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The increased performance densities in the top of elite sports, sometimes mutual contest results are within the error margins of the measuring systems, has caused major problems in comparing performances and deciding on winners. In case of Dutch speed skating, the pool of highly competitive athletes is large, and, since there is a limit on the number of Olympic participators per country, the Olympic selection procedure is obviously a precarious affair. Because more than 100 years of data is available, we are able to study in this respect the well-known Gould hypothesis: When the sport matures, the variation in performance, especially at the top, decreases, and extreme events, where one athlete outperforms all his competitors, become rarer. Since Gould’s hypothesis only holds with ‘unchanged rules of the game’, several data corrections, for example on the introduction of the klap skate, are needed. A major goal of this paper is to analyze the possible role of Gould’s hypothesis concerning the growing performance densities for top speed skating.

This research investigated whether it is possible to explain the performance of talented youth speed skaters between the age of 15 to 20 years. Multidimensional performance characteristics were compared with the speed skating performance on separate distances (500 m, 1.000 m, 1.500 m and 3.000 m) and the allround performance, in the season of 2009-2010. Eight athletes from the Dutch national youth speed skating team (18-19 year old), along with 20 talented athletes from the Dutch district teams (15- 17 year old) were included. Multidimensional performance characteristics consisted of anthropometrical, physiological, technical and psychological characteristics. Partial correlations were calculated between each performance characteristic and the speed skating performance, while controlling for gender and age. For the 500 m, jump height, measured as a squat jump (r=-.664) and countermovement jump (r=-.619), and the technique of the speed skaters (r=-.500) was found to be an explanation of the performance. For the 1.000 m this was only jump height, measured as a squat jump (r=-.555) and countermovement jump (r=-.491). The speed skating performance on the longer distances (1.500 m and 3.000 m) were found to be significantly related with evaluation (r=-.420 and r=-.481) and anaerobic power (r=.409 with the 3.000 m). The allround speed skating performance was significantly related with jump height, measured as a squat jump (r=-.406) and related with the technique (r=-.541). It can be concluded that explaining the performance of talented youth speed skaters is difficult. Nevertheless, this study reveals that jump height and technique seem to be important prerequisites for almost all distances.

In 1998, the International Skating Union and the International Olympic Committee decided to skate the 500-m twice during World Single Distances Championships, Olympic Games, and World Cups. The decision was based on a study by the Norwegian statistician N. L. Hjort, who showed that in the period 1984-1994, there was a significant difference between 500-m times skated with a start in the inner and outer lanes. Since the introduction of the clap skate in the season 1997-1998, however, there has been a general feeling that this difference is no longer significant. In this article we show that this is, in fact, the case.

The selection of entries to be included/excluded in Branch and Bound algorithms is usually done on the basis of cost values. We consider the class of Depth First Search algorithms, and we propose to use upper tolerances to guide the search for optimal solutions. In spite of the fact that it needs time to calculate tolerances, our computational experiments for Asymmetric Traveling Salesman Problems show that in most situations tolerance-based algorithms outperform cost-based algorithms. The solution time reductions are mainly caused by the fact that the branching process becomes much more effective, so that optimal solutions are found in an earlier stage of the branching process. The use of tolerances also reveals why the widely used choice for branching on a smallest cycle in assignment solutions is on average the most effective one. Moreover, it turns out that tolerance-based DFS algorithms are better in solving difficult instances than the Best First Search algorithm from Carpaneto et al. [Carpaneto, G., Dell'Amico, M., Toth, P., 1995. Exact solution of large-scale asymmetric traveling salesman problems. ACM Transactions on Mathematical Software 21 (4), 394-409]. (C) 2006 Elsevier B.V. All rights reserved.

An automated production system is considered in which several robots are used for transporting parts between workstations following a given route in a carousel mode. The problem is to maximize the throughput rate. Extending previous works treating scheduling problems for a single robot, we consider a more realistic case in which workstations are served by multiple robots. A graph model of the production process is developed, making it possible to apply PERT-CPM solution techniques. The problem is proved to be solvable in polynomial time. (c) 2005 Elsevier B.V. All rights reserved.

The simple plant location problem is a well-studied problem in combinatorial optimization. It is one of deciding where to locate a set of plants so that a set of clients can be supplied by them at the minimum cost. This problem often appears as a subproblem in other combinatorial problems. Several branch and bound techniques have been developed to solve these problems. In this paper we present two techniques that enhance the performance of branch and bound algorithms. The new algorithms thus obtained are called branch and peg algorithms, where pegging refers to fixing values of variables at each subproblem in the branch and bound tree, and is distinct from variable fixing during the branching process. We present exhaustive computational experiments which show that the new algorithms generate less than 60% of the number of subproblems generated by branch and bound algorithms, and in certain cases require less than 10% of the execution times required by branch and bound algorithms.

