Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Universal Sequencing on a Single Machine

We consider scheduling on an unreliable machine that may experience unexpected changes in processing speed or even full breakdowns. We aim for a universal solution that performs well without adaptation for any possible machine behavior. For the objective of minimizing the total weighted completion time, we design a polynomial time deterministic algorithm that finds a universal scheduling sequence with a solution value within 4 times the value of an optimal clairvoyant algorithm that knows the disruptions in advance. A randomized version of this algorithm attains in expectation a ratio of e. We also show that both results are best possible among all universal solutions. As a direct consequence of our results, we answer affirmatively the question of whether a constant approximation algorithm exists for the offline version of the problem when machine unavailability periods are known in advance. When jobs have individual release dates, the situation changes drastically. Even if all weights are equal, there are instances for which any universal solution is a factor of Ω(log n/ log log n) worse than an optimal sequence. Motivated by this hardness, we study the special case when the processing time of each job is proportional to its weight. We present a non-trivial algorithm with a small constant performance guarantee. © 2010 Springer-Verlag

Manufacturing flow line systems: a review of models and analytical results

The most important models and results of the manufacturing flow line literature are described. These include the major classes of models (asynchronous, synchronous, and continuous); the major features (blocking, processing times, failures and repairs); the major properties (conservation of flow, flow rate-idle time, reversibility, and others); and the relationships among different models. Exact and approximate methods for obtaining quantitative measures of performance are also reviewed. The exact methods are appropriate for small systems. The approximate methods, which are the only means available for large systems, are generally based on decomposition, and make use of the exact methods for small systems. Extensions are briefly discussed. Directions for future research are suggested.National Science Foundation (U.S.) (Grant DDM-8914277

Open Trial of a Personalized Modular Treatment for Mood and Anxiety

Psychosocial treatments for mood and anxiety disorders are generally effective, however, a number of treated individuals fail to demonstrate clinically-significant change. Consistent the decades-old aim to identify ‘what works for whom,’ personalized and precision treatments have become a recent area of interest in medicine and psychology. The present study followed the recommendations of Fisher (2015) to employ a personalized modular model of cognitive-behavioral therapy. Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analyses and dynamic factor models. The results of these analyses were then used to generated personalized modular treatment plans on a person-by-person basis. Thirty-two participants completed therapy. The average number of sessions was 10.38. Hedges g’s for the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) were 2.33 and 1.62, respectively. The change per unit time was g=.24/session for the HRSD and g=.17/session for the HARS. The current open trial provides promising data in support of personalization, modularization, and idiographic research paradigms