Iterated greedy algorithms for flow-shop scheduling problems: A tutorial
ZY Zhao, MC Zhou, SX Liu - IEEE Transactions on Automation …, 2021 - ieeexplore.ieee.org
An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely
used to solve flow-shop scheduling problems (FSPs), an important branch of production …
used to solve flow-shop scheduling problems (FSPs), an important branch of production …
Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
Scheduling under uncertainty for Industry 4.0 and 5.0
This article provides a review about how uncertainties in increasingly complex production
and supply chains should be addressed in scheduling tasks. Uncertainty management will …
and supply chains should be addressed in scheduling tasks. Uncertainty management will …
Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns
The utilization of real-time information in production scheduling decisions becomes possible
with the help of new developments in Information Technology and Industrial Informatics …
with the help of new developments in Information Technology and Industrial Informatics …
Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance
The introduction of modern technologies in manufacturing is contributing to the emergence
of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of …
of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of …
A data-driven simulation-optimization framework for generating priority dispatching rules in dynamic job shop scheduling with uncertainties
H Wang, T Peng, A Nassehi, R Tang - Journal of Manufacturing Systems, 2023 - Elsevier
Modeling and optimizing dynamic job shop scheduling problems (DJSSP) without ample
assumptions is inherently challenging due to the increasing complexity and uncertainty …
assumptions is inherently challenging due to the increasing complexity and uncertainty …
[HTML][HTML] A distributed permutation flow-shop considering sustainability criteria and real-time scheduling
Recent advancements in production scheduling have arisen in response to the need for
adaptation in dynamic environments. This paper addresses the challenge of real-time …
adaptation in dynamic environments. This paper addresses the challenge of real-time …
A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem
Manufacturing industry is experiencing a revolution in the creation and utilization of data, the
abundance of industrial data creates a need for data-driven techniques to implement real …
abundance of industrial data creates a need for data-driven techniques to implement real …
The online integrated order picking and delivery considering Pickers' learning effects for an O2O community supermarket
J Zhang, F Liu, J Tang, Y Li - Transportation Research Part E: Logistics and …, 2019 - Elsevier
The online-to-offline (O2O) community supermarket is currently a popular O2O business
model in China. Owing to the small lot-size, high frequency, time-sensitive, and dynamic …
model in China. Owing to the small lot-size, high frequency, time-sensitive, and dynamic …
A multi-start variable neighbourhood descent algorithm for hybrid flowshop rescheduling
Hybrid flowshop (HFS) rescheduling has important applications in modern industry. Much of
the existing research on HFS rescheduling only consider one type of dynamic event …
the existing research on HFS rescheduling only consider one type of dynamic event …