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[61410] Artykuł: An Application of Generalized Strength Pareto Evolutionary Algorithm for Finding a Set of Non-Dominated Solutions with High-Spread and Well-Balanced Distribution in the Logistics Facility Location ProblemCzasopismo: International Conference on Artificial Intelligence and Soft Computing Tom: 10245, Strony: 439-450ISSN: 0302-9743 ISBN: 978-3-319-59063-9 Wydawca: SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND Opublikowano: 2017 Seria wydawnicza: Lecture Notes in Artificial Intelligence Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Konferencja Informatyczna Punkty MNiSW: 20 Klasyfikacja Web of Science: Proceedings Paper Pełny tekst DOI Web of Science Keywords: Multi-objective genetic optimization  Finding set of non-dominated solutions with high spread and well-balanced distribution  Logistic facilities location problem  |
The paper presents an application of generalized Strength Pareto Evolutionary Algorithm (SPEA) in the Logistic Facilities Location (LFL) problem. The task is to optimize a distribution network, i.e. the number of distribution centers and their locations as well as the number of clients served by the particular centers in terms of three following contrary/contradictory criteria: (a) the total maintenance cost of the network, (b) carbon emissions emitted by combustion engines of trucks into the atmosphere (subjects to minimization) and (c) the customer service reliability (subject to maximization). For this purpose, an original multi-objective optimization technique which allow to obtain a set of so-called non-dominated solutions of the considered problem, representing different levels of compromise between the above criteria, is applied. In order to provide a broad, flexible selection of the final solution from the obtained set, the proposed approach aims at finding the set of solutions with high spread and well-balanced distribution in the objective (criteria) space. The functionality of our technique is demonstrated using numerical experiments. Its distinct advantages over alternative approaches are presented in the frame of comparative analysis as well.