Neighborhood structures for scheduling problems with additional resource types

The job shop scheduling is a challenging problem that has interested to researchers in the fields of Artificial Intelligence and Metaheuristics over the last decades. In this project, we face the job shop scheduling problem with an additional resource type (operators). This is a variant of the problem, which has been proposed recently in the literature. We start from a genetic algorithm that has been proposed previously to solve this problem and improve it in two different ways. Firstly, we introduce a modification in the schedule generation scheme in order to control the time of inactivity of the machines. Secondly we define a number of neighbourhood structures that are then incorporated in a memetic algorithm. In order to evaluate the proposed strategies, we have conducted an experimental study across a benchmark derived from a set of hard instances of the classic job shop problem

Antimicrobial use and production system shape the fecal, environmental, and slurry resistomes of pig farms

P. 1-17Background: The global threat of antimicrobial resistance (AMR) is a One Health problem impacted by antimicrobial use (AMU) for human and livestock applications. Extensive Iberian swine production is based on a more sustainable and eco-friendly management system, providing an excellent opportunity to evaluate how sustained differences in AMU impact the resistome, not only in the animals but also on the farm environment. Here, we evaluate the resistome footprint of an extensive pig farming system, maintained for decades, as compared to that of industrialized intensive pig farming by analyzing 105 fecal, environmental and slurry metagenomes from 38 farms. Results: Our results evidence a significantly higher abundance of antimicrobial resistance genes (ARGs) on intensive farms and a link between AMU and AMR to certain antimicrobial classes. We observed differences in the resistome across sample types, with a higher richness and dispersion of ARGs within environmental samples than on those from feces or slurry. Indeed, a deeper analysis revealed that differences among the three sample types were defined by taxa-ARGs associations. Interestingly, mobilome analyses revealed that the observed AMR differences between intensive and extensive farms could be linked to differences in the abundance of mobile genetic elements (MGEs). Thus, while there were no differences in the abundance of chromosomal-associated ARGs between intensive and extensive herds, a significantly higher abundance of integrons in the environment and plasmids, regardless of the sample type, was detected on intensive farms. Conclusions: Overall, this study shows how AMU, production system, and sample type influence, mainly through MGEs, the profile and dispersion of ARGs in pig production.S

Clinically Relevant Correction of Recessive Dystrophic Epidermolysis Bullosa by Dual sgRNA CRISPR/Cas9-Mediated Gene Editing

Gene editing constitutes a novel approach for precisely correcting disease-causing gene mutations. Frameshift mutations inCOL7A1 causing recessive dystrophic epidermolysis bullosaare amenable to open reading frame restoration by non-homologous end joining repair-based approaches. Efficient targeteddeletion of faulty COL7A1 exons in polyclonal patient keratinocytes would enable the translation of this therapeutic strategy to the clinic. In this study, using a dual single-guide RNA(sgRNA)-guided Cas9 nuclease delivered as a ribonucleoprotein complex through electroporation, we have achieved veryefficient targeted deletion of COL7A1 exon 80 in recessivedystrophic epidermolysis bullosa (RDEB) patient keratinocytescarrying a highly prevalent frameshift mutation. This ex vivonon-viral approach rendered a large proportion of correctedcells producing a functional collagen VII variant. The effectivetargeting of the epidermal stem cell population enabled longterm regeneration of a properly adhesive skin upon graftingonto immunodeficient mice. A safety assessment by next-generation sequencing (NGS) analysis of potential off-target sitesdid not reveal any unintended nuclease activity. Our strategycould potentially be extended to a large number of COL7A1mutation-bearing exons within the long collagenous domainof this gene, opening the way to precision medicine for RDEB.The study was mainly supported by DEBRA International, funded by DEBRA Austria (grant termed as Larcher 1). Additional funds came from Spanish grants SAF2017-86810-R (to M.D.R.) and PI17/01747 (to F.L.) from the Ministry of Economy and Competitiveness and Instituto de Salud Carlos III, respectively, both co-funded with European Regional Development Funds (ERDF) ERA-NET E-RARE JTC 2017 (MutaEB) and CIBERER (grant termed as Murillas- TERAPIAS ER2017)

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches

Efficient Reasoning about Infeasible One Machine Sequencing

This paper addresses the tasks of explaining and correcting infeasible one machine sequencing problems with a limit on the makespan. Concretely, the paper studies the computation of high-level explanations and corrections, which are given in terms of irreducible subsets of the set of jobs. To achieve these goals, the paper shows that both tasks can be reduced to the general framework of computing a minimal set over a monotone predicate (MSMP). The reductions enable the use of any general-purpose algorithm for solving MSMP, and three well-known approaches are instantiated for the two tasks. Furthermore, the paper details efficient scheduling techniques aimed at enhancing the performance of the proposed algorithms. The experimental results confirm that the proposed approaches are efficient in practice, and that the scheduling optimizations enable critical performance gains

Schedule Generation Schemes and Genetic Algorithm for the Scheduling Problem with Skilled Operators and Arbitrary Precedence Relations

In real-life production environments it is often the case that the processing of a task on a given machine requires the assistance of a human operator specially skilled to process that task. In this paper, we tackle a scheduling problem involving operators that are skilled to manage only subsets of the whole set of tasks in a given shop floor. This problem was recently proposed motivated by a handicraft company. In order to solve it, we make some contributions. We first propose a general schedule builder and particularize it to generate several complete solution spaces. This schedule builder is then exploited by a genetic algorithm that incorporates a number of problem-specific components, including a coding schema as well as crossover and mutation genetic operators. An experimental study shows substantial improvements over existing methods in the literature and reveals useful insights of practical interest

Repairing Infeasibility in Scheduling via Genetic Algorithms

International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC (8th. 2019, Almería, Spain