Sustainable aviation fuel is scarce, costs several times as much as fossil jet fuel, and is increasingly mandated by regulation. We study how an airline should commit to forward purchase contracts before observing fuel prices, carbon prices, and supply availability, and how it should allocate physical SAF and voluntary book-and-claim certificates across its network afterwards. We formulate a two-stage stochastic mixed-integer program whose objective combines the expected net compliance cost with its Conditional Value-at-Risk and calibrate one hundred correlated price and availability scenarios entirely from public data. A case study of a Gulf-carrier network with thirteen long-haul routes serving EU, UK, and other international destinations over twenty-four quarters shows that SAF compliance adds about four and a half percent to the fuel-and-carbon bill, and that voluntary certificate flexibility reduces tail cost by about three percent while cutting required forward commitments by more than one third

Vehicle scrappage programs serve a dual purpose of promoting the automobile industry and reducing vehicular emissions. However, the success of scrappage programs depends on consumer participation. In this study, we examine the optimal policy for consumer participation in vehicle scrapping programs, emphasizing the cost incurred by consumers as a key determinant of voluntary engagement. We develop a dynamic optimization model to minimize the costs consumers incur during vehicle ownership. Our findings suggest that frequent drivers derive greater benefits from participation, and monetary incentives provided by policymakers encourage earlier vehicle scrappage. Additionally, we assess an optimal scrappage policy in which the replacement vehicle is an electric vehicle, in the presence of secondary markets. We found that vehicle scrapping programs are effective in secondary-market-dominant regions only if consumers are offered varying subsidies for scrapping their vehicles. Results indicate that replacing an aging vehicle with an electric vehicle offers a cost-efficient alternative by reducing overall lifecycle vehicle expenses. These insights provide a framework for policymakers to design effective subsidy structures, based on the region's secondary-market penetration, that enhance consumer participation. Specifically, implementing heterogeneous subsidies—structured as a varying function of vehicle age—can accelerate consumer engagement in vehicle scrapping programs, thereby maximizing policy effectiveness.

In this paper, we consider the problem of scheduling the trains in the event of planned maintenance blocks in a Railway network. We need to generate appropriate dispatching plans to reconsider the affected train movements in order to minimize the overall delay experienced by commuters. This paper proposes an alternative approach in which the train timetable is made to accommodate the preventive maintenance schedule in order to avoid the exercise of rescheduling the original timetable during the maintenance time windows. This will also help to improve the commuting experience since the passengers usually relate the quality of service directly to the timetable without being aware of the in-built provisions for maintenance work. This problem is a part of the Railway challenge published by the Railway Application Section (RAS) of INFORMS in 2016. We provide a link-level model to optimize the joint problem of train timetabling and maintenance scheduling in a given network. The output is a timetable that minimizes the travel time of all trains while respecting several operational constraints and maintenance needs. We develop a Binary Integer Linear Programming (BILP) formulation and validate our model with a toy instance.

This paper develops a framework for optimizing the placement of Electric Vehicle Charging Stations (EVCS) in Qatar using the Set Covering Problem (SCP). The model identifies the minimum number of charging stations required to ensure full accessibility across eight municipalities, while minimizing the total installation costs. The study considers two charging technologies, Medium DC Fast (MDC) and High-Power DC (HPDC). The model is implemented in IBM ILOG CPLEX Studio and evaluated under six coverage threshold scenarios for planning horizons 2025 and 2030. Results show that the base scenario with a 15-minute MDC and a 30-minute HPDC threshold provides the best balance between accessibility and cost, requiring 71 stations in 2025 and 114 in 2030. Sensitivity analyses confirm that coverage constraints are binding and that the network configuration is driven by the accessibility threshold. Policy implications are discussed in alignment with Qatar National Vision 2030.