Free and regular mixed-model sequences by a linear program-assisted hybrid algorithm GRASP-LP

A linear program-assisted hybrid algorithm (GRASP-LP) is presented to solve a mixed-model sequencing problem in an assembly line. The issue of the problem is to obtain manufacturing sequences of product models with the minimum work overload, allowing the free interruption of operations at workstations and preserving the production mix. The implemented GRASP-LP is compared with other procedures through a case study linked with the Nissan’ Engine Plant from Barcelona.Peer ReviewedPostprint (author's final draft

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Learning about Sequence-Dependent DNA/Single-Wall Carbon Nanotube Hybrids

Since the single-wall carbon nanotubes (SWCNTs) were discovered in 1993, they have attracted significant interest with their extraordinary electrical and optical properties in addition to their remarkable mechanical strength and thermal conductivity. Single-stranded DNA conjugated SWCNT have shown outstanding functionality in terms of dispersibility and biocompatibility. In addition, some special DNA sequences have presented an ability to recognize specific SWCNT species, called recognition sequences. Ion-exchange chromatography and aqueous two-phase (ATP) separation technique have been widely used for SWCNT separation. However, little is known about the use of ATP as an analytical technique. Furthermore, for bio-applications, DNA/SWCNT hybrids have attracted significant interest due to their high solvatochromic sensitivity to changes in the local environment, which enables their use as sensors. Recognition properties can provide good candidates for molecular detection on the assumption that the recognition DNA/SWCNT hybrids have structurally well-defined DNA wrappings. Thus, there is a growing need for discovery of new recognition sequences. In this thesis, we explore new methods to quantify difference in solvation/binding characteristics using ATP, and a new approach to predicting recognition sequences using Machine Learning techniques. Finally, a new concept for a DNA/SWCNT-based sensing system is demonstrated