Информационно-Аналитические Модели и Эволюционные Аспекты Решения Задачи Комплектования

В статье рассмотрена технология решения задачи комплектования аварийно- спасательной техники с использованием многокритериальной оптимизации, последовательного анализа вариантов и эволюционного моделирования. Разработаны модели, служащие информационно- аналитическим базисом формирования интегрального критерия

Формализация задачи комплектования и эволюционные аспекты ее решения

В статье рассмотрена технология решения задачи комплектования аварийно-спасательной техники с использованием многокритериальной оптимизации, последовательного анализа вариантов и эволюционного моделирования. Разработаны модели, служащие информационно-аналитическим базисом формирования интегрального критерия.У статті розглянута технологія розв’язання задачі комплектування аварійно-рятувальної техніки з використанням багатокритеріальної оптимізації, послідовного аналізу варіантів та еволюційного моделювання. Розроблені моделі, які є інформаційно-аналітичним базисом формування інтегрального критерію.In this paper the problem decision technology of a rescue technics acquisition with use multiobjective optimization, the consecutive analysis of variants and evolutionary modelling is considered. The models which are information-analytical basis for forming of integrated criterion are developed

Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach

The Bin Packing Problem is a classic problem with wide industrial applicability. In fact, the efficient packing of items into bins is one of the toughest challenges in many logistic corporations and is a critical issue for reducing storage costs or improving vehicle space allocation. In this work, we resort to our previously published quantum-classical framework known as Q4RealBPP, and elaborate on the solving of real-world oriented instances of the Bin Packing Problem. With this purpose, this paper gravitates on the following characteristics: i) the existence of heterogeneous bins, ii) the extension of the framework to solve not only three-dimensional, but also one- and two-dimensional instances of the problem, iii) requirements for item-bin associations, and iv) delivery priorities. All these features have been tested in this paper, as well as the ability of Q4RealBPP to solve real-world oriented instances.Comment: 7 pages, 7 figures, paper accepted for being presented in the upcoming 26th IEEE International Conference on Intelligent Transportation Systems - ITSC 202

Greedy seeding procedure for GAs solving a strip packing problem

In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.Red de Universidades con Carreras en Informática (RedUNCI

Greedy seeding procedure for GAs solving a strip packing problem

In this paper, the two-dimensional strip packing problem with 3-stage level patterns is tackled using genetic algorithms (GAs). We evaluate the usefulness of a greedy seeding procedure for creating the initial population, incorporating problem knowledge. This is motivated by the expectation that the seeding will speed up the GA by starting the search in promising regions of the search space. An analysis of the impact of the seeded initial population is offered, together with a complete study of the influence of these modifications on the genetic search. The results show that the use of an appropriate seeding of the initial population outperforms existing GA approaches on all the used problem instances, for all the metrics used, and in fact it represents the new state of the art for this problem.Red de Universidades con Carreras en Informática (RedUNCI