Winds are changing : An explanation for the warming of the Netherlands

Western Europe is warming rapidly, much faster than the world average. To explain this phenomenon for the Netherlands, we look at the region where the airflow comes from instead of looking at the wind on the ground. Thereto, we consider 24 so-called weather patterns, which describe the origin of the airflow (north, northeast, etc.) and whether the airflow comes straight at us, or with bending of isobars (cyclonal or anticyclonal). For each day from January 1, 1836 onwards, we have determined the corresponding weather pattern on basis of the weather maps from Reanalysis archives at wettercentrale.de. Using a statistical test, we can see that a shift has occurred in the weather patterns, which has resulted in a significant increase in airflow coming from warmer directions. We further have applied linear regression to explain the daily average temperatures on basis of the weather patterns for the period 1961-2020. In this way, we find for the daily model an R-2 value of 0.60 and for the yearly model, based on the aggregated average daily values, we find an R-2 of 0.81, which is increased to 0.85 when we take the influence of the Atlantic Multidecadal Oscillation (AMO) and the Total Solar Irradiance (TSI) into account. These values strongly suggest that the warming in the Netherlands is caused by a shift in the origin of the airflow to warmer directions

Comparing Different Metrics Quantifying Pedestrian Safety

The quantification of pedestrian safety is an important research topic. If reliable quantification is possible, it can be used to predict and prevent dangerous situations, such as the crowd crush at the 2010 Love Parade. To quantify safety, we can use several metrics like density, velocity, flow and pressure. Unfortunately, there are several methods to evaluate these metrics, which may give different results. This can lead to different interpretations of similar situations. Researchers compare these metrics visually or search for trends in fundamental diagrams. This is inherently subjective. We propose an objective methodology to compare these methods, where we emphasize the different quantifications of peak “dangerousness”. Furthermore, we refine existing methods to include the obstacles in environments by replacing the Euclidean distance with the geodesic distance. In our experimental analysis, we observe large differences between different methods for the same scenarios. We conclude that switching to a different method of analysing crowd safety can lead to different conclusions, which asks for standardisation in this research field. Since we are concerned with human safety, we prefer to err on the side of caution. Therefore, we advocate the use of our refined Gaussian-based method, which consistently reports higher levels of danger

Getting rid of stochasticity: applicable sometimes

We consider the single-machine scheduling problem of minimizing the number of late jobs. This problem is well-studied and well-understood in case of deterministic processing times. We consider the problem with stochastic processing times, and we show that for a number of probability distributions the problem can be reformulated as a deterministic problem (and solved by the corresponding algorithm) when we use the concept of minimum success probabilities, which is, that we require that the probability that a job complete on time is `big enough\u27. We further show that we can extend our approach to the case of machines with stochastic output

Scheduling around a small common due date

A set of n jobs has to be scheduled on a single machine which can handle only one job at a time. Each job requires a given positive uninterrupted processing time and has a positive weight. The problem is to find a schedule that minimizes the sum of weighted deviations of the job completion times from a given common due date d, which is smaller than the sum of the processing times. We prove that this problem is NP-hard even if all job weights are equal. In addition, we present a pseudopolynomial algorithm that requires O(n2d) time and O(nd) space

Integrated Gate and Bus Assignment at Amsterdam Airport Schiphol

At an airport a series of assignment problems need to be solved before aircraft can arrive and depart and passengers can embark and disembark. A lot of different parties are involved with this, each of which having to plan their own schedule. Two of the assignment problems that the \u27Regie\u27 at Amsterdam Airport Schiphol (AAS) is responsible for, are the gate assignment problem (i.e. where to place which aircraft) and the bus assignment problem (i.e. which bus will transport which passengers to or from the aircraft). Currently these two problems are solved in a sequential fashion, the output of the gate assignment problem is used as input for the bus assignment problem. We look at integrating these two sequential problems into one larger problem that considers both problems at the same time. This creates the possibility of using information regarding the bus assignment problem while solving the gate assignment problem. We developed a column generation algorithm for this problem and have implemented a prototype. To make the algorithm efficient we used a special technique called stabilized column generation and also column deletion. Computational experiments with real-life data from AAS indicate that our algorithm is able to compute a planning for one day at Schiphol in a reasonable time

