Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

M Frutos,M Méndez,F Tohmé,D Broz

doi:10.1155/2013/418396

Abstract

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

Highlights

Production planning problems involve the allocation of scarce resources to different tasks in such way as to optimize one or more efficiency-related goals [1]
Hypervolume and R2, we found that Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Evolutionary Algorithm (IBEA) are the most efficient for the tasks at hand
This paper presents an analysis of the performance of three different Multiobjective Evolutionary Algorithms in experiments with Job-Shop Scheduling Problems

Summary

Introduction

Production planning problems involve the allocation of scarce resources to different tasks in such way as to optimize one or more efficiency-related goals [1] In most cases, these problems are analyzed as instances of the Job-Shop Scheduling Problem, in which given a set of machines and a list of jobs, represented as ordered sequences of operations, to be run on the machines, the goal is to minimize, in particular, the processing time of all jobs, known as makespan [2]. These competing approaches all use domination and elitism to reach the best possible approximation to the solutions of multiobjective problems They differ in the strategies on which they are based (lack of a predefined sharing parameter, in the case of NSGAII; a fine grained fitness assignment procedure in SPEA2; a qualitative indicator function over Pareto approximations in IBEA), but their performance on different instances tends to be the best available in the literature. We run the algorithms on real-world problems in which nonstandardized production (like in the Job-Shop context) has to share machines with standardized processes that have priority over the former

Job-Shop Scheduling Problem

Evolutionary Multiobjective Algorithms

Implementation and Design of Experiments

Results

Conclusions