The Data Correcting Algorithm is a branch and bound type algorithm in which the data of a given problem instance is `corrected' at each branching in such a way that the new instance will be as close as possible to a polynomially solvable instance and the result satisfies an acceptable accuracy (the difference between optimal and current solution). In this paper the data correcting algorithm is applied to determining exact and approximate optimal solutions to the simple plant location problem. Implementations of the algorithm are based on a pseudo-Boolean representation of the goal function of this problem, and a new reduction rule. We study the efficiency of the data correcting approach using two different bounds, the Khachaturov-Minoux bound and the Erlenkotter bound. We present computational results on several benchmark instances, which confirm the efficiency of the data-correcting approach.

Sensitivity analysis of e-optimal solutions is the problem of calculating the range within which a problem parameter may lie so that the given solution re-mains e-optimal. In this paper we study the sensitivity analysis problem for e-optimal solutions tocombinatorial optimization problems with min-max ob-jectives where e > 0. We show that the problem is easy if the original problem is easy. We also show that the converse is true under the assumption that it is possible to calculate an e-optimal solution to the problem in polynomial time.

Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic that makes effective use of memory structures in a way that is different from that in common implementations of tabu search. We report computational experiments with this heuristic on the traveling salesperson problem and the subset sum problem.

The cardboard company Kappa Graphic Board produces several kinds of cardboard with various weights and thicknesses. If the thickness is above a certain value (namely 1.7 mm), the cardboard is composed of three layers which are glued together, and is called heavy weight cardboard. The three layers are produced on different machines; the production speed depends on the thickness of the layers. In this paper we restrict our analysis to heavy weight cardboard. The objective is to find a composition of the layers such that the throughput time is minimized. The problem is solved by means of an LP-model in which the objective function is a linearized working hours function; also a number of constraints are linearized. The results of the calculations indicate that it is possible to decrease the total number of working hours in the production process without a large increase of production costs.

Johann Bernoulli is known mainly from his mathematical achievements concerning the differential and integral calculus, in which the concept of infinitely small plays a crucial part. This paper describes Bernoulli's struggle with this concept, especially the discrepancies that occur between his mathematical and philosophical interpretations. We concentrate on Bernoulli's Groningen period (1695-1705), in which the discussion with Leibniz about this question leads to a controversy between the two scholars.

This paper deals with a vehicle routing problem with split demands, namely the problem of determining a flight schedule for helicopters to off-shore platform locations for exchanging crew people employed on these platforms. The problem is formulated as an LP model and solved by means of a column-generation technique including solving TSP problems. Since the final solution needs to be integral, we have chosen a rounding procedure to obtain an integer solution. Since the LP approach needs a considerable amount of computer time, it is only suitable for long-term planning practices. For the usual short-term planning, we have designed the so-called Cluster-and-Route Heuristic together with a number of improvement heuristics. The Cluster-and-Route procedure constructs a suitable clustering of the platforms and simultaneously forms the routes of the helicopter flights associated with the clusters. This approach is different from the usual heuristics, in which the clusters are constructed first, and the routes for each cluster are made afterwards. Simulations with various data sets show that the new heuristic outperforms the usual heuristics for vehicle routing problems. Even better results are obtained when improvement heuristics are applied. We use four improvement heuristics, including, so-called 1-opt and 2-opt procedures.

We show how to use ATP and WTA rankings to estimate the probability that a player with a certain ranking advances to a specific round (for instance, the quarterfinals) in an upcoming tournament. We use the results from Grand Slam and Olympic tournaments in the period 2004 ? 2014. Pooling the data, which is justified according to our tests, allows us to compute probabilities with relatively small confidence intervals. For instance, the probability of a top 4 tennis player to reach the quarterfinals is 0.722 with a 95% confidence interval of (0.669; 0.771). This study was motivated by a request from the Dutch Olympic Committee (NOC*NSF). Based on our results, NOC*NSF decides which Dutch single tennis players to invite to participate at the 2016 Olympic Games of Rio de Janeiro

The Dutch gas company ''Gasunie'' has as one of its main policies, always meeting fluctuating demands, even in case of sudden changes of temperature. Gas originates roughly from three locations which have different gas qualities. A fast and flexible algorithm is needed to determine the quantities of sasses to be mixed and transported. In this paper the advantages of piecewise linear and network programming are combined to a simplex-like algorithm, that speeds up the recently used algorithms with at least a factor 50.