Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique

AbstractThe multiple-objective resource allocation problem (MORAP) seeks for an allocation of resource to a number of activities such that a set of objectives are optimized simultaneously and the resource constraints are satisfied. MORAP has many applications, such as resource distribution, project budgeting, software testing, health care resource allocation, etc. This paper addresses the nonlinear MORAP with integer decision variable constraint. To guarantee that all the resource constraints are satisfied, we devise an adaptive-resource-bound technique to construct feasible solutions. The proposed method employs the particle swarm optimization (PSO) paradigm and presents a hybrid execution plan which embeds a hill-climbing heuristic into the PSO for expediting the convergence. To cope with the optimization problem with multiple objectives, we evaluate the candidate solutions based on dominance relationship and a score function. Experimental results manifest that the hybrid PSO derives solution sets which are very close to the exact Pareto sets. The proposed method also outperforms several representatives of the state-of-the-art algorithms on a simulation data set of the MORAP

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

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

Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched