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.

Waste management is gaining very high importance in recent years. As automotive is one of the most critical sectors worldwide, which is rapidly increasing, the management of end-of-life vehicles (ELVs) gains importance day by day. Due to legislation and new regulations, actors like users, producers, and treatment facilities are being conferred new responsibilities in the ELV management process. Besides, the ELV management is of vital importance for environment conservation, circular economy and sustainable development. All of these reasons are making the ELV management such a crucial issue to study. Today, the ELV management is a well-positioned and emergent research area. However, the available review papers are focused only on a small area of the ELV management, such as reverse logistics, recovery infrastructure, disassemblability, etc. Besides, a review of state-of-the-art mathematical models for the ELV management is still missing. This paper aims to provide an extensive content analysis overview of studies on the ELV management. A total of 232 studies published in the period 2000–2019 are collected, categorized, reviewed and analyzed. A critical review of the published literature is provided. Gaps in the literature are identified to clarify and suggest future research directions. This review can provide a source of references, valuable insights, and opportunities for researchers interested in the ELV management and inspire their additional attention.

Background: Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty. Method: For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment. Findings: A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.