Identification of weights in multi-cteria decision problems based on stochastic optimization

Many scientific papers are devoted to solving multi-criteria problems using methods that find discrete solutions. However, the main challenge addressed by our work is the case when new decision-making variants have emerged which have not been assessed. Unfortunately, discrete identification makes it impossible to determine the preferences for new alternatives if we do not know the whole set of parameters, such as criteria weights. This paper proposes a new approach to identifying a multi-criteria decision model to address this challenge. The novelty of this work is using a discretization in the space of the problem to identify a continuous decisional model. We present a hybrid approach where the new alternative can be assessed based on stochastic optimization and the TOPSIS technique. The stochastic methods are used to find criteria weights used in the TOPSIS method. In that way, we get assessed easily each new alternative based only on the initial set of evaluated alternatives

Ant-Balanced multiple traveling salesmen: ACO-BmTSP

A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.info:eu-repo/semantics/publishedVersio

Multi-objective optimization for time-based preventive maintenance within the transport network: a review

Preventive maintenance in transportation is essential not only to safeguard billions in business and infrastructure investment, but also to guarantee safety, reliability and efficacy within the network. Government, industry and society have been increasingly recognising the importance of keeping transport units condition well-preserved. The challenge, however, is to achieve optimal performance of the existing transport systems within acceptable costs, effective workforce use and minimum disruption. Those are generally conflicting objectives. Multi-objective optimisation approaches have served as powerful tools to assist stakeholders to properly deploy preventive maintenance in industry. In this study, we review the research conducted in the application of multi-objective optimisation for preventive maintenance in transport-related activities. We focus on time-based preventive maintenance for production, infrastructure, rail and energy providers. In our review, we are interested in aspects such as the types of problems addressed, the existing objectives, the approaches to solutions, and how the outcomes obtained support decision

Новые подходы к управлению ценами на транспортные услуги

Development of new approaches to formation of analytics mechanisms for the purpose of pricing management of services is an important aspect of increasing the efficiency of transport management processes.Research aimed at improving the tools for determining the optimal parameters of the ratio of quality and price of service for formation of a competitive and efficient tariff policy continues to remain relevant and in demand in modern market conditions. The objective of the study, presented in the article, is to analyse and evaluate the prospects for implementation of the areas to improve the apparatus for assessing the price elasticity of demand for railway passenger transport services as the transition to the use of non-linear parameters in terms of customer behaviour modelling functions, as well as introduction of the most effective algorithms from the set of modern global mathematical optimisation tools.The research conclusions are based on the use of system analysis mechanisms, methods of economic and mathematical modelling and optimisation, as well as of non-parametric statistics tools.The results based on the use of an array of data on the demand of passengers of branded trains include: a comparative assessment of quality of modelling the price elasticity of demand using 15 functions that are nonlinear in terms of parameters; the most promising tools of the search for unknown parameters for non-smooth nonlinear functions for modelling the behaviour of railway customers are identified based on a three-stage procedure for comparative analysis of the performance of more than 60 optimisation algorithms (including the calculation of minima and medians for the sums of squares of modelling errors, bootstrap analysis, Kruskal– Wallace and Mann–Whitney tests, as well as the calculation of a metric specially developed by the authors for assessing the degree of superiority of one algorithm over another within the framework of non-parametric analysis).The findings seem able to be successfully used in relation to other modes of transport in solving similar problems of developing an effective toolkit for managing the prices of transport services.Важным аспектом повышения эффективности процессов управления на транспорте является развитие новых подходов к формированию механизмов аналитики для целей управления ценами услуг.В современных рыночных условиях продолжают оставаться актуальными и востребованными исследования, направленные на совершенствование инструментария определения оптимальных параметров соотношения качества и стоимости обслуживания для формирования конкурентоспособной и эффективной тарифной политики.Цель исследования, представленного в статье, – анализ и оценка перспектив реализации таких направлений по совершенствованию аппарата оценки ценовой эластичности спроса на услуги железнодорожного пассажирского транспорта, как переход к использованию нелинейных по параметрам функций моделирования поведения клиентов, а также внедрение наиболее эффективных алгоритмов из арсенала современного инструментария глобальной математической оптимизации.Формирование выводов исследования основывается на применении механизмов системного анализа, методов экономико-математического моделирования и оптимизации, а также инструментария непараметрической статистики.В итоге, на основе использования массива данных о спросе пассажиров фирменных поездов проведена сравнительная оценка качества моделирования ценовой эластичности спроса при использовании 15 нелинейных по параметрам функций, а также, в результате осуществления трёхэтапной процедуры сравнительного анализа эффективности работы более чем 60 алгоритмов оптимизации (включающей, в том числе, расчёт минимумов и медиан для сумм квадратов ошибок моделирования, бутстреп-анализ, тесты Краскела–Уоллеса и Манна–Уитни, а также расчёт специально разработанной авторами метрики оценки степени превосходства одного алгоритма над другим в рамках непараметрического анализа) определены наиболее перспективные механизмы поиска неизвестных параметров для негладких нелинейных функций моделирования поведения клиентов железнодорожного транспорта.Представляется, что полученные выводы могут быть успешно использованы и применительно к другим видам транспорта при решении ими аналогичных задач формирования эффективного инструментария управления ценами транспортных услуг