Nowadays, robust and efficient solid waste collection is crucial to motivate citizens to participate in the circular economy by sorting recyclable solid waste. Vocational vehicles, including garbage trucks, contribute significantly to CO${}_2$ emissions; therefore, it is strongly recommended, and in the European Union it is mandatory, to replace conventional-fuel-based garbage trucks with electric ones. For providing sustainable and energy-efficient solid waste collection with a heterogeneous fleet, in-depth mathematical computations are needed to support solving complex decision-making problems, including crew rostering and vehicle routing, because the distance and capacity of electric garbage trucks differ from conventional-fuel-based ones. However, the literature on solid waste collection using electric garbage trucks is still relatively scarce. The main contribution of this paper is developing an optimization problem for balancing travel distance assigned to each garbage truck of a heterogeneous fleet. The problem is based on specific requirements of the Municipal Solid Waste Management in Cracow, Poland, where the working time of routes is balanced and the total time of collection service can be minimized. For the problem, an MIP program was developed to generate optimal crew schedules, so that the hitherto network of segregated solid waste pickup nodes can be served using a heterogeneous fleet in which the share of electric garbage trucks is up to 30%. We study the impact of the changed composition of the fleet on modifications in crew rostering due to the shorter range of an electric vehicle compared to a conventional-fuel-based one.

The development of a comprehensive mathematical model depicting an asynchronous motor in generalised (resultant) vectors, accounting for voltage asymmetry, stands as a significant stride in this research endavour. This model serves as a powerful tool for conducting an exhaustive analysis of...

Nowadays, in the European Union selective solid waste management be-longs to important responsibilities of municipalities. In Solid Waste Management (SWM) the main operational task is to set a schedule for solid waste collection and to find optimal routes for garbage trucks so that the total costs of solid waste collection service can be minimized subject to a series of constraints which guarantee not only fulfillment of SWM’s obligations but also desirable level of quality of that service. Optimization problem of garbage trucks routing is a special case of rich Vehicle Routing Problem as it has to cover following constraints: pickup nodes (clients) must be visited during their predefined time windows; the number and capacity of depots and specialized sorting units can-not be exceeded; each garbage truck can be assigned to at most one depot; each route should be dedicated to collecting one type of segregated solid waste, and the route must be served by a garbage truck which can collect that type of solid waste; availability of garbage trucks and their drivers must be respected; each garbage truck must be drained at a specialized sorting unit before going back to the depot. This paper contributes with a new Mixed-Integer Programming (MIP) model for the Selective Solid Waste Collection Routing Problem (SS-WCRP) with time windows, limited heterogeneous fleet, and different types of segregated solid waste to be collected separately. Utilization of MIP for solving small-sized instance of the Fleet Optimization Problem for Selective Solid Waste Collection (FOPSSWC) is and obtained results are reported.

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.

This study explores how contextual framings influence sustainable decision-making in everyday situations. Building on the literature about the intention–behaviour gap, we examine the combined effect of role activation and environmental risk on pro-environmental preferences. A scenario-based behavioural experiment, conducted via oTree, integrated within-subject role framing (citizen, consumer, neutral) with randomised environmental risk conditions. Participants completed repeated binary choice tasks, where Eco-Preference was defined as the frequency with which they chose the sustainable option. The results indicate that activating a citizen role significantly increased Eco-Preference compared to consumer or neutral framings, while high-risk contexts did not directly boost sustainable behaviour. Instead, risk cues had an indirect effect through motivational states, highlighting the mediating role of Eco-Preference. Theoretically, this study advances Eco-Preference as a latent behavioural construct linking identity-based theories of responsibility with decision-based models of sustainability. Practically, the findings underscore the potential of role-based communication strategies to enhance ecological responsibility, suggesting that both policy and organisational interventions can benefit from fostering civic identities. Ultimately, the framework is applicable across cultures by offering a behavioural measure less prone to survey bias, supporting future comparative research on environmental decision-making.

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning.