Metaheuristic Algorithms for Spatial Multi-Objective Decision Making

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when compared to their standard version and other algorithms. Moreover, Paper IV proposed an urban land-use allocation model that involved three spatial objectives and proposed an improved Non-dominated Sorting Biogeography-based Optimization algorithm (NSBBO) to solve the problem efficiently and effectively.Overall, the work in this thesis demonstrates that different metaheuristic algorithms have the potential to change the way spatial decision problems are structured and can improve the transparency and facilitate decision-makers to map solutions and interactively modify decision preferences through trade-offs between multiple objectives. Moreover, the obtained results can be used in a systematic way to develop policy recommendations. From the perspective of GIS - Multi-Criteria Decision Making (MCDM) research, the thesis contributes to spatial optimization modelling and extended knowledge on the application of metaheuristic algorithms. The insights from this thesis could also benefit the development and practical implementation of other Artificial Intelligence (AI) techniques to enhance the capabilities of GIS for tackling complex spatial multi-objective decision problems in the future

Cross-Layer Optimization and Dynamic Spectrum Access for Distributed Wireless Networks

We proposed a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop communication links using the partial operating parameter and environmental information from adjacent devices within the wireless network. Assuming stationary wireless nodes, all wireless communication links employ non-contiguous orthogonal frequency division multiplexing (NC-OFDM) in order to enable dynamic spectrum access (DSA). The proposed approach will attempt to simultaneously minimize the bit error rate, minimize out-of-band (OOB) interference, and maximize overall throughput using a multi-objective fitness function. Without loss in generality, genetic algorithms are employed to perform the actual optimization. Two generic optimization approaches, subcarrier-wise approach and block-wise approach, were proposed to access spectrum. We also proposed and analyzed several approaches implemented via genetic algorithms (GA), such as quantizing variables, using adaptive variable ranges, and Multi-Objective Genetic Algorithms, for increasing the speed and improving the results of combined spectrum utilization/cross-layer optimization approaches proposed, together with several assisting processes and modifications devised to make the optimization to improve efficiency and execution time

The sustainable home health care process based on multi-criteria decision-dupport

The increase in life expectancy has led to a growing demand for Home Health Care (HHC) services. However, some problems can arise in the management of these services, leading to high computational complexity and time-consuming to obtain an exact and/or optimal solution. This study intends to contribute to an automatic multi-criteria decision-support system that allows the optimization of several objective functions simultaneously, which are often conflicting, such as costs related to travel (distance and/or time) and available resources (health professionals and vehicles) to visit the patients. In this work, the HHC scheduling and routing problem is formulated as a multi objective approach, aiming to minimize the travel distance, the travel time and the number of vehicles, taking into account specific constraints, such as the needs of patients, allocation variables, the health professionals and the transport availability. Thus, the multi-objective genetic algorithm, based on the NSGA-II, is applied to a real-world problem of HHC visits from a Health Unit in Bragança (Portugal), to identify and examine the different compromises between the objectives using a Pareto-based approach to operational planning. Moreover, this work provides several efficient end-user solutions, which were standardized and evaluated in terms of the proposed policy and compared with current practice. The outcomes demonstrate the significance of a multi-criteria approach to HHC services.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021) and ALGORITMI Research Centre / LASI (UIDB/00319/2020). Filipe Alves thanks the FCT for supporting its research with the Ph.D. grant SFRH/BD/143745/2019.info:eu-repo/semantics/publishedVersio

A decision support tool (R-SWAT-DS) for integrated watershed management

Best management practices (BMPs) can be used effectively to reduce nutrient and sediment loads generated from point sources or non-point sources to receiving water bodies. Methodologies for optimal, cost effective BMP selection and placement are needed to assist watershed management planners and stakeholders. We developed a modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. The framework integrates the Soil and Water Assessment Tool (SWAT) watershed model, spatial representation of BMPs, an economic component, and multi-objective optimization routines in the R environment. The framework can be used to launch individual or iterative BMPs simulations, or search for optimal strategies. Advanced plotting, mapping and statistical analysis functionalities that facilitate the interpretation and assessment of the results are included

