Stochastic mixed model sequencing with multiple stations using reinforcement learning and probability quantiles

In this study, we propose a reinforcement learning (RL) approach for minimizing the number of work overload situations in the mixed model sequencing (MMS) problem with stochastic processing times. The learning environment simulates stochastic processing times and penalizes work overloads with negative rewards. To account for the stochastic component of the problem, we implement a state representation that specifies whether work overloads will occur if the processing times are equal to their respective 25%, 50%, and 75% probability quantiles. Thereby, the RL agent is guided toward minimizing the number of overload situations while being provided with statistical information about how fluctuations in processing times affect the solution quality. To the best of our knowledge, this study is the first to consider the stochastic problem variation with a minimization of overload situations.

Impact of the Learning-Forgetting Effect on Mixed-Model Production Line Sequencing

In this study, the learning-forgetting (L-F) effect is considered in a mixed-model sequencing problem to investigate its impact on makespan minimization. To this end, mathematical models of the learning and forgetting effects are modified in accordance with a mixed-model production environment, and the L-F functions for a serial workstation and multiple products are established. Subsequently, their impact on production is demonstrated via data experiments. The relationships between the learning effect, forgetting effect, product model combination, and makespan are also discussed based on the experimental results. The results show that the learning and forgetting functions can significantly affect the work time in the mathematical scheduling model and that a balanced product model combination and small MPS (minimum part set) batch can help to reduce the L-F effect.

Modelling Customer Delight in Hotel Industry

It is not an exaggeration that the present-day competitive environment calls for continuous and consistent delivery of customer delight in any sector of the business. The studies conducted by separate researchers across different settings suggest the existence of a common model sequencing the events that lead to customer delight. This study attempts to empirically establish the model after conceptualizing it. Discriminant analysis is used as a basic tool to establish the relationship between different events using the data generated from a sample of repeat hotels guests. The results show that surpassing of expectations of the hotel guests affect their perception on pleasant surprises, which in turn affect their consequent happiness. Such happiness along with their perceived excitement and perceived positive feelings create a delighting experience for them. It is hoped that the findings shall encourage other researchers to test this model in different business environments.

Molecular Analysis of Ephrin A4 and Ephrin B1 in a Rabbit Model of Craniosynostosis

Craniosynostosis (CS) has a prevalence of approximately 1 in every 2000 live births and is characterized by the premature fusion of one or more cranial sutures. Failure to maintain the cell lineage boundary at the coronal suture is thought to be involved in the pathology of some forms of CS. The Ephrin family of receptor tyrosine kinases consists of membrane-bound receptors and ligands that control cell patterning and the formation of developmental boundaries. Mutations in the ephrin A4 (EFNA4) and ephrin B1 (EFNB1) ligands have been linked to nonsyndromic CS and craniofrontonasal syndrome, respectively, in patient samples. We have previously described a colony of rabbits with a heritable pattern of coronal suture synostosis, although the genetic basis for synostosis within this model remains unknown. The present study was performed to determine if EFNA4 or EFNB1 could be the loci of the causal mutation in this unique animal model. Sequencing of EFNA4 and EFNB1 was performed using templates obtained from wild-type (n = 4) and craniosynostotic (n = 4) rabbits. No structural coding errors were identified in either gene. A single-nucleotide transversion was identified in one wild-type rabbit within the third intron of EFNA4. These data indicate that the causal locus for heritable CS in this rabbit model is not located within the structural coding regions of either EFNA4 or EFNB1.