• Environmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimization

      Maiyar, Lohithaksha M.; Thakkar, Jitesh J.; Indian Institute of Technology Kharagpur; University of Sheffield (Elsevier Ltd, 2019-05-28)
      This paper develops a hub and spoke network based multi-objective green transportation model for evaluating optimal shipment quantity, modal choice, route selection, hub location, and vehicle velocity decisions considering wastages in Indian food grain context. A hybrid version of multi-objective meta-heuristic, Multi-Objective Particle Swarm Optimization with Differential Evolution (MOPSODE) is proposed to tackle the resulting non-linear formulation. Benchmarking with NSGA-II confirms the dominance of MOPSODE over NSGAII pertaining to near optimal pareto fronts obtained for the tested cases. Finally, the study derives the economic and environmental impact of varying hub location level, food grain wastage threshold and intermodal hub capacity.
    • Fuzzy modelling of fuel consumptions and emissions for optimal navigation of a BOEING-747 aircraft

      Obajemu, Olusayo; Mahfouf, Mahdi; Maiyar, Lohithaksha M.; He, Changjiang; Allerton, David J.A.; Chen, Jun; Weiszer, Michal; University of Sheffield; Queen Mary University of London (IEEE Computer Society, 2020-08-21)
      Air traffic at many airports around the world is expected to grow, more often than not and at near capacity. Investing in new infrastructure is an option albeit relatively long-term but making a better use of existing facilities is an even better short and mid-term solution. Although Aircraft ground movement represents only a fraction of overall operations, optimal airport taxiing in terms of fuel burn and CO emission can contribute significantly to running costs and environment impact. Hence, with the view of optimising ground movement at busy airports, this research paper investigates a new framework for utilising model predictions to optimise the planning of taxiing operations of a BOEING-747 Aircraft. Research studies relating to how fuel consumption and emissions models (such as Carbon monoxide oxides of Nitrogen) are carried out. Specifically, the use of fuzzy-logic based models via quantitative data for the successful prediction of fuel consumption and CO emissions is explored in the paper. The fuzzy models are accurate, transparent but most importantly are capable of dealing with uncertainties, normally present in the system, intrinsically. These models and analyses will be integrated into a future study involving the development of optimal taxiing and navigation algorithms and to which the development of accurate models of aircraft fuel consumptions and emissions is crucial.
    • Real-time four-dimensional trajectory generation based on gain-scheduling control and a high-fidelity aircraft model

      Obajemu, Olusayo; Mahfouf, Mahdi; Maiyar, Lohithaksha M.; Al-Hindi, Abrar; Weiszer, Michal; Chen, Jun; University of Sheffield; University of Bedfordshire; Queen Mary University of London (Elsevier Ltd, 2021-03-19)
      Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gain-scheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.