An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

The warehousing industry is faced with increasing customer demands and growing global
competition. A major factor in the efficient operation of warehouses is the strategic storage …

In this work, we propose a Self-Supervised training strategy specifically designed for
combinatorial problems. One of the main obstacles in applying supervised paradigms to …

Research on deep reinforcement learning (DRL) based production scheduling (PS) has
gained a lot of attention in recent years, primarily due to the high demand for optimizing …

Existing learning-based methods for solving job shop scheduling problem (JSSP) usually
use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and …

Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority
dispatching rule, may yield satisfactory results for several problem instances but …

Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …

Learned construction heuristics for scheduling problems have become increasingly
competitive with established solvers and heuristics in recent years. In particular, significant …

Job shop scheduling problems (JSSPs) have been the subject of intense studies for
decades because they are often at the core of significant industrial planning challenges and …

In the landscape of modern industries, scheduling problems are pivotal as they influence an
organization's competitiveness, operational costs, and capacity to meet demands. This work …