Solving large scale industrial production scheduling problems with complex constraints: an overview of the state-of-the-art

M Schlenkrich, SN Parragh - Procedia Computer Science, 2023 - Elsevier
Production scheduling is challenging and the body of literature addressing various variants
of the problem is large. It can roughly be divided into two streams: The first stream addresses …

A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

JP Huang, L Gao, XY Li, CJ Zhang - Computers & Industrial Engineering, 2023 - Elsevier
Distributed manufacturing can reduce the production cost through the cooperation among
factories, and it has been an important trend in the industrial field. For the enterprises with …

Deep reinforcement learning for dynamic distributed job shop scheduling problem with transfers

Y Lei, Q Deng, M Liao, S Gao - Expert Systems with Applications, 2024 - Elsevier
Dynamic events and transportation constraints would significantly affect the full utilization of
resources and the reduction of production costs in distributed job shops. Therefore, in this …

[HTML][HTML] Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement

T Hidayat, K Ramli, N Thereza, A Daulay, R Rushendra… - Informatics, 2024 - mdpi.com
Currently, utilizing virtualization technology in data centers often imposes an increasing
burden on the host machine (HM), leading to a decline in VM performance. To address this …

Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

HK Wang, CW Chou, CH Wang, LA Ho - International Journal of Production …, 2024 - Elsevier
TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The
Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end …

A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals

JP Huang, L Gao, XY Li - IEEE Transactions on Automation …, 2024 - ieeexplore.ieee.org
The Distributed Job-shop Scheduling Problem (DJSP) is a significant issue in both
academic and industrial fields. In real-world production, uncertain disturbances such as job …

Learning to schedule (L2S): Adaptive job shop scheduling using double deep Q network

AD Workneh, M Gmira - Smart Science, 2023 - Taylor & Francis
The stochasticity and randomly changing nature of the production environment posed a
significant challenge in develo** real-time responsive scheduling solutions. Many …

AI-Driven Optimization Approach Based on Genetic Algorithm in Mass Customization Supplying and Manufacturing.

S Alfayoumi, N Eltazi… - International Journal of …, 2023 - search.ebscohost.com
Numerous artificial intelligence (AI) techniques are currently utilized to identify planning
solutions for supply chains, which comprise suppliers, manufacturers, wholesalers, and …

Effect of backtracking strategy in population-based approach: the case of the set-union knapsack problem

I Dahmani, M Ferroum, M Hifi - Cybernetics and Systems, 2022 - Taylor & Francis
In this article, we study the effect of the backtracking strategy when injected into solutions
related to a population-based approach, especially when tackling the set-union knapsack …

Dynamic Job Shop Scheduling Problem with New Job Arrivals using Hybrid Genetic Algorithm

KB Ali, S Bechikh, A Louati, H Louati, E Kariri - IEEE Access, 2024 - ieeexplore.ieee.org
The paper tackles the dynamic job shop scheduling problem (DJSSP), aiming to schedule a
new set of jobs while minimizing the completion time of all operations. This paper proposes …