Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation
This research examines the transformative potential of artificial intelligence (AI) in general
and Generative AI (GAI) in particular in supply chain and operations management (SCOM) …
and Generative AI (GAI) in particular in supply chain and operations management (SCOM) …
Optimisation of COVID-19 vaccination process using GIS, machine learning, and the multi-layered transportation model
COVID-19 has affected the lives and well-being of billions of citizens worldwide. While
nondrug interventions have been partially effective in containing the COVID-19 epidemic …
nondrug interventions have been partially effective in containing the COVID-19 epidemic …
Advancing interdisciplinary science for disrupting wildlife trafficking networks
Wildlife trafficking, whether local or transnational in scope, undermines sustainable
development efforts, degrades cultural resources, endangers species, erodes the local and …
development efforts, degrades cultural resources, endangers species, erodes the local and …
An artificial-immune-system-based algorithm enhanced with deep reinforcement learning for solving returnable transport item problems
This paper proposes a new approach, ie, virtual pooling, for optimising returnable transport
item (RTI) flows in a two-level closed-loop supply chain. The supply chain comprises a set of …
item (RTI) flows in a two-level closed-loop supply chain. The supply chain comprises a set of …
Online reinforcement learning-based inventory control for intelligent E-Fulfilment dealing with nonstationary demand
DY Mo, YP Tsang, Y Wang, W Xu - Enterprise Information Systems, 2024 - Taylor & Francis
In this study, an online reinforcement learning-based approach and a reinforcement learning
with prior knowledge approach are proposed to enhance decision intelligence in inventory …
with prior knowledge approach are proposed to enhance decision intelligence in inventory …
Analytics and machine learning in scheduling and routing research
1. Background This special issue largely originated from various discussions during several
cross-domain, multi-disciplinary conferences and workshops, especially the 9th …
cross-domain, multi-disciplinary conferences and workshops, especially the 9th …
Deep learning based high accuracy heuristic approach for knapsack interdiction problem
S Kwon, H Choi, S Park - Computers & Operations Research, 2024 - Elsevier
Interdiction problems are a subfamily of bilevel optimization problems, characterized by a
hierarchical structure involving two agents: a leader and a follower. In these problems, the …
hierarchical structure involving two agents: a leader and a follower. In these problems, the …
Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard
The increasing vessel size and automation level have shifted the productivity bottleneck of
automated container terminals from the terminal side to the yard side. Operating an …
automated container terminals from the terminal side to the yard side. Operating an …
Two-stage nodal network interdiction under decision-dependent uncertainty
A Ahmadi Digehsara, A Ardestani-Jaafari… - Annals of Operations …, 2024 - Springer
Infrastructures such as power stations, water systems, railways, highways, subway stations,
and roads play an important role in ensuring that the network operates safely and effectively …
and roads play an important role in ensuring that the network operates safely and effectively …
Network Shortest Path Interdiction Problem Based on Generalized Set Coverage
In the network shortest path interdiction problem, an evader attempts to find the shortest path
between the origin and the destination in a network, while an interdictor attempts to …
between the origin and the destination in a network, while an interdictor attempts to …