The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions.

The environmental decision problems often are divisive, even in a technical realm, decision makers with strong personalities influence outcomes. The purpose of this study is to define and quantify the factors that affect the conservation objectives of a national natural park located in Colombia, South America adding the judgments of six decision makers with different knowledge (every decision maker is also a stakeholder representative). This paper uses a hybrid multiple criteria group decision-making model (MCDM), combining the social network analysis (SNA), analytic hierarchy process (AHP) and similarity measures to solve the consensus and anchoring problem among environmental decision makers. The SNA technique is used to build an influential network relation map among decision makers and to obtain their weights for applying a weighted AHP. Then, the final decision matrices for every decision maker are compared between them in order to identify the consensus level of the problem.

The purpose of this paper is to present a quantitative approach to the selection of the mining extraction method by developing a methodological problem of discrete multicriteria decision making (MCDM), this approach seeks to support the process of planning and mine design. Select the extractive method is one of the problems of Discrete Multicriteria Decision (MCDM) where decision-makers had problems in assigning weight to each criterion. To solve this problem, this article suggests the ENTROPY method. This paper wants to handle the subjectivity inherent to this problem by using the VIKOR method, which yields results in a compromise alternative. The methodology proposed in this article applies in coal deposit located in the western side of Cerro Tasajero in Norte de Santander, Colombia

Equity-aware electric vehicle (EV) charging planning remains difficult in data-constrained cities. In this work, an integrated framework was developed by combining spatiotemporal graph neural networks (ST-GNNs), EVI-Pro Lite demand estimation, and lexicographic bi-level optimization, and was applied to Bogotá, Colombia (8.3 million inhabitants). Household travel survey data (12,500 households across 142 zones) were used to estimate zone-level priority scores and venue-specific temporal weights. EVI-Pro Lite simulations projected a 2025 requirement of 10,870 charging ports (7352 residential, 2739 workplace, and 779 public). In the allocation stage, Level 1 preserved priority-proportional targets, while Level 2 minimized inter-zonal inequality in Hansen accessibility subject to near-optimal Level-1 compliance. The final allocation retained strong priority alignment in installed ports (Spearman $\rho =0.799$, $p<{10}^{-31}$), while the priority–accessibility association was lower (Spearman $\rho =0.320$, $p=1.04\times {10}^{-4}$), consistent with second-stage equity redistribution. Equity outcomes also improved (Hansen Gini = 0.433; bottom-50% Lorenz share = 0.204). The mean Hansen accessibility reached 296.630 (standard deviation 248.099; minimum 1.126). These findings indicate that reproducible, equity-oriented EV infrastructure plans can be produced in cities where revealed charging microdata are limited.