Stronger Lagrangian bounds by use of slack variables: applications to machine scheduling problems

Lagrangian relaxation is a powerful bounding technique that has been applied successfully to manyNP-hard combinatorial optimization problems. The basic idea is to see anNP-hard problem as an easy-to-solve problem complicated by a number of nasty side constraints. We show that reformulating nasty inequality constraints as equalities by using slack variables leads to stronger lower bounds. The trick is widely applicable, but we focus on a broad class of machine scheduling problems for which it is particularly useful. We provide promising computational results for three problems belonging to this class for which Lagrangian bounds have appeared in the literature: the single-machine problem of minimizing total weighted completion time subject to precedence constraints, the two-machine flow-shop problem of minimizing total completion time, and the single-machine problem of minimizing total weighted tardiness

A hybrid local search algorithm for the Continuous Energy-Constrained Scheduling Problem

We consider the Continuous Energy-Constrained Scheduling Problem (CECSP). A set of jobs has to be processed on a continuous, shared resource. A schedule for a job consists of a start time, completion time, and a resource consumption profile. We want to find a schedule such that: each job does not start before its release time, is completed before its deadline, satisfies its full resource requirement, and respects its lower and upper bounds on resource consumption during processing. Our objective is to minimize the total weighted completion time. We present a hybrid local search approach, using simulated annealing and linear programming, and compare it to a mixed-integer linear programming (MILP) formulation. We show that the hybrid local search approach matches the MILP formulation in solution quality for small instances, and is able to find a feasible solution for larger instances in reasonable time.Comment: 19 pages, 2 figures, submitted for review at Journal of Schedulin

Flower power:Finding optimal plant cutting strategies through a combination of optimization and data mining

We study a problem that plays an important role in the flower industry: we must determine how many mother plants are required to be able to produce a given demand of cuttings per week. This sounds like an easy problem, but working with living material (plants) introduces complications that are rarely encountered in optimization problems: there is no list with possible cutting patterns, describing the average number of cuttings taken from a mother plant per week. More importantly, there is no easy way to find out whether a cutting pattern is feasible, that is, whether the mother plants can keep up delivering the number of cuttings required by the cutting pattern each week: the only alternative to asking for an 'expert's opinion' is to apply a field-test, which takes a lot of time (and there are very many options to check).We have tackled this problem by a combination of data mining and linear programming. We apply data mining to infer constraints that a feasible cutting pattern should obey, and we use these constraints in a linear programming formulation to determine the minimum number of mother plants that are needed to supply the demand. Due to the linearity of the constraints obtained by data mining, this formulation can be reformulated such that it becomes trivially solvable. Next, we look at the problem of finding the optimal number of mother plants for the case that we can sell a given number of the remaining cuttings on the market for a given price; we show that this problem can be solved efficiently through linear programming

Personnel Scheduling on Railway Yards

In this paper we consider the integration of the personnel scheduling into planning railway yards. This involves an extension of the Train Unit Shunting Problem, in which a conflict-free schedule of all activities at the yard has to be constructed. As the yards often consist of several kilometers of railway track, the main challenge in finding efficient staff schedules arises from the potentially large walking distances between activities. We present two efficient heuristics for staff assignment. These methods are integrated into a local search framework to find feasible solutions to the Train Unit Shunting Problem with staff requirements. To the best of our knowledge, this is the first algorithm to solve the complete version of this problem. Additionally, we propose a dynamic programming method to assign staff members as passengers to train movements to reduce their walking time. Furthermore, we describe several ILP-based approaches to find a feasible solution of the staff assignment problem with maximum robustness, which solution we use to evaluate the quality of the solutions produced by the heuristics. On a set of 300 instances of the train unit shunting problem with staff scheduling on a real-world railway yard, the best-performing heuristic integrated into the local search approach solves 97% of the instances within three minutes on average