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

Dynamic resectorization to improve utility of healthcare systems

Balancing is an essential challenge in healthcare systems that requires effective strategies. This study aims to address this crucial issue by suggesting a practical approach. We show the potential of balancing a regional healthcare system to improve its utility. We consider a regional healthcare system comprising multiple hospitals with different sizes, capacities, quality of service, and accessibility. We define a utility function for the system based on the sectorization concept, which endeavors to form a balance between hospitals in terms of essential outputs such as waiting times and demands. The dynamic nature of the system means that this balance degrades over time, necessitating periodic sectorization, which is called resectorization. Our methodology stands out for incorporating resectorization as a dynamic strategy, enabling more flexible and responsive adaptations to continuously changing healthcare needs. Unlike previous studies, based on a system-oriented approach, our resectorization scenarios include the periodic closure of some hospitals. This enables us to enhance both the capacity and quality of healthcare facilities. Furthermore, in contrast to other studies, we investigate the states of diminishing demand throughout the resectorization process. To provide empirical insights, we conduct a simulation using data from a real-world case study. Our analysis spans multiple time periods, enabling us to dynamically quantify the utility of the healthcare system. The numerical findings demonstrate that substantial utility improvements are attainable through the defined scenarios. The study suggests a practical solution to the critical challenge of balancing issues in regional healthcare systems.info:eu-repo/semantics/publishedVersio

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

Design of in-building wireless networks deployments using evolutionary algorithms

In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based Evolutionary Algorithm) is a hybr id evolutionary algorithm adapted to use a novel fitness function, based on the definition of quality zones for the different objective functions considered. This approach is conceived to solve wireless network design problems without previous information of the required number of transmitters, considering simultaneously a high number of objective functions and optimizing multiple configuration parameters of the transmitters

Multi-objective design-for-control of resilient water distribution networks

Resilience, which defines the ability of a system to maintain continuous customer supply, is a key driver in the management of water distribution networks (WDN). The research programme investigates the optimal design-for-control (DfC) problem which consists in simultaneously selecting new valves and pipes for installation, and optimizing control valve settings in existing WDNs, to minimize pressure induced pipe stress and leakage, and maximize resilience. The problem is illustrated with practical DfC applications on case study networks and a large-scale operational system. First, the thesis focuses on the single-objective DfC problem for the minimization of pressure induced pipe stress and leakage, or the maximization of resilience, represented, respectively, by the average zone pressure (AZP) and the resilience index (Ir), which are both increasing functions of the pressure at network nodes. A tailored spatial branch-and-bound (sBB) algorithm is developed to compute a feasible solution of the resulting non-convex mixed-integer non-linear program (MINLP), with global optimality bounds. Iterative bound tightening is implemented, to accelerate the convergence of the algorithm, and an equivalent reformulation of Ir is introduced, to facilitate the computation of global bounds. Next, we consider jointly the conflicting objectives of AZP minimization and Ir maximization. Using the method of epsilon-constraints, the resulting non-convex bi-objective mixed-integer non-linear program (BOMINLP) is reformulated as a sequence of non-convex MINLPs, which are solved using sBB. The approach returns a set of feasible trade-off solutions between AZP and Ir, along with global non-dominance bounds, in the form of an outer approximation of the Pareto front. Finally, we investigate the dynamic aggregation of DMAs in a large-scale, sectorized network. Due to the computational complexity of the DfC problem, the previous global methods scale badly to large network instances, and a heuristic method is developed, based on the solution of the problem on a reduced network model. The results of the numerical experiment show that dynamic DMA aggregation improves network resilience while minimizing pressure induced pipe stress and leakage.Open Acces

Learning-based perception and control with adaptive stress testing for safe autonomous air mobility

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, successfully bringing such vehicles and airspace operations to fruition will require introducing orders-of-magnitude more aircraft to a given airspace volume, and the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. Although there are existing solutions for communication technology, onboard computing capability, and sensor technology, the computation guidance algorithm to enable safe, efficient, and scalable flight operations for dense self-organizing air traffic still remains an open question. In order to enable safe and efficient autonomous on-demand free flight operations in this UAM concept, a suite of tools in learning-based perception and control systems with stress testing for safe autonomous air mobility is proposed in this dissertation. First, a key component for the safe autonomous operation of unmanned aircraft is an effective onboard perception system, which will support sense-and-avoid functions. For example, in a package delivery mission, or an emergency landing event, pedestrian detection could help unmanned aircraft with safe landing zone identification. In this dissertation, we developed a deep-learning-based onboard computer vision algorithm on unmanned aircraft for pedestrian detection and tracking. In contrast with existing research with ground-level pedestrian detection, the developed algorithm achieves highly accurate multiple pedestrian detection from a bird-eye view, when both the pedestrians and the aircraft platform are moving. Second, for the aircraft guidance, a message-based decentralized computational guidance algorithm with separation assurance capability for single aircraft case and multiple cooperative aircraft case is designed and analyzed in this dissertation. The algorithm proposed in this work is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For the multiple cooperative aircraft case, a novel coordination strategy is introduced by using the logit level-$k$ model in behavioral game theory. To achieve higher scalability, we introduce the airspace sector concept into the UAM environment by dividing the airspace into sectors, so that each aircraft only needs to coordinate with aircraft in the same sector. At each decision step, all of the aircraft will run the proposed computational guidance algorithm onboard, which can guide all the aircraft to their respective destinations while avoiding potential conflicts among them. In addition, to make the proposed algorithm more practical, we also consider the communication constraints and communication loss among the aircraft by modifying our computational guidance algorithms given certain communication constraints (time, bandwidth, and communication loss) and designing air-to-air and air-to-ground communication frameworks to facilitate the computational guidance algorithm. To demonstrate the performance of the proposed computational guidance algorithm, a free-flight airspace simulator that incorporates environment uncertainty is built in an OpenAI Gym environment. Numerical experiment results over several case studies including the roundabout test problem show that the proposed computational guidance algorithm has promising performance even with the high-density air traffic case. Third, to ensure the developed autonomous systems meet the high safety standards of aviation, we propose a novel, simulation driven approach for validation that can automatically discover the failure modes of a decision-making system, and optimize the parameters that configure the system to improve its safety performance. Using simulation, we demonstrate that the proposed validation algorithm is able to discover failure modes in the system that would be challenging for humans to find and fix, and we show how the algorithm can learn from these failure modes to improve the performance of the decision-making system under test

