Limiting global warming to 1.5°C requires immediate action. Although allowable global greenhouse gas emissions are well-defined, translating these targets into actionable strategies at the company-level remains challenging. This study addresses critical gaps in understanding transition cost...

The accelerating penetration of electric vehicles (EVs) is reshaping modern distribution networks by introducing substantial, time-varying charging demand and new operational uncertainties. At the same time, Vehicle-to-Grid (V2G) capable EVs offer unprecedented flexibility to support grid...

In the dual risk model, while the ultimate ruin probability has an exact and straightforward formula, the mathematics becomes significantly more complex when considering a finite time horizon, and the literature on this topic is scarce. As a result, there is a need for numerical approximations....

In ergodic singular stochastic control problems, a decision-maker can instantaneously adjust the evolution of a state variable using a control of bounded variation, with the goal of minimizing a long-term average cost functional. The cost of control is proportional to the magnitude of...

The integration of solar photovoltaic systems into power grids has become crucial for sustainable energy development, particularly in extreme climate conditions where accurate power forecasting remains challenging. This study presents a novel multi-architecture machine learning framework with...

Background: The transition to renewable energy is intensifying demand for lithium-ion batteries (LIBs), thereby increasing the need for sustainable lithium sourcing. Traditional mining practices pose environmental and health risks, which can be mitigated through efficient end-of-life recycling...

Background: Sustainability is a critical concern in transportation, notably in light of governmental initiatives such as cap-and-trade systems and eco-label regulations aimed at reducing emissions. In this context, collaborative approaches among carriers, which involve the exchange of shipment...

Optimizing solid waste management (SWM) is essential for ensuring a sustainable and healthy environment in a city. This study considers a two-echelon solid waste logistics system (2E-SWLS) in a metropolitan city with a fleet of capacitated heterogeneous vehicles. The problem consists of waste...

Production scheduling is critical in manufacturing operations, requiring the optimal assignment of limited resources. This paper introduces a novel generalization of the Unrelated Parallel Machine (UPM) problem, addressing key real-world complexities: sequence- and machine-dependent setup times,...

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage...

Multi-objective optimization problems (MOPs) are common in real-world applications, including scheduling, vehicle routing, and engineering design. A key challenge in solving MOPs is balancing convergence and diversity, as these problems often involve conflicting objectives and complex...

Sustainable Supplier Selection and Order Allocation (SSSOA) are critical strategic decisions in supply chain management. The decision-making process becomes complex under uncertainty, especially in a multi-supplier, multi-item, and multi-period environment. This study proposes a four-stage...

This study investigates spatial crime patterns in the municipality of Matelândia, Brazil, with the aim of optimizing the placement of police facilities through an enhanced clustering approach. We use an unsupervised machine learning technique, and we introduce a refined k-Means methodology...