A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

Abstract

Identifying the best Dispatching Rule in order to minimize makespan in a Job Shop Scheduling Problem is a complex task, since no Dispatching Rule is better than all others in different scenarios, making the selection of a most effective rule which is time-consuming and costly. In this paper, a novel approach combining Data Mining, Simulation, and Dispatching Rules is proposed. The aim is to assign in real-time a set of Dispatching Rules to the machines on the shop floor while minimizing makespan. Experiments show that the suggested approach is effective and reduces the makespan within a range of 1–44%. Furthermore, this approach also reduces the required computation time by using Data Mining to determine and assign the best Dispatching Rules to machines.