![]() ![]() A large body of evidence suggests that metaheuristics could realize this goal to some extent. Since service composition is an NP-hard problem, numerous research aimed to determine optimal or near-optimal solutions within a reasonable budget. Within the realm of big services, performing composition also became a computationally expensive and challenging task. The proliferation of services with expanding quality attributes navigates this problem towards big service compositions, which fall under the umbrella of NP-hard. Quality of Service (QoS)-aware service composition plays an increasingly important role in various computational paradigms and delivery models, predominantly cloud computing. It will also have theoretical impacts on Set Cover Problem, TSP, Vehicular Routing Problem, and Geometry-based optimizations. The thesis will have practical impacts on Sustainability, Smart Cities, IoT, Industry 4.0, and the effective use of Electric Vehicles. ![]() A novel concave hull-based TSP approximation heuristic is also proposed as a part of the Pathfinding Heuristic algorithm. Square and triangular charging station grids are compared in the benchmarks. The classical Shortest Path algorithm is augmented with dynamic constraint edges. For the Pathfinding Heuristics, a combination of custom TSP and custom Shortest Path type heuristics were proposed. The rescue research involved Graph-Theoretic modeling, Probabilistic path analysis, and Pathfinding Heuristics. An approximate solution is searched with Evolutionary Algorithms (Genetic Algorithm, Simulated Annealing, Differential Evolutionary Optimization). The disaster region coverage problem is modeled as the Geometric Set Cover problem. The proposed frameworks are benchmarked in case studies like disaster region coverage for communication and pathfinding for sea rescue operations. In principle, the proposed heuristics either exploit the existing geometric configurations of the entities or introduce geometric regularities for their configurations. Specifically, I proposed multi-party multi-objective (energy, waiting time) optimization frameworks (coverage/pathfinding) in which novel geometry-based heuristics are utilized for drone-based operations. This work enhances the inspection capability of the scanning probe on CMM.įor my doctoral thesis topic, in general, I addressed the issue of limited onboard energy of Electric Vehicles. Unnecessary transition distances are reduced by 31%-75%. The methodology can capture the large form error with the minimum inspection cost. The CLS simulations and scanning experiments are conducted. An Improved Ant Colony Optimization (IACO) is proposed to generate time-saving paths for discrete scanning lines. Secondly, an inspection path planner is utilized to plan local and global inspection paths. Firstly, a Cobweb-Like adaptive Sampling strategy (CLS) is designed to sample the form error by distributing scanning lines in a cobweb-shape mode. Focusing on scanning line distribution and inspection path planning, this paper proposes a two-module scanning inspection planning methodology. With the emergence of the continuous-contact scanning probe, it becomes possible to inspect large-scale scanning lines rapidly without losing accuracy. Benchmarks of the permutation flow shop scheduling problem with the informally derived MIP model and the traveling salesman problem are used to present the limits of the software’s applicability.Ĭoordinate measuring machine (CMM) plays an essential role in the high precision measurement of complex surfaces. The source codes presented may be an aid because this tool is not yet as well known as the MATLAB Optimisation Toolbox. Instead of the traditional approach based on the use of approximate or stochastic heuristic methods, we focus here on the direct use of mixed integer programming models in the GAMS environment, which is now capable of solving instances much larger than in the past and does not require complex parameter settings or statistical evaluation of the results as in the case of stochastic heuristics because the computational core of software tools, nested in GAMS, is deterministic in nature. For some of the derived problems having exponential time complexity, the question arises of their solvability for larger instances. Showing such correlations is one of the aims of this paper. The assignment problem is a problem that takes many forms in optimization and graph theory, and by changing some of the constraints or interpreting them differently and adding other constraints, it can be converted to routing, distribution, and scheduling problems. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |