network-level optimization, following either a top-down or bottom-up approach, to identify sets of optimal or near-optimal element intervention actions for a network of bridges. A robust metaheuristic genetic algorithm (i.e., Non-dominated Sorting Genetic Algorithm II, [NSGA-II]) is deployed to handle the large size of multi-objective optimization problems. A MATLAB-based tool prototype was developed to test concepts, demonstrate effectiveness, and communicate benefits. Several examples of unconstrained and constrained scenarios were established for implementing the methodology using the tool prototype. Results reveal the capability of the proposed EB-MOO methodology to generate a high quality of Pareto optimal or near-optimal solutions, predict performance, and determine appropriate intervention actions and funding requirements. The five modules collectively provide a systematic process for the development and evaluation of improvement programs and transportation plans. Trade-offs between Pareto optimal or near-optimal solutions facilitate identifying best investment strategies that address short- and long-term goals and objective priorities

Operational research IO 2021—analytics for a better world. XXI Congress of APDIO, Figueira da Foz, Portugal, November 7–8, 2021

This book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.info:eu-repo/semantics/publishedVersio

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC