Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.

Formalin-Fixed and Paraffin-Embedded Samples for Next Generation Sequencing: Problems and Solutions

Over the years, increasing information has been asked of the pathologist: we have moved from a purely morphological diagnosis to biomolecular and genetic studies, which have made it possible to implement the use of molecular targeted therapies, such as anti-epidermal growth factor receptor (EGFR) molecules in EGFR-mutated lung cancer, for example. Today, next generation sequencing (NGS) has changed the approach to neoplasms, to the extent that, in a short time, it has gained a place of absolute importance and diagnostic, prognostic and therapeutic utility. In this scenario, formaldehyde-fixed and paraffin-embedded (FFPE) biological tissue samples are a source of clinical and molecular information. However, problems can arise in the genetic material (DNA and RNA) for use in NGS due to fixation, and work is being devoted to possible strategies to reduce its effects. In this paper, we discuss the applications of FFPE tissue samples in the execution of NGS, we focus on the problems arising with the use of this type of material for nucleic acid extraction and, finally, we consider the most useful strategies to prevent and reduce single nucleotide polymorphisms (SNV) and other fixation artifacts.

Interval sequencing problem

This paper deals with sequencing problems for ‘n’ jobs on single machine, ‘n’ jobs on two machines, ‘n’ jobs on three machines and ‘n’ jobs on ‘m’ machines. Here, we consider the sequencing problem where the processing time, due dates, weights are taken as intervals. An algorithm is provided for obtaining an optimal sequence and also for determining the minimum duration taken to complete all the jobs. To illustrate this, numerical examples are provided.

A Mathematical Model for an Integrated Assembly Line Regarding Learning and Fatigue Effects

SUMMARY In this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.