Revisión de la optimización de Bess en sistemas de potencia

The increasing penetration of Distributed Energy Resources has imposed several challenges in the analysis and operation of power systems, mainly due to the uncertainties in primary resource. In the last decade, implementation of Battery Energy Storage Systems in electric networks has caught the interest in research since the results have shown multiple positive effects when deployed optimally. In this paper, a review in the optimization of battery storage systems in power systems is presented. Firstly, an overview of the context in which battery storage systems are implemented, their operation framework, chemistries and a first glance of optimization is shown. Then, formulations and optimization frameworks are detailed for optimization problems found in recent literature. Next, A review of the optimization techniques implemented or proposed, and a basic explanation of the more recurrent ones is presented. Finally, the results of the review are discussed. It is concluded that optimization problems involving battery storage systems are a trending topic for research, in which a vast quantity of more complex formulations have been proposed for Steady State and Transient Analysis, due to the inclusion of stochasticity, multi-periodicity and multi-objective frameworks. It was found that the use of Metaheuristics is dominant in the analysis of complex, multivariate and multi-objective problems while relaxations, simplifications, linearization, and single objective adaptations have enabled the use of traditional, more efficient, and exact techniques. Hybridization in metaheuristics has been important topic of research that has shown better results in terms of efficiency and solution quality.La creciente penetración de recursos distribuidos ha impuesto desafíos en el análisis y operación de sistemas de potencia, principalmente debido a incertidumbres en los recursos primarios. En la última década, la implementación de sistemas de almacenamiento por baterías en redes eléctricas ha captado el interés en la investigación, ya que los resultados han demostrado efectos positivos cuando se despliegan óptimamente. En este trabajo se presenta una revisión de la optimización de sistemas de almacenamiento por baterías en sistemas de potencia. Pare ello se procedió, primero, a mostrar el contexto en el cual se implementan los sistemas de baterías, su marco de operación, las tecnologías y las bases de optimización. Luego, fueron detallados la formulación y el marco de optimización de algunos de los problemas de optimización encontrados en literatura reciente. Posteriormente se presentó una revisión de las técnicas de optimización implementadas o propuestas recientemente y una explicación básica de las técnicas más recurrentes. Finalmente, se discutieron los resultados de la revisión. Se obtuvo como resultados que los problemas de optimización con sistemas de almacenamiento por baterías son un tema de tendencia para la investigación, en el que se han propuesto diversas formulaciones para el análisis en estado estacionario y transitorio, en problemas multiperiodo que incluyen la estocasticidad y formulaciones multiobjetivo. Adicionalmente, se encontró que el uso de técnicas metaheurísticas es dominante en el análisis de problemas complejos, multivariados y multiobjetivo, mientras que la implementación de relajaciones, simplificaciones, linealizaciones y la adaptación mono-objetivo ha permitido el uso de técnicas más eficientes y exactas. La hibridación de técnicas metaheurísticas ha sido un tema relevante para la investigación que ha mostrado mejorías en los resultados en términos de eficiencia y calidad de las soluciones

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making

Disaster Management Cycle-Based Integrated Humanitarian Supply Network Management

While logistics research recently has placed increased focus on disruptionmanagement, few studies have examined the response and recovery phases in post-disaster operations. We present a multiple-objective, integrated network optimizationmodel for making strategic decisions in the supply distribution and network restorationphases of humanitarian logistics operations. Our model provides an equity- or fairness-based solution for constrained capacity, budget, and resource problems in post-disasterlogistics management. We then generate efficient Pareto frontiers to understand the trade-off between the objectives of interest.Next, we present a goal programming-based multiple-objective integratedresponse and recovery model. The model prescribes fairness-based compromise solutionsfor user-desired goals, given limited capacity, budget, and available resources. Anexperimental study demonstrates how different decision making strategies can beformulated to understand important dimensions of decision making.Considering multiple, conflicting objectives of the model, generating Pareto-optimal front with ample, diverse solutions quickly is important for a decision maker tomake a final decision. Thus, we adapt the well-known Non-dominated Sorting GeneticAlgorithm II (NSGA-II) by integrating an evolutionary heuristic with optimization-basedtechniques called the Hybrid NSGA-II for this NP-hard problem. A Hypervolume-basedtechnique is used to assess the algorithm’s effectiveness. The Hazards U.S. Multi-Hazard(Hazus)-generated regional case studies based on earthquake scenarios are used todemonstrate the applicability of our proposed models in post-disaster operations

Meta-heuristic based Construction Supply Chain Modelling and Optimization

Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems