11,445 research outputs found
Enriching the tactical network design of express service carriers with fleet scheduling characteristics
Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers' legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions
Scheduling aircraft landings - the static case
This is the publisher version of the article, obtained from the link below.In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all successive planes are respected. We present a mixed-integer zero–one formulation of the problem for the single runway case and extend it to the multiple runway case. We strengthen the linear programming relaxations of these formulations by introducing additional constraints. Throughout, we discuss how our formulations can be used to model a number of issues (choice of objective function, precedence restrictions, restricting the number of landings in a given time period, runway workload balancing) commonly encountered in practice. The problem is solved optimally using linear programming-based tree search. We also present an effective heuristic algorithm for the problem. Computational results for both the heuristic and the optimal algorithm are presented for a number of test problems involving up to 50 planes and four runways.J.E.Beasley. would like to acknowledge the financial support of the Commonwealth Scientific and Industrial Research Organization, Australia
Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach
Mobile energy storage systems (MESSs) provide mobility and flexibility to
enhance distribution system resilience. The paper proposes a Markov decision
process (MDP) formulation for an integrated service restoration strategy that
coordinates the scheduling of MESSs and resource dispatching of microgrids. The
uncertainties in load consumption are taken into account. The deep
reinforcement learning (DRL) algorithm is utilized to solve the MDP for optimal
scheduling. Specifically, the twin delayed deep deterministic policy gradient
(TD3) is applied to train the deep Q-network and policy network, then the well
trained policy can be deployed in on-line manner to perform multiple actions
simultaneously. The proposed model is demonstrated on an integrated test system
with three microgrids connected by Sioux Falls transportation network. The
simulation results indicate that mobile and stationary energy resources can be
well coordinated to improve system resilience.Comment: Submitted to 2020 IEEE Power and Energy Society General Meetin
Optimized shunting with mixed-usage tracks
We consider the planning of railway freight classification at hump yards, where the problem
involves the formation of departing freight train blocks from arriving trains subject to
scheduling and capacity constraints. The hump yard layout considered consists of arrival
tracks of sufficient length at an arrival yard, a hump, classification tracks of non-uniform
and possibly non-sufficient length at a classification yard, and departure tracks of sufficient
length. To increase yard capacity, freight cars arriving early can be stored temporarily
on specific mixed-usage tracks. The entire hump yard planning process is covered in this
paper, and heuristics for arrival and departure track assignment, as well as hump scheduling,
have been included to provide the neccessary input data. However, the central problem
considered is the classification track allocation problem. This problem has previously
been modeled using direct mixed integer programming models, but this approach did not
yield lower bounds of sufficient quality to prove optimality. Later attempts focused on
a column generation approach based on branch-and-price that could solve problem instances
of industrial size. Building upon the column generation approach we introduce
a direct arc-based integer programming model, where the arcs are precedence relations
between blocks on the same classification track. Further, the most promising models
are adapted for rolling-horizon planning. We evaluate the methods on historical data
from the Hallsberg shunting yard in Sweden. The results show that the new arc-based
model performs as well as the column generation approach. It returns an optimal schedule
within the execution time limit for all instances but from one, and executes as fast
as the column generation approach. Further, the short execution times of the column
generation approach and the arc-indexed model make them suitable for rolling-horizon
planning, while the direct mixed integer program proved to be too slow for this.
Extended analysis of the results shows that mixing was only required if the maximum
number of concurrent trains on the classification yard exceeds 29 (there are 32 available
tracks), and that after this point the number of extra car roll-ins increases heavily
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