Decision Support Algorithms for Sectorization of Water Distribution Networks

Many water utilities, especially ones in developing countries, continue to operate low efficient water distribution networks (WDNs) and are consequently faced with significant amount of water (e.g. leakage) and revenue losses (i.e. non-revenue water – NRW). First step in reducing the NRW is assessment of water balance in WDN aimed to establish the baseline level of water losses. Then, water utilities can plan NRW reduction activities according to this baseline. Sectorization of WDN into District Metered Areas (DMAs) is the most cost-effective strategy used for active leakage (i.e. water loss) control, achieved by monitoring the flow data on DMAs’ boundaries. Sectorization of WDN has to be designed carefully, as required network interventions can endanger network’s water supply and pressure distribution. In this thesis new methods and algorithms, aimed to support making more effective and objective decisions regarding the WDN sectorization procedure, are presented, tested and validated. Presented methods and algorithms are part of proposed decision support methodology compensating for disadvantages in available methods, valuable to practicing engineers commencing implementation of sectorization strategy in WDN. Main sectorization objective adopted in methodology presented in this thesis is to design layout of DMAs that will allow efficient tracking of water balance in the network. Least investment for field implementation and maintaining the same level of WDN’s operational efficiency are adopted as main design criteria. New sectorization algorithm, named DeNSE (Distribution Network SEctorization), is developed and presented, adopting above-named objective and design criteria. DeNSE algorithm utilizes newly developed uniformity index which drives the sectorization process and identifies clusters. New engineering heuristic is developed and used for placing the flow-meters and isolation valves on clusters’ boundary edges, making them DMAs. Post sectorization operational efficiency of WDN is evaluated using adopted performance indicators (PIs). Top-down approach to hierarchical sectorization of WDN, particulary convenient for water utilities constrained with limited funding and insufficient reliable input data, is also implemented in DeNSE algorithm. New method for hydraulic simulation, named TRIBAL-DQ is developed to address the issue of low computational efficiency, recognized in available sectorization methodologies employing optimization. TRIBAL-DQ is a loop-flow based method which combines the novel TRIangulation Based ALgorithm (TRIBAL) for loop identification with efficient implementation of the loop-flow hydraulic solver (DQ). TRIBAL-DQ method is tested on various networks of different complexities and topologies. This thesis reports only results of testing on literature benchmark networks, used to validate methods’ performance. TRIBAL-DQ method based hydraulic solver is compared to the node based solver implemented in EPANET, most prominent software for hydraulic calculation of WDN. New TRIBAL-DQ solver showed significant dominance in computational efficiency, with stable numerical performance and same level of prediction accuracy. DeNSE algorithm is benchmarked against other available sectorization methodologies on real-sized WDN. Obtained results demonstrate the ability of DeNSE algorithm to identify good set of feasible solutions, without worsening operational status of the WDN compared to its baseline condition. Reported computational efficiency of the algorithm is one of its strong points, as it allows generation of feasible solutions for large WDN in reasonable time. In this field, algorithm particularly outperforms methods employing multi-objective optimization (e.g. minutes compared to hours).Комунална предузећа која управљају водоводним системима, нарочита она у земљама у развоју, суочена су са проблемима дотрајале и лоше одржаване дистрибутивне мрже који за последицу имају значајне количине воде која се губи у дистрибуцији. Први корак ка смањењу губитака у водоводном систему је процена водног биланса у дистрибутивној мрежи како би се утврдило почетно стање система, а затим и приступило планирању и предузимању мера за смањење губитака како би се то стање поправило. Најисплативија, и опште прихваћена, стратегија за остваривање овог циља је подела дистрибутивне мреже, односно њена секторизација, на тзв. основне зоне билансирања (ОЗБ). ОЗБ се у мрежи успостављају јасним дефинисањем њихових граница, на којима се инсталирају изолациони затварачи и мерачи протока. Избор ОЗБ није једнозначан, и приликом њиховог дефинисања мора се водити рачуна о планираним интервенцијама у мрежи које могу имати негативан утицај на водоснабдевање потрошача и распоред притисака у мрежи. У овој дисератацији су приказане и тестиране нове методе и алгоритми намењени за подршку одлучивању приликом секторизације водоводне дистрибутивне мреже на ОЗБ. Презентоване методе и алгоритми надомешћују недостатке постојећих метода и могу бити од користи инжењерима који се у пракси баве задатком секторизације дистрибутивних мрежа. Основни циљ методологије за секторизацију приказане у овој дисертацији је дефинисање распореда ОЗБ који ће омогућити ефикасно праћење водног биланса у дистрибутивној мрежи. Основни критеријуми за вредновање и избор оптималног решења су минимална улагања у неопходне интервенције у мрежи и очување поузданости система. У дисертацији је приказан нови алгоритам за секторизацију водоводне мреже, назван DeNSE (Distribution Network SEctorization), заснован на претходно наведеном основном циљу и критеријумима. Секторизација применом DeNSE алгоритма је базирана на употреби новог индекса униформности мреже, који омогућава идентификацију зона у мрежи уједначених према потрошњи. За дефинисање ОЗБ, на границе претходно идентификованих зона потребно је поставити мераче протока и изолационе затвараче. За ове потребе развијена је и приказана методлогија засновна на практичним инжењерским принципима. За процену поузданости система након секторизације коришћени су усвојени индикатори перформанси (PIs – Performance Indicators). Предвиђена је и могућност за хијерархијску секторизацију дистрибутивне мреже, нарочито привлачна за комунална предузећа која располажу ограниченим финансијским средствима и имају потребу да процес секторизације изведу у неколико фаза. Услед проблема са значајним рачунарским временом који имају постојеће методе за секторизацију које користе оптимизацију, у оквиру истраживања је развијен и нови метод за хидраулички прорачун мрежа под притиском, назван TRIBAL-DQ. TRIBAL-DQ метод је заснован на примени новог алгоритма за идентификацију прстенова у мрежи базираног на триангулацији (TRIBAL – TRIangulation Based ALgorithm) и ефикасној имплементацији нумеричког модела хидрауличког прорачуна базираног на методи прстенова (DQ). TRIBAL-DQ метод је тестиран на бројним дистрибутивним мрежама различите сложености. У овој дисертацији су приказани само резултати добијени применом на тест-мрежама познатим из литературе, како би се потврдила њихова ваљаност. TRIBAL-DQ метод је упоређен са методом коју користи најпознатији софтвер за хидраулички прорачун мрежа под притиском – EPANET. Резултати приказују значајну предност новог метода у погледу рачунарске ефикаснонсти, уз очување нумеричке стабилности и тачности решења хидрауличког прорачуна. DeNSE алгоритам је упоређен са постојећим методама за секторизацију дистрибутивних мрежа. Резултати потврђују да је нови алгоритам у стању да идентификује скуп могућих решења, која не угрожавају поузданост система и снабдевање потрошача. Рачунарска ефикаснонст DeNSE алгоритма је једна од његових најзначајнијих предности јер омогућава идентификацију не једног, већ скупа могућих решења за реалне дистрибутивне мреже у релативно кратком рачунарском времену. Ова чињеница посебно долази до изражаја када се рачунарско време DeNSE алгоритма упореди са рачунарским временом метода које користе оптимизационе алгоритме (минути у поређењу са сатима).Belgrade: University of Belgrade-Faculty of Civil Engineerin

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems