Decision-making is a ubiquitous and paramount issue in the modern business world. Inappropriate decisions may lead to severe consequences for companies. Considering that the evaluation of alternatives is generally affected by several criteria, decision-making should be considered a very challenging task. From the 1980s to the present day, various multi-criteria decision-making (MCDM) methods have evolved, supporting people in the decision-making process. The main aim of this paper is to propose an original MCDM method and to demonstrate its applicability in an empirical case study. To solve the electric vehicle selection problem for the last-mile delivery, we developed and applied a new MCDM method - the AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization) method. To demonstrate the robustness of the proposed method, a comparative analysis with other state-of-the-art MCDM methods is conduct-ed. The results indicate a high level of confidence in the AROMAN method in the decision-making field. In addition, the sensitivity analysis is performed, and the results indicate a high level of stability.

Nowadays, cargo bikes play a vital role in the last-mile delivery process. Parcel distribution by cargo bikes becomes a more accessible and ecologically friendly solution. This paper addresses the investment decision on the cargo bike delivery concept selection problem. We investigated a better solution in terms of whether the company needs to perform the delivery by investing in its fleet of cargo bikes or renting cargo bikes from a third party. The third solution is to combine those two alternatives. This case considers four criteria: cargo bike procurement cost, cargo bike maintenance cost, return on investment, and financial profitability. To solve this problem, we applied the extended alternative ranking order method accounting two-step normalization (AROMAN) method. The results compared with the MARCOS and ARAS methods confirmed that delivery concept 2 (i.e. renting cargo bikes from third-party providers) represented the best solution for the e-commerce company.

Companies can perform their freight distribution in three different ways. The first concept, the in-house concept, represents the use of a company’s own resources and knowledge to organize transportation from the production to retailers or from the warehouse to customers. The opposite concept is to outsource distribution activities by hiring third-party logistics providers. The third concept represents a combination of the previous two. Although the arguments in favor of outsourcing can be found in the literature, an appropriate selection of a freight distribution concept is specific for each company and depends on many evaluation criteria and their symmetrical roles. This paper presents a methodology that can be used by companies that need to choose their freight distribution concept. An advanced extension of the Additive Ratio ASsessment (ARAS) method is developed to solve the freight distribution concept selection problem. To illustrate the implementation of the proposed methodology, a tire manufacturing company from the Czech Republic is taken as a case study. However, the proposed picture fuzzy ARAS method is general and can be used by any other company. To validate the novel picture fuzzy ARAS method, a comparative analysis with the nine existing state-of-the-art picture fuzzy multi-criteria decision-making methods is provided.

Sustainable Last-Mile Delivery (LMD) is one of the key phases in city logistics. Micro-hubs in cities are new emerging solutions for an easier and viable last-mile delivery process. The important question in smart and modern cities is the determination of the best micro-hub location for the LMD. This paper solves the micro-hub location selection for sustainable LMD using the multi-criteria decision-making (MCDM) techniques. The main reason for solving the micro-hub location selection is to make the last-mile delivery process in Pardubice as easier and effortless as possible. The Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC) method, and Weighted Aggregated Sum Product Assessment (WASPAS) method are coupled to solve the micro-hub location selection for sustainable LMD. First, five criteria and alternatives are identified and discussed with the experts. Second, the hybrid criteria importance is determined by combining the BWM and CRITIC methods. Third, the obtained hybrid weights are integrated within the WASPAS method to rank the micro-hub locations. The findings of the Hybrid BWM-CRITIC-WASPAS model show the Alternative 2 („Hůrka“) as the best possible location for Pardubice in the context of the LMD. In addition, a comparative analysis with some of the existing MCDM approaches is conducted for the same problem and its results show a high level of matching with the applied hybrid BWM-CRITIC-WASPAS method, which means that Alternative 2 („Hůrka“) is strongly recommended as a micro-hub location for sustainable LMD in Pardubice.

The selection of a third-party logistics (3PL) provider is an important and demanding task for many companies and organizations dealing with distribution activities. To assist in decision making, this paper proposes the implementation of fuzzy logic. To design a fuzzy inference system (FIS), the first prerequisite is to determine a set of evaluation criteria and sub-criteria and to find the relationship between them. This task was solved by an extensive review of the literature and expert opinions on implementing the Fuzzy Analytic Hierarchy Process (AHP) approach. The results obtained in the first part of the research, together with data collected from 20 3PL providers, were further used in the second part, which was related to the implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, a decision-making tool for 3PL provider selection was designed as an FIS structure, where the inputs were the previously defined criteria and the output was a preference for 3PL selection. The fuzzy rules were generated on the basis of the collected empirical data, the preferences obtained by the TOPSIS method, and expert opinion using the Wang–Mendel method. The proposed fuzzy model is particularly suitable when input data are not crisp values but are provided descriptively through linguistic statements.