An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.

Inner loop program construct: A faster way for program execution

Loops are repetitive control structures in programming languages. They are used extensively in many algorithms. The for-loop and while-loop exist, where the former is repeated a number of times while the latter is repeated until a condition is met. Some have asked if re-arranging loops in certain ways can change a program’s speed to produce machine-independent optimisation. Therefore, this research sought to find out if there is any speed difference in a single loop of computations and a loop with an inner loop of same computations. Greater focus is on inner for-loop. The research used a comparative study method in order to evaluate the primary data obtained from running several tests in four popular programming languages: C, C#, Python and R. The Python implementations were further tested on Ubuntu 16 for comparison with results from Windows 10. Results established that, across all languages, there were more computations performed per unit time with an inner for-loop than no inner loop, meaning, given the same number of computations to perform, a loop with an inner for-loop will finish faster. The inner while-loop didn’t perform so well, though. This study will help developers in making better choices with programming language and style.

A Co-Ordinated Single-Vendor Multi-Buyer Supply Chain Model

his chapter considers the co-ordination in a single-vendor multi-buyer supply chain by synchronising ordering and production cycles. The synchronisation is achieved by scheduling the actual ordering days of the buyers and co-ordinating it with the vendor’s production cycle whilst allowing the buyers to choose their own lot sizes and order cycle. A mathematical model for our proposed co-ordination is developed and analysed. Our results show that the synchronised cycles policy works better than independent optimisation or restricting buyers to adopt a common order cycle. Some illustrative examples demonstrate that there are circumstances where both the vendor and the buyers gain from such synchronisation without the need for price and quantity discount incentives.