In this paper, we introduce a Dantzig-Wolfe reformulation to compute aircraft arrival routes in a terminal maneuvering area (TMA) where the aircraft are flying according to theoptimal continuous-descent-operation (CDO) speed profile with idle thrust. This model assumes fixed entry times for all aircraft and a single separation time that is independent of wake-turbulence categories.Preliminary experiments show that this approach leads to significantly reduced runtimes.Moreover, the results indicate the possiblity of extending the reformulation to the full model and applying it to address additional practical considerations, such as wind effects in the future.

In this study, we consider rescheduling freight trains to reduce the effects of major interruptions. We assume that the interruption is an unexpected marshalling-yard closure, and we develop a macroscopic Mixed-Integer-Linear-Programming (MILP) model to reschedule the timetable. Furthermore, we design a rescheduling strategy of letting trains wait on the way when the destination yard has a closure. We consider stopping restrictions and the capacity of each segment and station in the model. The order of the trains affected by the interruption is not fixed. We present experimental results for three different cases of various sizes.

In this paper, we validate non-intrusive eye-tracking and head-movement indicators for predicting workload (WL) to support an air traffic controller (ATCO)self-evaluation. This will allow, e.g., to open or close sectors under more accurate consideration of the individual’s state, identifying over- and underload risk. We investigate n = 18 ATCOs during simulated working sessions with three varying traffic-load scenarios (light, moderate, heavy task load), adhering to a counterbalanced, within-subjects design. We apply non-intrusive eye tracking and the Cooper-Harper WL Rating Scale (CHS). Further, we employed a wearable electroencephalography (EEG)device optimized for monitoring WL. We evaluate the performance of five classical machine learning models across two distinct labeling tasks: CHS and EEG. For CHS labeling, the models achieve an accuracy of 84% (F1-score72%) when classifying WL levels into three categories (low, medium, high), and 93% (F1-score 83%) when categorizing WL into two classes (low/medium, high). Similarly, for EEG labeling, the models achieve an accuracy of 86% (F1-score 77%) in the three-level WL classification and 96%(F1-score 84%) in the binary WL classification. With this, we display the potential of machine learning techniques in predicting ATCO WL solely based on eye-tracking and head-movement measures.

We consider the problem of creating weekly shift schedules for train dispatchers, which conform to a variety of operational constraints, in particular, several work and rest time restrictions. We create the schedules in a two-stage process. First, using a previously presented IP model, we create a set of feasible daily shifts, which takes care of minimum-rest and shift-length requirements, taskload bounds, and combinability of dispatching areas. We then formulate an IP model to combine these daily shifts into weekly schedules, enforcing that each daily shift is covered by some dispatcher every day of the week, while ensuring that the weekly schedules comply with various restrictions on working hours from a union agreement. With this approach, we aim to identify "good" sets of daily shifts for the longer schedules. We run experiments for real-world sized input and consider different distributions of the daily shifts w.r.t. shift length and ratio of night shifts. Daily shifts with shift-length variability, relatively few long shifts, and a low ratio of night shifts generally yield better weekly schedules. The runtime for the second stage with the best daily-shift pattern is below three hours, which - together with the runtime for stage 1 of ca. 2 hours per run - can be feasible for real-world use.

This study is about rescheduling freight trains to reduce the eff ects of major interruptions. In this paper, we consider that the interruption is an unexpected marshallingyard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP)model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider arescheduling strategy of letting trains wait on the way when the destination yard havea closure. The model considers stopping restrictions and the capacity of each segmentand station. The order of the trains aff ected by the interruption is not fi xed. We presentexperimental results of three diff erent cases, which are all based on artifi cial data.

This study is about rescheduling freight trains to reduce the effects of major interruptions. In this paper, we consider that the interruption is an unexpected marshalling-yard closure. We develop a macroscopic Mixed-Integer Linear Programming (MILP) model to reschedule railway timetables. One important principle is that we simultaneously reschedule several trains, instead of one-by-one. Furthermore, we consider a rescheduling strategy of letting trains wait on the way when the destination yard have a closure. The model considers stopping restrictions and the capacity of each segment and station. The order of the trains affected by the interruption is not fixed. We present experimental results of three different cases, which are all based on artificial data.

We consider the watchman route problem for a k-transmitter watchman: standing at point p in a polygon P, the watchman can see if intersects P’s boundary at most k times—q is k-visible to p. Traveling along the k-transmitter watchman route, either all points in P or a discrete set of points must be k-visible to the watchman. We aim for minimizing the length of the k-transmitter watchman route. We show that even in simple polygons the shortest k-transmitter watchman route problem for a discrete set of points is NP-complete and cannot be approximated to within a logarithmic factor (unless P=NP), both with and without a given starting point. Moreover, we present a polylogarithmic approximation for the k-transmitter watchman route problem for a given starting point and with approximation ratio (with). 

Train dispatchers monitor and control train traffi c from a dispatching center, which isresponsible for a certain region in the railway network. This region is divided into subareas, where each train dispatcher controls one or several subareas at any time. Giventhe high safety concerns of their profession, dispatchers’ working shifts should fulfi lseveral legal and operational constraints, such as bounds on the length of shifts andon the resting periods between shifts. To construct shift schedules for train dispatchers is a complex and time-consuming process that is currently done manually. In thispaper, we present an optimization framework to automate this process, based on amodel for single-day shifts. Here, we focus on the objective of minimizing the numberof dispatchers as a baseline for future objectives. We present experimental results forreal-world sized data (number of geographical areas and train movements in the orderof magnitude as for one dispatching center in Sweden, covering the southern partof the country). We study the impact on the run time for diff erent input parameters,namely: the total number of geographical areas, the maximum number of geographical areas that can be assigned to a dispatcher in any period, changes in adjacencybetween the geographical areas, and the number of geographical areas that eachdispatcher is qualifi ed to monitor. The run time for the instances is between 19 and305 seconds

We study the art gallery problem for opposing halfguards: guards that can either see to their left or to theirright only. We present art gallery theorems, show thatthe problem is NP-hard in monotone polygons, presentapproximation algorithms for spiral and staircase poly-gons, and show that the location of half guards in 2-guardable polygons is not restricted to extensions.