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With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing. [ABSTRACT FROM AUTHOR]

After surgery most of the surgical patients have to be admitted in a ward in the hospital. Due to financial reasons and a decreasing number of available nurses in the Netherlands over the years, it is important to reduce the bed usage as much as possible. One possible way to achieve this is to create an operating room (OR) schedule that spreads the usage of beds nicely over time, and thereby minimizes the number of required beds. An OR-schedule is given by an assignment of OR-blocks to specific days in the planning horizon and has to fulfill several resource constraints. Due to the stochastic nature of the length of stay of patients, the analytic calculation of the number of required beds for a given OR-schedule is a complex task involving the convolution of discrete distributions. In this paper, two approaches to deal with this complexity are presented. First, a heuristic approach based on local search is given that takes into account the detailed formulation of the objective. A second approach reduces the complexity by simplifying the objective function. This allows modeling and solving the resulting problem as an ILP. Both approaches are tested on data provided by Hagaziekenhuis in the Netherlands. Furthermore, several what-if scenarios are evaluated. The computational results show that the approach that uses the simplified objective function provides better solutions to the original problem for instances based on the situation in HagaZiekenhuis. By using this approach, the number of required beds for the considered instance of HagaZiekenhuis can be reduced by almost 20 %. [ABSTRACT FROM AUTHOR]

In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problem are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming, a Hybrid algorithm based on Integer Nonlinear Programming, the Parallel Prioritized Genetic Solver, and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA is statistically significantly better in terms of quality of solutions in most of the instances and for most levels of weighted coverage, although it requires more execution time. [ABSTRACT FROM AUTHOR]

We consider a hybrid flowshop scheduling problem that includes parallel unrelated discrete machines or batch processing machines in different stages of a production system. The problem is motivated by a bottleneck process within the production system of a transformer producer located in the Netherlands. We develop an integer programming model that minimises the total tardiness of jobs over a finite planning horizon. Our model is applicable to a wide range of production systems organised as hybrid flowshops. We strengthen our integer program by exploiting the special properties of some constraints in our formulation. We develop a decision support system (DSS) based on our proposed optimisation model. We compare the results of our initial optimisation model with an improved formulation as well as with a heuristic that was in use at the company before the implementation of our DSS. Our results show that the improved optimisation model significantly outperforms the heuristic and the initial optimisation model in terms of both the solution time and the strength of its linear programming relaxation. [ABSTRACT FROM AUTHOR]

We consider a fundamental integer programming (IP) model for cost–benefit analysis and flood protection through dike building in the Netherlands, due to Zwaneveld and Verweij (2017). Experimental analysis with data for the IJsselmeer shows that the solution of the linear programming relaxation of the IP model is integral. This naturally leads to question whether the polytope associated to the IP is always integral. In this paper we first give a negative answer to this question by proving the non-integrality of the polytope. Secondly, we establish natural conditions that guarantee the linear programming relaxation of the IP model is integral. We show that these conditions are indeed satisfied by the recent data on flood probabilities, damage and investment costs of IJsselmeer. Finally, we show that the IP model can be solved in polynomial time when the number of dike segments, or the number of feasible barrier heights, are bounded. [ABSTRACT FROM AUTHOR]

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multi-disciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample average approximation approach. Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital. [ABSTRACT FROM AUTHOR]