A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping

by   Deepak Vivekanandan, et al.

The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy but also for increasing the overall efficiency. Among the different job scheduling problems, the JSSP is addressed in this work. JSSP falls into the category of NP-hard COP, in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as FIFO, LPT and metaheuristics such as Taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using DRL to solve COP has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the PPO algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated an OSM in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.


page 1

page 2

page 3

page 4


Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems

Scheduling plays an important role in automated production. Its impact c...

Improving Generalization of Deep Reinforcement Learning-based TSP Solvers

Recent work applying deep reinforcement learning (DRL) to solve travelin...

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybr...

Scheduling a single machine with compressible jobs to minimize maximum lateness

The problem of scheduling non-simultaneously released jobs with due date...

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

Due to new government legislation, customers' environmental concerns and...

Monte-Carlo Tree-Search for Leveraging Performance of Blackbox Job-Shop Scheduling Heuristics

In manufacturing, the production is often done on out-of-the-shelf manuf...

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

The permutation flow shop scheduling (PFSS), aiming at finding the optim...

Please sign up or login with your details

Forgot password? Click here to reset