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

An integrated approach for requirement selection and scheduling in software release planning

It is essential for product software companies to decide which requirements should be included in the next release and to make an appropriate time plan of the development project. Compared to the extensive research done on requirement selection, very little research has been performed on time scheduling. In this paper, we introduce two integer linear programming models that integrate time scheduling into software release planning. Given the resource and precedence constraints, our first model provides a schedule for developing the requirements such that the project duration is minimized. Our second model combines requirement selection and scheduling, so that it not only maximizes revenues but also simultaneously calculates an on-time-delivery project schedule. Since requirement dependencies are essential for scheduling the development process, we present a more detailed analysis of these dependencies. Furthermore, we present two mechanisms that facilitate dynamic adaptation for over-estimation or under-estimation of revenues or processing time, one of which includes the Scrum methodology. Finally, several simulations based on real-life data are performed. The results of these simulations indicate that requirement dependency can significantly influence the requirement selection and the corresponding project plan. Moreover, the model for combined requirement selection and scheduling outperforms the sequential selection and scheduling approach in terms of efficiency and on-time delivery. \u

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

How to Measure the Robustness of Shunting Plans

The general problem of scheduling activities subject to temporal and resource constraints as well as a deadline emerges naturally in numerous application domains such as project management, production planning, and public transport. The schedules often have to be implemented in an uncertain environment, where disturbances cause deviations in the duration, release date or deadline of activities. Since these disruptions are not known in the planning phase, we must have schedules that are robust, i.e., capable of absorbing the disturbances without large deteriorations of the solution quality. Due to the complexity of computing the robustness of a schedule directly, many surrogate robustness measures have been proposed in literature. In this paper, we propose new robustness measures, and compare these and several existing measures with the results of a simulation study to determine which measures can be applied in practice to obtain good approximations of the true robustness of a schedule with deadlines. The experiments are performed on schedules generated for real-world scheduling problems at the shunting yards of the Dutch Railways (NS)

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

Secondary analysis of data on comorbidity/multimorbidity: a call for papers

Despite the high proportion and growing number of people with comorbidity/multimorbidity, clinical trials often exclude this group, leading to a limited evidence base to guide policy and practice for these individuals [1–5]. This evidence gap can potentially be addressed by secondary analysis of studies that were not originally designed to specifically examine comorbidity/multimorbidity, but have collected information from participants on co-occurring conditions. For example, secondary data analysis from randomized controlled trials may shed light on whether there is a differential impact of interventions on people with comorbidity/multimorbidity. Furthermore, data regarding comorbidity/multimorbidity can often be obtained from registration networks or administrative data sets

Current state of research on the clinical benefits of herbal medicines for non-life-threatening ailments

Herbal medicines are becoming increasingly popular among patients because they are well tolerated and do not exert severe side effects. Nevertheless, they receive little consideration in therapeutic settings. The present article reviews the current state of research on the clinical benefits of herbal medicines on five indication groups, psychosomatic disorders, gynecological complaints, gastrointestinal disorders, urinary and upper respiratory tract infections. The study search was based on the database PubMed and concentrated on herbal medicines legally approved in Europe. After applying defined inclusion and exclusion criteria, 141 articles were selected: 59 for psychosomatic disorders (100% randomized controlled trials; RCTs), 20 for gynecological complaints (56% RCTs), 19 for gastrointestinal disorders (68% RCTs), 16 for urinary tract infections (UTI, 63% RCTs) and 24 for upper respiratory tract infections (URTI) (79% RCTs). For the majority of the studies, therapeutic benefits were evaluated by patient reported outcome measures (PROs). For psychosomatic disorders, gynecological complaints and URTI more than 80% of the study outcomes were positive, whereas the clinical benefit of herbal medicines for the treatment of UTI and gastrointestinal disorders was lower with 55%. The critical appraisal of the articles shows that there is a lack of high-quality studies and, with regard to gastrointestinal disorders, the clinical benefits of herbal medicines as a stand-alone form of therapy are unclear. According to the current state of knowledge, scientific evidence has still to be improved to allow integration of herbal medicines into guidelines and standard treatment regimens for the indications reviewed here. In addition to clinical data, real world data and outcome measures can add significant value to pave the way for herbal medicines into future therapeutic applications

Enhancing research quality and reporting: why the Journal of Comorbidity is now publishing study protocols

The Journal of Comorbidity was launched in 2011 and has since become established as a high-quality journal that publishes open-access, peer-reviewed articles, with a focus on advancing the clinical management of patients with comorbidity/multimorbidity. To further enhance research quality and reporting of studies in this field, the journal is now offering authors the opportunity to publish a summary of their study protocols – a move designed to generate interest and raise awareness in ongoing clinical research and to enable researchers to detail their methodologies in order that replication by scientific peers is possible

Pitfalls of Power Systems Modelling Metrics

In power system modelling the unit commitment problem is used to simulate the wholesale electricity market. A solution to the unit commitment problem is a least-cost schedule that contains information regarding the capacity factors of each generator, the total CO2 emissions, and unserved energy per hour. However, since there might be a large variety of (sub)-optimal solutions, these characteristics might be arbitrary and conclusions about them may be presumptuous.In this article, we illustrate this by running multiple experiments on a future European power system. Each scenario was run multiple times by adding additional terms to the objective function such as the minimization and maximization of generator capacity factors, carbon emissions, and loss of load hours. The results showed that schedules can be equivalent in terms of cost, but that relative capacity factors, emissions, and loss of load hours could differ by large factors.</p

Evolutionary air traffic flow management for large 3D-problems

We present an evolutionary tool to solve free-route Air Traffic Flow Management problems within a three-dimensional air space. This is the first evolutionary tool which solves free-route planning problems involving a few hundred aircraft. We observe that the importance of the recombination operator increases as we scale to larger problem instances. The evolutionary algorithm is based on a variant of the elitist recombinationalgorithm. We show a theoretical analysis of the problem, and present the results of experiments