The cyclic job-shop scheduling problem: The new subclass of the job-shop problem and applying the Simulated annealing to solve it

TL;DR

This paper introduces a new subclass of job-shop scheduling called cyclic job-shop scheduling of order k, and demonstrates that planning entire cycles with Simulated Annealing improves efficiency over single-iteration planning.

Contribution

The paper defines the cyclic job-shop scheduling problem of order k and applies Simulated Annealing to effectively solve it, showing significant efficiency improvements.

Findings

Planning entire cycles is more effective than single-iteration planning.

Simulated Annealing significantly improves cyclic scheduling efficiency.

Experimental results confirm the approach's effectiveness.

Abstract

In the paper, the new approach to the scheduling problem are described. The approach deals with the problem of planning the cyclic production and proposes to consider such scheduling problem as the cyclic job-shop problem of the order k, where k is the number of reiterations. It was found out that planning of only one iteration of the loop is less effective than planning of the entire cycle. To the experimental research, a number of test instances of the job-shop scheduling problem by Operation Research Library were used. The Simulated Annealing was applied to solve the instances. The experiments proved that the approach proposed allows increasing the efficiency of cyclic scheduling significantly.