A genetic algorithm for the mixed flow shop problem

In this thesis we present a new interesting version of the mixed flow shop se-quencing problem, which at the same time is a version of the classic flow shop,a very common topic on operations research.We propose a genetic algorithm to solve it that we will compare at the endwith a simple initial genetic-based algorithm previously design. For that wefirst focus on the crossover operator as we consider it the most challenging parton a sequencing problem. We study and compare 5 different crossover operatorsand we choose the one that performs better. Finally we calibrate the populationsize, the weight of mutation and crossover operators on the algorithm and alsothe mutations operator itself.The goal of the thesis is to better understand the specific mixed flow shopproblem version presented and design a genetic algorithm that clearly improvesthe performance of the initial algorith

A genetic algorithm for the mixed flow shop problem

In this thesis we present a new interesting version of the mixed flow shop se-quencing problem, which at the same time is a version of the classic flow shop,a very common topic on operations research.We propose a genetic algorithm to solve it that we will compare at the endwith a simple initial genetic-based algorithm previously design. For that wefirst focus on the crossover operator as we consider it the most challenging parton a sequencing problem. We study and compare 5 different crossover operatorsand we choose the one that performs better. Finally we calibrate the populationsize, the weight of mutation and crossover operators on the algorithm and alsothe mutations operator itself.The goal of the thesis is to better understand the specific mixed flow shopproblem version presented and design a genetic algorithm that clearly improvesthe performance of the initial algorith

Switching TNF antagonists in patients with chronic arthritis: An observational study of 488 patients over a four-year period

The objective of this work is to analyze the survival of infliximab, etanercept and adalimumab in patients who have switched among tumor necrosis factor (TNF) antagonists for the treatment of chronic arthritis. BIOBADASER is a national registry of patients with different forms of chronic arthritis who are treated with biologics. Using this registry, we have analyzed patient switching of TNF antagonists. The cumulative discontinuation rate was calculated using the actuarial method. The log-rank test was used to compare survival curves, and Cox regression models were used to assess independent factors associated with discontinuing medication. Between February 2000 and September 2004, 4,706 patients were registered in BIOBADASER, of whom 68% had rheumatoid arthritis, 11% ankylosing spondylitis, 10% psoriatic arthritis, and 11% other forms of chronic arthritis. One- and two-year drug survival rates of the TNF antagonist were 0.83 and 0.75, respectively. There were 488 patients treated with more than one TNF antagonist. In this situation, survival of the second TNF antagonist decreased to 0.68 and 0.60 at 1 and 2 years, respectively. Survival was better in patients replacing the first TNF antagonist because of adverse events (hazard ratio (HR) for discontinuation 0.55 (95% confidence interval (CI), 0.34-0.84)), and worse in patients older than 60 years (HR 1.10 (95% CI 0.97-2.49)) or who were treated with infliximab (HR 3.22 (95% CI 2.13-4.87)). In summary, in patients who require continuous therapy and have failed to respond to a TNF antagonist, replacement with a different TNF antagonist may be of use under certain situations. This issue will deserve continuous reassessment with the arrival of new medications. © 2006 Gomez-Reino and Loreto Carmona; licensee BioMed Central Ltd