Deep reinforcement learning (DRL), as a promising technique, is a new approach to solve
the job shop scheduling problem (JSSP). Although DRL method is effective for solving …

Operations in areas of importance to society are frequently modeled as mixed-integer linear
programming (MILP) problems. While MILP problems suffer from combinatorial complexity …

Job-shop scheduling is an important but difficult combinatorial optimization problem for low-
volume and high-variety manufacturing, with solutions required to be obtained quickly at the …

Dynamic scheduling is one of the most important problems in the field of production
scheduling. Existing ways to solve the problem are mainly based on experienced workers or …

The crankshaft manufacturing process primarily comprises machining, single jacket, and
double jacket stages. These stages collectively produce substantial carbon emissions …

Due to the short life cycles of electronic products, trial run lots of new products are crucial in
IC packaging for production verification and engineering adjustments. The processing time …

The robot-task-sequencing planning problem is investigated in this paper, where multi-robot
tasks with resource conflicts and precedence constraints are involved making the problem …

Abstract Mixed-Integer Linear Programming (MILP) plays an important role across a range of
scientific disciplines and within areas of strategic importance to society. The MILP problems …

Motivated by the presence of uncertainties as well as combinatorial complexity within the
links of supply chains, this paper addresses the outstanding and timely challenge illustrated …

Recently, deep reinforcement learning (DRL) is shown to be promising in learning
dispatching rules end-to-end for complex scheduling problems. However, most research is …