10 research outputs found
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling
The following interdisciplinary article presents a memetic algorithm with
applying deep reinforcement learning (DRL) for solving practically oriented
dual resource constrained flexible job shop scheduling problems (DRC-FJSSP).
From research projects in industry, we recognize the need to consider flexible
machines, flexible human workers, worker capabilities, setup and processing
operations, material arrival times, complex job paths with parallel tasks for
bill of material (BOM) manufacturing, sequence-dependent setup times and
(partially) automated tasks in human-machine-collaboration. In recent years,
there has been extensive research on metaheuristics and DRL techniques but
focused on simple scheduling environments. However, there are few approaches
combining metaheuristics and DRL to generate schedules more reliably and
efficiently. In this paper, we first formulate a DRC-FJSSP to map complex
industry requirements beyond traditional job shop models. Then we propose a
scheduling framework integrating a discrete event simulation (DES) for schedule
evaluation, considering parallel computing and multicriteria optimization.
Here, a memetic algorithm is enriched with DRL to improve sequencing and
assignment decisions. Through numerical experiments with real-world production
data, we confirm that the framework generates feasible schedules efficiently
and reliably for a balanced optimization of makespan (MS) and total tardiness
(TT). Utilizing DRL instead of random metaheuristic operations leads to better
results in fewer algorithm iterations and outperforms traditional approaches in
such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202
Robust-Stable Scheduling in Dynamic Flow Shops based on Deep Reinforcement Learning
Source code and experiment result
Robustness Prediction in Dynamic Production Processes - A new Surrogate Measure based on Regression Machine Learning
Integrated genomic characterization of adrenocortical carcinoma.
International audienceAdrenocortical carcinomas (ACCs) are aggressive cancers originating in the cortex of the adrenal gland. Despite overall poor prognosis, ACC outcome is heterogeneous. We performed exome sequencing and SNP array analysis of 45 ACCs and identified recurrent alterations in known driver genes (CTNNB1, TP53, CDKN2A, RB1 and MEN1) and in genes not previously reported in ACC (ZNRF3, DAXX, TERT and MED12), which we validated in an independent cohort of 77 ACCs. ZNRF3, encoding a cell surface E3 ubiquitin ligase, was the most frequently altered gene (21%) and is a potential new tumor suppressor gene related to the β-catenin pathway. Our integrated genomic analyses further identified two distinct molecular subgroups with opposite outcome. The C1A group of ACCs with poor outcome displayed numerous mutations and DNA methylation alterations, whereas the C1B group of ACCs with good prognosis displayed specific deregulation of two microRNA clusters. Thus, aggressive and indolent ACCs correspond to two distinct molecular entities driven by different oncogenic alterations