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Some aspects of batch cooling crystallization in the industrial practice are analysed by computer simulations. The results indicate that without appropriate kinetics and very accurate process control, even the qualitative effects of applying controlled cooling and seeding are highly unpredictable. An increase in product size by applying controlled cooling is likely to be successful only rather randomly and the size distribution becomes broader. A linear or weakly non-linear cooling curve usually produces larger crystals than a natural cooling curve, and a better reproducibility than a controlled cooling curve. Seeding increases both size and reproducibility, but is also likely to increase significantly the coefficient of variation of the product distribution. The product weight mean size and coefficient of variation may increase or decrease at increasing amount of seeds, depending on governing kinetics. ~~~~ . ~~~Nous analysons dans cet article quelques-uns des aspects de la cristallisation par refroidissement discontinu dans la pratique industrielle B I'aide d'une simulation par ordinateur. Les resultats montrent que sans une cinCtique approprike et un contrBle de procede trks precis, meme les effets qualitatifs d'un refroidissement et d'un ensemencement contrcilis sont hautement non predictibles. Une augmentation de la taille du produit par un refroidissement contrBI& a peu de change de survenir et la distribution de taille s'accroit. Une courbe de refroidissement lineaire ou ICgkrement non lineaire donne des cristaux plus larges que dans le cas d'une courbe de refroidissement naturelle et donne une meilleure reproductibilite qu'une courbe de refroidissement contr61C. L'ensemencement augmente la taille et la reproductibilite mais augmente aussi probablement de faqon significative le coefficient de variation de la distribution du produit. Le poids moyen du produit et son coefficient de variation peuvent augmenter ou diminuer B des quantites de graines croissantes. en fonction de la cinitique gouvernant la reaction.Keywords: batch cooling crystallization, simulation of CSD, cooling profile and seeding influence. batch cooling crystallizer is supposed to produce a I20

T his paper considers mathematical optimization for the multistage train formation problem, which at the core is the allocation of classification yard formation tracks to outbound freight trains, subject to realistic constraints on train scheduling, arrival and departure timeliness, and track capacity. The problem formulation allows the temporary storage of freight cars on a dedicated mixed-usage track. This real-world practice increases the capacity of the yard, measured in the number of simultaneous trains that can be successfully handled. Two optimization models are proposed and evaluated for the multistage train formation problem. The first one is a column-based integer programming model, which is solved using branch and price. The second model is a simplified reformulation of the first model as an arc-indexed integer linear program, which has the same linear programming relaxation as the first model. Both models are adapted for rolling horizon planning and evaluated on a five-month historical data set from the largest freight yard in Scandinavia. From this data set, 784 instances of different types and lengths, spanning from two to five days, were created. In contrast to earlier approaches, all instances could be solved to optimality using the two models. In the experiments, the arc-indexed model proved optimality on average twice as fast as the column-based model for the independent instances, and three times faster for the rolling horizon instances. For the arc-indexed model, the average solution time for a reasonably sized planning horizon of three days was 16 seconds. Regardless of size, no instance took longer than eight minutes to be solved. The results indicate that optimization approaches are suitable alternatives for scheduling and track allocation at classification yards.

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Abstract. Software testing in industrial projects typically requires large test suites. Executing them is commonly expensive in terms of effort and wall-clock time. Indiscriminately executing all available test cases leads to sub-optimal exploitation of testing resources. Selecting too few test cases for execution on the other hand might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. This paper introduces a novel method prioritizing test cases to detect faults earlier. The method combines TOPSIS decision making with fuzzy principles. The method is based on multi-criteria like fault detection probability, execution time, or complexity. Applying the method in an industrial context for testing a train control management subsystem from Bombardier Transportation in Sweden shows its practical benefit.

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Maintenance planning is an important problem for railways, as well as other application domains that employ machinery with expensive replacements and high downtime costs. In a previous paper, we have developed methods for efficiently finding optimized maintenance schedules for a single unit, and proposed that the maintenance plan should be continuously re-optimized based on the condition of components. However, fleet-level resources, such as the availability of expensive spare parts, have largely been ignored. In this paper, we extend our previous approach by proposing a solution for the fleet level maintenance scheduling problem with spare parts optimization. The new solution is based on a mixed integer linear programming formulation of the problem. We demonstrate the merits of our approach by optimizing instances of maintenance schedules based on maintenance data from railway companies operating in Sweden.

Prioritization, selection and minimization of test cases are well-known problems in software testing. Test case prioritization deals with the problem of ordering an existing set of test cases, typically with respect to the estimated likelihood of detecting faults. Test case selection addresses the problem of selecting a subset of an existing set of test cases, typically by discarding test cases that do not add any value in improving the quality of the software under test. Most existing approaches for test case prioritization and selection suffer from one or several drawbacks. For example, they to a large extent utilize static analysis of code for that purpose, making them unfit for higher levels of testing such as integration testing. Moreover, they do not exploit the possibility of dynamically changing the prioritization or selection of test cases based on the execution results of prior test cases. Such dynamic analysis allows for discarding test cases that do not need to be executed and are thus redundant. This paper proposes a generic method for prioritization and selection of test cases in integration testing that addresses the above issues. We also present the results of an industrial case study where initial evidence suggests the potential usefulness of our approach in testing a safety-critical train control management subsystem.