We offer a simulation of low-carbon electricity supply for Australia, based on currently and economically operating technologies and proven resources, contributing new knowledge by: featuring a GIS-based spatial optimisation process for identifying suitable generator locations; including expanded transmission networks; covering the entire continent; and investigating the significance of biofuels availability and carbon price. We find that nationwide low-carbon electricity supply is possible at about 160 GW installed capacity, at indicative cost of around 20 ¢/kW h, involving wind, concentrating solar, and PV utilities, and less than 20 TW h of biofuelled generation. Dispatchable hydro and biofuel plants are required to plug gaps caused by occasional low-resource periods. Technology and cost breakthroughs for storage, geothermal and ocean technologies, as well as offshore wind deployment would substantially alter our assessment.
Freight railway crew scheduling consists of generating crew duties for operating trains on a schedule at minimal cost while meeting all work regulations and operational requirements. Typically, a freight railway operation uses thousands of trains and requires thousands of crew members to operate them. Because of the problem's large size, even moderate percentage savings in crew costs translate into large monetary savings. However, freight railway operations are complex, and a crew-scheduling problem is difficult to solve. We describe the development and implementation of crew-scheduling software at DB Schenker, the largest European railway freight carrier. The software is based on a column-generation solution technique. Computational results demonstrate that high-quality solutions can be obtained using reasonable run times, even for large problem instances. We implemented all of DB Schenker's major requirements to ensure that the software is operationally viable. Management also uses this software as a decision support tool for strategic planning.
Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing highquality solution schedules while significantly cutting runtimes compared to the nondecomposition solution algorithm. We are following a "greenfield" approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc.
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