Deep Reinforcement Learning for a Multi-Objective Online Order Batching Problem

On-time delivery and low service costs are two important performance metrics in warehousing operations. This paper proposes a Deep Reinforcement Learning (DRL) based approach to solve the online Order Batching and Sequence Problem (OBSP) to optimize these two objectives. To learn how to balance the trade-off between two objectives, we introduce a Bayesian optimization framework to shape the reward function of the DRL agent, such that the influences of learning to these objectives are adjusted to different environments. We compare our approach with several heuristics using problem instances of real-world size where thousands of orders arrive dynamically per hour. We show the Proximal Policy Optimization (PPO) algorithm with Bayesian optimization outperforms the heuristics in all tested scenarios on both objectives. In addition, it finds different weights for the components in the reward function in different scenarios, indicating its capability of learning how to set the importance of two objectives under different environments. We also provide policy analysis on the learned DRL agent, where a decision tree is used to infer decision rules to enable the interpretability of the DRL approach

Deep Reinforcement Learning for a Multi-Objective Online Order Batching Problem

On-time delivery and low service costs are two important performance metrics in warehousing operations. This paper proposes a Deep Reinforcement Learning (DRL) based approach to solve the online Order Batching and Sequence Problem (OBSP) to optimize these two objectives. To learn how to balance the trade-off between two objectives, we introduce a Bayesian optimization framework to shape the reward function of the DRL agent, such that the influences of learning to these objectives are adjusted to different environments. We compare our approach with several heuristics using problem instances of real-world size where thousands of orders arrive dynamically per hour. We show the Proximal Policy Optimization (PPO) algorithm with Bayesian optimization outperforms the heuristics in all tested scenarios on both objectives. In addition, it finds different weights for the components in the reward function in different scenarios, indicating its capability of learning how to set the importance of two objectives under different environments. We also provide policy analysis on the learned DRL agent, where a decision tree is used to infer decision rules to enable the interpretability of the DRL approach

The genetics of familial combined hyperlipidaemia.

Item does not contain fulltextAlmost 40 years after the first description of familial combined hyperlipidaemia (FCHL) as a discrete entity, the genetic and metabolic basis of this prevalent disease has yet to be fully unveiled. In general, two strategies have been applied to elucidate its complex genetic background, the candidate-gene and the linkage approach, which have yielded an extensive list of genes associated with FCHL or its related traits, with a variable degree of scientific evidence. Some genes influence the FCHL phenotype in many pedigrees, whereas others are responsible for the affected state in only one kindred, thereby adding to the genetic and phenotypic heterogeneity of FCHL. This Review outlines the individual genes that have been described in FCHL and how these genes can be incorporated into the current concept of metabolic pathways resulting in FCHL: adipose tissue dysfunction, hepatic fat accumulation and overproduction, disturbed metabolism and delayed clearance of apolipoprotein-B-containing particles. Genes that affect metabolism and clearance of plasma lipoprotein particles have been most thoroughly studied. The adoption of new traits, in addition to the classic plasma lipid traits, could aid in the identification of new genes implicated in other pathways in FCHL. Moreover, systems genetic analysis, which integrates genetic polymorphisms with data on gene expression levels, lipidomics or metabolomics, will attribute functions to genetic variants in addition to revealing new genes.1 juni 201