On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems

Conducting stochastic-time-cost-tradeoff-problem (STCTP) analysis beneficially extends the scope of discrete project duration-cost analysis for oil and gas field development projects. STCTP can be particularly insightful when using a dual-objective optimization approach to locate minimum-total-project-cost solutions, and to additionally derive a Pareto frontier of non-dominated-total-project-cost solutions across a wide range of potential project durations. For STCTP project-work-item durations and costs are expressed as probability distributions and sampled with random numbers (0, 1). By controlling the fractional numbers used to sample the work-item cost distributions by formulas linked to the random numbers used to sample the work-item duration distribution, a wide range of complex time-cost relationships are readily applied. The memetic algorithm developed for constrained STCTP involves ten metaheuristics configured to focus partly on local exploitation and partly on exploration of the feasible solution space. This dual focus effectively delivers the dual objective of: 1) locating the global minimum total-project- cost solution, if it exists, or the region in the vicinity of where that solution exists; and, 2) developing a Pareto frontier. Analysis of an example project, applying eight distinct work-item time-cost relationships, demonstrates with the aid of metaheuristic profiling, that the memetic STCTP algorithm coded in Visual Basic for Applications and operated in Microsoft Excel effectively delivers on both objectives. Dynamic adjustment factors applied by some metaheuristics, derived from fat-tailed distributions adjusted by chaotic sequences, aid the efficient sampling of the feasible solution space. The metaheuristic profiles also help to fine tune the configuration of the algorithm to further enhance performance for specific work-item time-cost relationships.Cited as: Wood, D.A. Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems. Advances in Geo-Energy Research, 2018, 2(1): 14-33, doi: 10.26804/ager.2018.01.0

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Cooperation of Combinatorial Solvers for Air Traffic Management and Control

In the context of the SESAR project, Air Traffic Control (ATC) and Management (ATM) in Europe is undergoing a paradigm shift to be able to accommodate the current traffic growth forecast: many expert-based systems will be enhanced by optimization software to improve the decisionmaking process and regulation planning. Current state-of-the-art combinatorial optimization techniques that are applied to ATC and ATM include approximation algorithms like metaheuristics (e.g. Genetic Algorithm, Tabu Search, Simulated Annealing, etc.) and complete algorithms like Constraint Programming (CP) and Mixed Integer Programming. However, the large scale of the considered instances and the handling of their inherent uncertainties result in very hard problems, which can hinder or even defeat either of the previously mentioned optimization methods alone. To overcome these difficulties and improve the resolution efficiency of standard algorithms, we propose to study the generic cooperation of any set of combinatorial solvers by sharing solutions, optimization bounds and possibly other information in order to speed up the overall process. In this thesis, we have specified and implemented a distributed system which is able to integrate any combinatorial solver with the suitable interface, adapt existing solvers to take into account and provide information on the state of the search from and to other solvers, and applied this framework to two ATC and ATM problems: the en-route conflict resolution problem and the Gate Allocation Problem (GAP). For the first one, we have presented a new generic framework for the modeling and resolution of en-route conflicts in three dimensions as well as a large set of realistic instances, which have been solved with the cooperation of a Memetic Algorithm and Integer Linear Programming (ILP) solver. For the GAP, we have presented a new CP model, as well as new optimization constraints to maximize the robustness of the schedule, and search strategies together with their parallel cooperation. The solver, implemented with the FaCiLe CP library, outperforms a state-of-the-art ILP solver on real instances

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems

La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados. Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo. Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida. Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has conducted the research community to face historical complex problems by devising UGV-UAV cooperation paradigms. However, it is usually not a trivial task to determine whether or not a UGV-UAV cooperation is suitable for a particular problem. For this reason, in this thesis, we investigate a particular UGV-UAV cooperation paradigm over two problems in the literature, and we propose an autonomous controller to test it on simulated scenarios. Driven by the planetary exploration, we formulate a particular cooperative exploration problem consisting of reaching a set of target points in a large-scale exploration area. This problem defines the UGV as a moving charging station to carry the UAV through different locations from where the UAV can reach the target points. Consequently, we propose the cooperaTive ExploRation Routing Algorithm (TERRA) to solve it. This algorithm stands out for splitting up the exploration problem into five sub-problems, in which each sub-problem is solved in a particular stage of the algorithm. In the same way, driven by the explosion of parcels delivery in e-commerce companies, we formulate a generalization of the well-known last-mile delivery problem. This generalization defines the same UGV’s and UAV’s rol as the exploration problem. That is, the UGV acts as a moving charging station which carries the parcels along several UAVs to deliver them. In this way, we follow the split strategy depicted by TERRA to propose the COoperative Unmanned deliveRIEs planning algoRithm (COURIER). This algorithm replicates the first four TERRA’s stages, but it builds a new fifth stage to produce a task plan solving the problem. In order to evaluate the UGV-UAV cooperation paradigm on simulated scenarios, we propose the Autonomous coopeRatIve Execution System (ARIES). This controller follows a hierarchical decentralized leader-follower approach to integrate any cooperation paradigm in a distributed manner. Both algorithms have been characterized to identify the relevant aspects of the cooperation paradigm in the related problems. Also, both of them demonstrate a great performance of the cooperation paradigm in such problems, and as well as the autonomous controller, reveal a great potential for future real applications

Flight Planning Tool an Aid for Efficient Flight Evaluation

Airborne surveillance systems have multiple sensors and communication links on board a suitable platform. They work in a cohesive manner to provide effective surveillance over the region of interest. The performance proving of such a system is challenging and requires flight trails extending over years. The test results often have to be interpreted using statistical analysis of the flight test data. An efficient way is to carefully design the flight test profiles such that enough samples can be collected during the test and multiple requirements can be tested in a single sortie. Such meticulous test strategies where both own ship platform and test targets are moving with high dynamics call for software based tool for planning of test sorties and the test points. Flight Planning Tool (FPT) plays an important role in pre-flight stage during developmental trials for analysis of the MOEs and MOPs of overall system and of various on-board sensors of an airborne multi-sensor system. The FPT provides statistical & graphical analysis for sensor behaviour for various scenarios (flight trials) before actual flight test is conducted. It provides prior information on number of valid samples for sensor testing during flight trials. In addition, the tool aids in assessing number of profiles to be flown for proving each MOE. The profiles can also be optimised such that valid samples are collected for evaluation