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The article is devoted to the issue of determining the best logistics route when solving operational problems of transport logistics under martial law by a team of volunteers. The article reviews multi-criteria decision-making methods used to solve operational problems of transport logistics. The choice of the best route for transporting passengers or cargo is affected by changes in the state of the external environment, which dynamically changes during military operations. All members of the volunteer team, who have up-to-date information on the conditions of the routes, are involved in the rapid assessment of the state of the external environment. This information is transferred to the coordinator of the volunteer team, who makes a decision on the choice of the logistics route. In the conditions of martial law, both the number of logistics routes and the state of the routes can change dynamically. Thus, the methods used by the volunteer coordinator to support decision-making should be robust to changes in the number of possible logistical routes and their changes. And also have a small computational complexity and support group decision making. The article compared AHP (Analytic Hierarchy) and mSmart methods based on such factors as: adequacy to changes in alternatives or criteria; flexibility in the decision-making process; computational complexity; adequacy of group decision-making support; number of alternative routes and criteria. The task of choosing the best possible route from Odesa to Kherson was considered. The results showed that both methods are suitable for solving the problem of choosing the best route, but the mSmart method turned out to be better in terms of the factors considered in the work. [ABSTRACT FROM AUTHOR]

The paper presents the multinominal logit discrete choice model that allows determining parameters coefficients of the cyclists’ route. The basic model includes six parameters, however, only a number of signalized intersections, speed of motorized traffic and total physical work required from cyclist prove to be significant factors. The model provides a better understanding about cyclist traffic assignment.