Strategic Decision-Making for Multi-Period Fleet Transition Towards Zero-Emission: Preliminary Study

Abstract

Municipal Solid Waste Management (MSWM) struggles with significant policy and operational challenges, particularly concerning collection routes for recyclables and fleet composition. Within the European Union, phasing out traditional fuel-based vocational vehicles, like garbage trucks, in favor of zero-emission alternatives, is mandatory to achieve sustainable development objectives. This paper presents a preliminary study on the problem of multi-period fleet transition from combustive fuels towards more eco-friendly fueling types. Initially developed for energy sector, the MARKAL framework was used here to support the technological transition of the fleet. The mixed-integer program was formulated for the Fleet Transition Problem (FTP), a simplified theoretical problem. The objective of the FTP and a mixed-integer linear program used to solve it is minimizing the overall cost of fleet modernization throughout a multi-phase planning horizon so that the sustainable transition of the fleet can be assured. Computational experiments run on randomly generated data instances affirmed the model’s effectiveness in strategizing fleet transition. This research outlines a multi-period model for transitioning to a zero-emission fleet and demonstrates the FTP’s potential for strategic decision-making. Notably, the study observes consistent reductions in permissible emissions across the planning horizon.

Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

“…The reliance on fossil fuel-powered vehicles contributes to air pollution and greenhouse gas emissions, exacerbating climate change and posing health risks to populations in urban areas. Additionally, congestion caused by numerous delivery vehicles further intensifies environmental degradation and reduces overall efficiency [7]. Addressing these challenges requires innovative solutions such as alternative fuel vehicles [8], route optimization algorithms [9], and collaborative delivery models [10] to minimize costs and ecological footprints while ensuring timely and sustainable last-mile delivery services [11].…”